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CURRICULUM VITA PERSONAL INFORMATION Name: Tony S. Wirjanto. Affiliation:

(1) School of Accounting & Finance (SAF), Faculty of Arts, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1.

(2) Department of Statistics & Actuarial Science (SAS), Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1. Position:

(1) Full Professor at SAF (2) Full Professor at SAS (3) University Research Chair (2009-2015)

Telephone Number: (519) 888-4567 ext. 35210. Fax Numbers: (SAF): (519) 888-7562. (SAS): (519) 746-1875. E-mail: (SAF): twirjant@uwaterloo.ca (SAS): twirjant@math.uwaterloo.ca Official Home Pages: (SAF): https://artsonline.uwaterloo.ca/safprofile/view_profile.php?id=45 (SAS): http://math.uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

Citizenship: Canadian. Marital Status: Married With One Child. Languages: English, German. EDUCATION Ph.D., Queen's University, Canada, 1993. Fields of Specialization: Econometrics. M.A., Queen's University, Canada, 1987. Field of Specialization: Econometrics. B.A. (Honours), University of Toronto, Canada, 1982. Fields of Specialization: Economics and Statistics. EMPLOYMENT HISTORY 2009-Present: Full Professor, School of Accounting & Finance (Faculty of Arts), University of Waterloo, Ontario Canada. 2009-Present: Full Professor, Department of Statistics & Actuarial Science (Faculty of Mathematics), University of Waterloo, Ontario Canada. 2004-2009: Full Professor, Department of Economics, University of Waterloo, Waterloo,

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Ontario, Canada. 1996-2004: Associate Professor, Department of Economics, University of Waterloo, Waterloo, Ontario, Canada. 1991-1996: Assistant Professor, Department of Economics, University of Waterloo, Waterloo, Ontario, Canada. OTHER ACADEMIC APPOINTMENTS 2011-Present: Associate Director of Research & PhD Program, School of Accounting & Finance, Faculty of Arts, University of Waterloo, Ontario, Canada. 2009-Present: University Research Chair. University of Waterloo, Ontario, Canada. 2009-Present: Associate Director of Waterloo Research Institute in Insurance, Securities and Quantitative finance (WatRISQ), University of Waterloo, Ontario, Canada. 2008-2009: Associate Director of Institute for Quantitative Finance and Insurance (IQFI), University of Waterloo, Ontario, Canada. 2008-Present: Senior Fellow at the Rimini Centre for Economic Analysis (RCEA), Rimini, Italy. 2008-Present: Member of the Office of the Superintendent of Financial Institutions Canada (OSFI) Risk Analytics Working Group. 2005-Present: Senior Guest Professor, Zhejiang University, School of Economics, Department of Finance, Hangzhou, China. EDITORIAL BOARDS 2006-2013: Associate Editor for Empirical Economics Journal Website: http://www.springer.com/economics/journal/181 2012-Present: Associate Editor-in-Chief for Journal of Mathematical Finance Journal Website: http://www.scirp.org/journal/jmf/ 2013-Present: Associate Editor for Econometrics Journal Website: http://www.mdpi.com/journal/econometrics?utm_source=mdpi_logo_link&utm_medium=email&utm_campaign=newyear2012_journal 2013-Present: Editorial Board, Mathematical Finance Letters. 2013-Present: Associate Editor, International Journal of Finance and Accounting Studies. OTHER AND VISITING APPOINTMENTS 2005: Visiting Full Professor, Department of Economics, Faculty of Social Science, University of Western Ontario, London, Ontario, Canada. 2005-Present: Senior Guest Professor, Zhejiang University, School of Economics, Department of Finance, Hangzhou, China. 2003-2009: Cross-appointment with School of Accountancy, University of Waterloo, Waterloo, Ontario, Canada.

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2000-2009: Cross-appointment with Department of Statistics and Actuarial and Science, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada. 1999-2003: Associated Graduate Faculty, University of Guelph, Guelph, Ontario, Canada. 1998-1999: Visiting Associate Professor, Department of Economics, McMaster University, Hamilton, Ontario, Canada. 1998-1999: Visiting Associate Professor, Department of Economics, University of Guelph, Guelph, Ontario, Canada. PROFESSIONAL MEMBERSHIP 1991-Present: Member of the Econometric Society. 1991-Present: Member of the American Statistical Association. 2006-Present: Member of the Society for Financial Econometrics. 1991-Present: member of the Canadian Econometrics Study Group (CESG). 2001-2004: Member of the Executive Council of Canadian Economics Association (CEA). 1997-2000: Member of the Editorial Board of Canadian Journal of Economics (CJE). 1994-Present: Member of the Centre for Advanced Studies in Finance (CASF), University of Waterloo. 2005-2009: Member of the Institute of Insurance and Pension Research (IIPR), University of Waterloo. 2010-Present: Member of the European Finance Association (EFA). 2010-Present: Member of the Northern Finance Association (NFA). 2011-Present: Member of the Midwest Finance Association (MFA). 2013-Present: Member of the Statistical Society of Canada (SSC). 2013-Present: Member of the American Finance Association (AFA). RESEARCH ACTIVITIES I am a trained econometrician working in finance and finance related area. My profession is known as financial econometrics. Broadly speaking, financial econometrics involves the study of quantitative problems arising from finance. It uses statistical techniques and finance theory to address a variety of problems from finance. These include building financial models, estimation and inferences of financial models, volatility estimation, risk management, testing financial economics theory, capital asset pricing, derivative pricing, portfolio allocation, risk-adjusted returns, simulating financial systems, hedging strategies, among others. Selected Completed Working Papers in 2013 A. Financial Econometrics 1. Men, Z., T. S. Wirjanto, and A. W. Kolkiewicz (2013). Bayesian Inference of Multiscale Stochastic Conditional Duration Models. 2. Men Z., A. W. Kolkiewicz, and T. S. Wirjanto (2013). A New Variant of Threshold Stochastic Conditional Duration Model for Transaction Data.

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3. Men, Z., D. McLeish, A. W. Kolkiewicz, and Tony S. Wirjanto (2013). Comparison of Asymmetric Stochastic Volatility Models under Different Correlation Structures. 4. Men, Z., A. W. Kolkiewicz, and Tony S. Wirjanto (2013).Bayesian Analysis of Asymmetric Stochastic Conditional Duration Model.. 5. Men, Z., T. S. Wirjanto, and, A. W. Kolkiewicz (2013). A Threshold Stochastic Conditional Duration Model for Financial Transaction Data. 6. Men, Z., A. W. Kolkiewicz and T. S. Wirjanto (2013). Bayesian Analysis of Threshold Stochastic Volatility Models. 7. Wirjanto, T. S., and Z. Men (2013). Toward a Bayesian Inference of Time-Deformation Models: Some Simulation Studies. Submitted. 8. Wirjanto, T. S., Z. Men, and A. W. Kolkiewicz (2013). Stochastic Conditional Duration Models with Mixture Processes . Submitted. 9. Men, Z., A. W. Kolkiewicz and T. S. Wirjanto (2013). Bayesian Inference of Asymmetric Stochastic Conditional Duration Models. Submitted. 10. Ning, C., D. Xu, and T. S. Wirjanto (2013). Modeling Asymmetric Volatility Clusters using Copulas and High Frequency Data. Under revision. 11. Xu, D. and T. S. Wirjanto (2013). Risk Measures under Stochastic Volatility Model with Mixture-of-Normal Error Distributions. 13. Xu, D., J. Knight and T. S. Wirjanto (2013). Stochastic Conditional Duration Model with Mixture-of-Normal Error Distributions: Theoretical and Monte-Carlo Results. 14. Xu, D. and T. S. Wirjanto (2013). A Tractable Computation of Portfolio VaRs with GARCH Models Using Independent Component Analysis. 15. Xu, D. and T. S. Wirjanto (2013). A Mixture-of-Normal Distribution Modeling Approach in Financial Econometrics: A Selected Review. B. Mathematical Finance 16. Cheng, Y-H. and T. S. Wirjanto (2013). Pricing Financial Derivatives by Gram-Charlier Expansions. 17. Tao, F. and T. S. Wirjanto (2013). Discrete-Time Portfolio Optimization with Transaction Costs. 18. Choi, Y, and T. S. Wirjanto (2013) A Simple Model of the Nominal Term Structure of Interest Rates. 19. Redekop, J. and T. S. Wirjanto (2013). Exploring a Two-State Markov-Switching Model for Option Pricing. C. Computational Finance 20. Memartoluie, A., D. Saunders and T. S. Wirjanto (2013). Worst-Case Copulas, Mass Transportation and Wrong-Way Risk in Counterparty Credit Risk Management. Submitted. 21. Lozynskyy, V. and T. S. Wirjanto (2013). Pricing and Hedging Arithmetic Average Asian Options. 22. Sheng, S. and T. S. Wirjanto (2013). Applications of the Vanna-Volga Method in FX Markets. D. Finance and Financial Economics 23. Zhang, Min (July), Adam W. Kolkiewicz, Tony S. Wirjanto and Xindan Li (2013). The Impacts of Financial Crisis on Sovereign Credit Risk in Asia and Europe.

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24. Wang, T. and T. S. Wirjanto (2013). Uncertainty, Unemployment Insurance, Individuals Optimal Stopping Time and Duration of Unemployment. E. IT and Financial Accounting 25. Lim, J. H., T. C. Stratopoulos and T. S. Wirjanto (2013). IT Reputation Building and Market Valuation. 26. Huang, A., S. P. Bandyopadhyay, K. Sun, and T. S. Wirjanto (2013). The Accrual Volatility Anomaly. 27. Bandyopadhyay, S. P., A. G. Huang and T. S. Wirjanto (2013). The Return Premiums to Accrual Quality. 28. Huang, A. G., S. P. Bandyopadhyay and T. S. Wirjanto (2012). Does Income Smoothing Create Value? Work Published in Refereed, Scholarly Journals 1. Lim, J-H., T. C. Stratopoulos and T. S. Wirjanto (2012). Sustainability of a Firms Reputation for IT Capability: Role of Senior IT Executives. Forthcoming in the Journal of Management Information. 2. Feng, D., P. X. K. Song, T. S. Wirjanto (2012). Time-Deformation Modeling of Stock Returns Directed by Duration Processes. Forthcoming in Econometric Reviews. 3. Bae, K. H. A. Ozoguz, H. P. Tan and T. S. Wirjanto (2012). Do Foreigners Facilitate Information Transmission in Emerging Markets? Journal of Financial Economics, 105, 209227. 4. Lim, J. H., T. C. Stratopoulos and T. S. Wirjanto (2012). Path Dependence of Dynamic Information Technology Capability: An Empirical Investigation, Journal of Management Information Systems, 28 (3): 45-84. 5. Packalen, M. and T. S. Wirjanto (2012). Inference About Clustering and Parametric Assumptions in Covariance Matrix Estimation, Computational Statistics and Data Analysis, 56, 114 . 6. Lim, J. H., T. C. Stratopoulos and T. S. Wirjanto (2012). Role of IT Executives on the Firm's Ability to Achieve Competitive Advantage through IT Capability, International Journal of Accounting Information Systems, 13, 2140. 7. Wirjanto, T. S. and A. G. Huang (2012). Is Chinas P/E Ratio Too Low? Examining the Role of Earnings Volatility, Pacific-Basin Finance Journal, 20, 4161. 8. Huang, A. G., Y. Tian, and T. S. Wirjanto (2012). Re-examining Accounting Conservatism: The Importance of Adjusting for Firm Heterogeneity, Advances in Quantitative Analysis of Finance and Accounting, 10, 193-223. 9. Xu, D., J. Knight and T. S. Wirjanto (2011). Asymmetric Stochastic Conditional Duration Model: A Mixture-of-Normal Approach, Journal of Financial Econometrics, 9(3), 469488. 10. Xu, D. and T. S. Wirjanto (2010). An Empirical Characteristic Function Approach to VaR under a Mixture of Normal Distributions with Time-Varying Volatility, Journal of Derivatives, 18, 1, 39-58. 11. Sen, A. and T. S. Wirjanto (2010). Estimating the Impacts of Taxes on the Initiation and Persistence of Youth Smoking: Empirical Evidence from Ontario, Canada, Health Economics, 12, 12641280.

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13. Insley, M. C. and T. S. Wirjanto (2010), Contrasting Two Approaches in Real Options Valuation: Contingent Claims versus Dynamic Programming. Journal of Forest Economics, 16(2), 157-176. 14. DeJuan, J. P., J. J. Seater, and T. S. Wirjanto (2010). Testing the Stochastic Implications of Permanent Income Hypothesis Using Canadian Provincial Data, Oxford Bulletin of Economics and Statistics, 72 (1), 89-108. 15. Zhang, F., Y. Y. Tian and T. S. Wirjanto (2009). Empirical Tests of the Float Adjusted Return Model. Finance Research Letters 6, 219-229. 16. Ning, C. and T. S. Wirjanto (2009). Extreme Return-Volume Dependence in East-Asian Stock Markets: A Copula Approach. Finance Research Letters 6, 202-209. 17. Qian, Y. Y. Tian and T. S. Wirjanto (2009). Do Publicly Listed Chinese Companies Adjust their Capital Structure toward a Target Level? China Economic Review 20, 662676. 18. Ning, C., Xu, D. and T. S. Wirjanto (2008). Modelling the Leverage Effect with Copulas and Realized Volatility. Finance Research Letters 5, 221-227. 19. Shamim, A. and T. S.Wirjanto (2008). The Impact of Sales Taxation on Internet Commerce -- An empirical Analysis. Economics Letters 99, 557-560. 20. Choi, Y. and T. S. Wirjanto (2007). An Analytic Approximation Formula for Pricing Zero-Coupon Bonds. Finance Research Letters 4, 116-126. 21. Joseph P. DeJuan, J. Seater and T. S. Wirjanto (2006). Testing the Permanent-Income Hypothesis: New Evidence from West-German States. Empirical Economics 31, 613-629. 22. Yousefi, A. and T. S. Wirjanto (2005). A Stylized Model of Crude Oil Price Formation. OPEC Review 29, 177-192. 23. DeJuan, J. P., J. J. Seater and T. S. Wirjanto (2004). A Direct Test of the Permanent Income Hypothesis with an Application to the U.S. States. Journal of Credit, Money and Banking 36, 1091-1103. 24. Wirjanto, T. S. (2004). Exploring Consumption-Based Asset Pricing Model with Stochastic-Trend Forcing Processes. Applied Economics 36, 1591-1597. 25. Wang T. and T. S. Wirjanto (2004). The Role of Risk and Risk Aversion in an Individuals Migration Decision. Stochastic Models 20, 129-147. 26. Yousefi, A. and T. S. Wirjanto (2004). The Empirical Role of the Exchange Rate on the Crude-Oil Price Formation. Energy Economics 26, 783-799. 27. Yousefi, A. and T. S. Wirjanto (2003). Exchange Rate of the U.S. Dollar and the J Curve: The Case of Oil Exporting Countries. Energy Economics 25, 741-765. 28. Amano, R. A. and T. S. Wirjanto. (2000). On the Stability of Long-Run M2 Demand in Japan. Japanese Economic Review 51, 536-543. 29. Ghosh, S. K. and T. S. Wirjanto (2000). Risk Function of Zellners Extended MELO Estimators and Some Monte Carlo Results. Journal of Quantitative Economics 16, 1-18. 30. Reilly, K. T. and T. S. Wirjanto (1999). Does More Mean Less? The Male/Female Wage Gap and the Proportion of Females at the Establishment Level. Canadian Journal of Economics 32, 906-929. 31. Reilly, K. T. and T. S. Wirjanto (1999).The Proportion of Females at the Establishment Levels: Discrimination, Preferences and Technology. Canadian Public Policy 25, 73-94. 32. Wirjanto, T. S. (1999). Empirical Indicators of the Currency Crises in East-Asian Countries.

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Pacific Economic Review 4, 165-183. 33. Amano, R. A. and T. S. Wirjanto (1998). Government Expenditures and the Permanent-Income Model. Review of Economic Dynamics 1, 719-730. 34. Amano, R. A. and T. S. Wirjanto (1998). Re-examining Variance-Bounds Tests for Asset Prices. Review of Quantitative Finance and Accounting 10, 155-172. 35. Lee, T. Y. and T. S. Wirjanto (1998). On the Efficiency of Conditional Heteroskedasticity Models. Review of Quantitative Finance and Accounting 10, 21-37. 36. Amano, R. A. and T. S. Wirjanto (1997). An Empirical Study of Dynamic Labour Demand With Integrated Forcing Processes. Journal of Macroeconomics 19, 697-716. 37. Amano, R. A. and T. S. Wirjanto (1997). Adjustment Costs and Import Demand Behaviour: Evidence from Canada and the United States. Journal of International Money and Finance 16, 461-476. 38. Amano, R. A. and T. S. Wirjanto (1997). Intratemporal Substitution and Government Spending. Review of Economics and Statistics 79, 605-609. 39. Wirjanto, T. S. (1996). Empirical Investigation into Permanent-Income Hypothesis: Further Evidence from the Canadian Data. Applied Economics 28, 1451-1461. 40. Wirjanto, T. S. (1997). Aggregate Consumption Behaviour With Time Nonseparable Preferences and Liquidity Constraints. Applied Financial Economics 7, 107-114. 41. Wirjanto, T. S. (1997). Optimal Estimating Functions, Quasi-Likelihood and Statistical Modelling. Comment. Journal of Statistical Planning and Inference 60, 77-121. 42. Amano, R. A. and T. S. Wirjanto (1996). Intertemporal Substitution, Imports and the Permanent-Income Model. Journal of International Economics 40(3,4), 439-457. 43. Amano, R. A. and T. S. Wirjanto (1996). Nonstationary Regression Models with a Lagged Dependent Variable. Communications in Statistics: Theory and Methods 25, 1489-1503.. 44. Wirjanto, T. S. (1996). The Limiting Distributions of Unit-Root Tests for Data with Cross-Sectional and Time-Series Dimensions. Statistics and Probability Letters 30, 73-77. 45. Amano, R. A. and T. S. Wirjanto (1996). Target Stock and Money Supply: A Closer Examination of the Data. Journal of Applied Econometrics 11, 94-103. 46. Wirjanto, T. S. (1995). Aggregate Consumption Behaviour and Liquidity Constraints: The Canadian Evidence. Canadian Journal of Economics 28, 1135-1152. 47. Gregory, A. W. and T. S. Wirjanto (1993). The Effect of Sampling Error on the Time Series Behavior of Consumption Data. Discussion. Journal of Econometrics 55, 267-273. 48. Wirjanto, T. S. (1991). Testing the Permanent-Income Hypothesis: The Evidence from Canadian Data. Canadian Journal of Economics 24, 563-577. 49. Otto, G. D. and T. S. Wirjanto (1990). Seasonal Unit-Root Tests on Canadian Macroeconomic Time Series. Economics Letters 34, 117-120. Work Published as Chapters in Books and Conference Proceedings (Refereed) 1. Wirjanto, T. S. (2000). Comment on K.McPhails Broad Money: a Guide for Monetary Policy, in Money, Monetary Policy, and Transmission Mechanism, Proceedings of a Conference held by the Bank of Canada in November 1999, Bank of Canada, 98-100. 2. Wirjanto, T. S. (1997). Estimating Functions and Over-identified Models, in: Selected

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Proceedings of the Symposium on Estimating Equations, edited by I.V. Basawa, V. P. Godambe, and R. L. Taylor, IMS Lectures Notes - Monograph Series 32, 239-256. 3. Wilton, D. and T. S. Wirjanto (1995). Unemployment and Unemployment Insurance, in: Aspects of Labour Market Behaviour: Essays in Honour of John VanDerkamp, edited by L. N. Christofides, E. K. Grant and R Swidinsky, University of Toronto Press, 107-125. 4. Gregory, A. W., G. W. Smith and T. S. Wirjanto (1992). Synthesis of Money-Demand and Indicator Models, in: The Bank of Canada Monetary Seminar May 7-9 Proceedings 1990, edited by D. Longworth, F. Caramazza, and B. Montador, Bank of Canada, 465-509. Work Published Elsewhere 1. Xu, D. and T. S. Wirjanto (2008). Value at Risk for Stochastic Volatility Model under Bivariate Mixtures of Normal Distribution - Part I: Univariate Modeling, TD-UW Computational Finance Research Partnership Project. 2. Hendry, S., M. Gosselin and T. S. Wirjanto (2000). Money Supply and Demand in VECM, Bank of Canada, Ottawa. 3. Wirjanto, T. S. (1999). Estimation of Import and Export Elasticities, Economic Studies and Policy Analysis Division, Department of Finance, Ottawa. 4. Wilton, D. and T. S. Wirjanto (1999). An Analysis of the Seasonal Variation in the National Tourism Indicators, Canadian Tourism Commission, Research Report No. 1999-1, Ottawa. 5. Wirjanto, T. S. (1997). World Trade Patterns and Contemporary Issues in International Trade Policy, International and Development Studies, John Deutsch Institute for the Study of Economic Policy, Queen's University, Kingston. Unpublished Monographs 1. Wirjanto, T. S. (2004). Inference in Continuous-time Finance Models. University of Waterloo. 2. Wirjanto, T. S. (2004). Estimation of Continuous-time Finance Models. University of Waterloo. 3. Wirjanto, T. S. (2004). Empirical-likelihood Approach to Testing Continuous-Time Finance Models. University of Waterloo. 4. Wirjanto, T. S. (2002). Generalized Method of Moments, University of Waterloo. 5. Wirjanto, T. S. (2002). Deterministic Volatility Processes, University of Waterloo. 6. Wirjanto, T. S. (2002). Stochastic Volatility Processes, University of Waterloo. 7. Wirjanto, T. S. (1991). Introduction to Estimation and Hypothesis Testing Using Generalized Methods of Moment, University of Waterloo. 8. Wirjanto, T. S. (1991). Introduction to Econometrics of Integrated Time Series, University of Waterloo. 9. Wirjanto, T. S. (1991). Introduction to Vector Autoregressive (VAR) Modelling Approach, University of Waterloo. 10. Otto, G. D. and T. S. Wirjanto (1990). Modelling Seasonality: Deterministic, Stochastic, or Integrated?, Queen's University.

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11. Otto, G. D. and T. S. Wirjanto (1990). Seasonal Cycles: The Canadian Evidence, Queen's University. 12. Wirjanto, T. S. (1990). Seasonal Cointegration and Error Correction Model: Canadian Consumption, Queen's University. 13. Wirjanto, T. S. (1990). Heteroskedasticity and Autocorrelation-Robust Tests in Regression Directions, Queen's University. 14. Wirjanto, T. S. (1989). An Excursion into the Land of Generalized Method of Moments, Queen's University. Presentation of Research Work At Conferences 2012 1. Lim, J.H., Stratopoulos, T., and Wirjanto, T. The Impact of Senior IT Executives on IT Capability. (Americas Conference on Information Systems (AMCIS), Seattle, August 9-11, 2012). 2. Lim, J.H., Stratopoulos, T., and Wirjanto, T. The Impact of Senior IT Executives on IT Capability. (The 2012 Annual Meeting at American Accounting Association, Washington D.C., August 4-8, 2012). 3. Lim, J.H., Stratopoulos, T., and Wirjanto, T. Reciprocity between Senior IT Executives and IT Capable Firms: A Source of Sustainable Competitive Advantage. (The OCIS Division of the Academy of Management Annual Meeting, Boston, August 3-7, 2012). 4. Lim, J.H., Stratopoulos, T., and Wirjanto, T. The Impact of Senior IT Executives on IT Capability. (The 2012 Canadian Academic Accounting Association, Charlottetown, PEI, May 3- June 3, 2012). 5. Lim, J.H., Stratopoulos, T., and Wirjanto, T. Reciprocity between Senior IT Executives and IT Capable Firms: A Source of Sustainable Competitive Advantage. (The 2012 AIS Mid-Year Conference, Phoenix, AZ, January 5-8, 2012 2011 1. Lim, J.H., T. Stratopoulos and T. S. Wirjanto. Role of IT Executives on the Firm's Ability to Achieve Competitive Advantage Through IT Capability. Presented at the 2011 International Conference on Enterprise Systems, Accounting and Logistics, Thassos Island, Greece, July 10-12, 2011, and it won the Best Paper Award) 2. Lim, J.H., T. Stratopoulos and T. S. Wirjanto. Reciprocity between CIO Power and IT Leadership. Presented at the 2011 Annual Meeting at American Accounting Association, Denver, August 6-10, 2011; the 2011 Canadian Academic Accounting Association, Toronto, May 26-29, 2011; the 2011 AIS Mid-Year Conference, Atlanta, GA, January 5-8, 2011. 3. Lim, J.H., T. Stratopoulos and T. S. Wirjanto, Reciprocity between Senior IT Executives and IT Capable Firms: A Source of Sustainable Competitive Advantage. Presented at the the UWCISA and International Journal of Accounting Information Systems 7th Bi-Annual Research symposium, Toronto, October 2011; Post-ICIS (International Conference of

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Information Systems) 2011 Conference, Shanghai, China, December 7, 2011 ; the 2012 AIS Mid-Year Conference, Phoenix, AZ, January 5-8, 2012; 4. Ning, C., D. Xu, and T. S. Wirjanto. Modeling Asymmetric Volatility Clusters using Copulas and High Frequency Data. Presented at (i) the 28th Annual Meeting of the Canadian Econometrics Study Group at Ryerson University, on October 21-23, 2011 and (ii) the 45th Annual Conference of the Canadian Economics Association at University of Ottawa on June 25, 2011. 2010 1. Bandyopadhyay, S.P., A. G. Huang, and T. S. Wirjanto. The Accrual Volatility Anomaly. Presented at the 2010 Financial Management Association International (FMA) Annual Meeting, October 20 23, 2010, New York, New York, USA. 2. Bandyopadhyay, S.P., A. G. Huang, and T. S. Wirjanto. The Accrual Volatility Anomaly. Presented at the Northern Finance Association (NFA) 2010 Annual Meeting, September 24-26, 2010, Winnipeg, Manitoba, Canada. 3. Bandyopadhyay, S.P., A. G. Huang, and T. S. Wirjanto. The Accrual Volatility Anomaly. Presented at the 37th Annual Meeting of the European Finance Association, August 2528, 2010, Campus Westend, Goethe University Frankfurt, Germany. 4. Xu, D., J. Knight and T. S. Wirjanto. Asymmetric Stochastic Conditional Duration Model: A Mixture-of-Normal Approach, Presented at the Econometric Society's 10th World Congress, August 17-21, 2010, Shanghai, China. 5. Xu, D., J. Knight and T. S. Wirjanto. Asymmetric Stochastic Conditional Duration Model: A Mixture-of-Normal Approach, Presented at the Rimini Conference in Economics and Finance, June 10-13, 2010, Rimini, Italy. 6. Bandyopadhyay, S.P., A. G. Huang, and T. S. Wirjanto. The Accrual Volatility Anomaly. Presented at the 2010 American Accounting Association (AAA) Annual Meeting, July 31 August 4, 2010, San Francisco, California, USA. 7. Bandyopadhyay, S.P., A. G. Huang, and T. S. Wirjanto. The Value of Long-Term Accrual Management. Presented at the Midyear Meeting of the Financial Accounting and Reporting Section (FARS), January 22, 2010, San Diego, California, USA. 2009 1. Ning, C. and T. S. Wirjanto. Extreme Return-Volume Dependence in East-Asian Stock Markets: A Copula Approach. Presented at the 43rd Annual Conference of the Canadian Economics Association, May 29 May 31, 2009; hosted by University of Toronto, Toronto, Ontario, Canada. 2. Xu, D. and T. S. Wirjanto. A Mixture-of-Normal Distribution Modeling Approach in Financial Econometrics: A Selected Review. Presented at the 2009 Chinese Economists Society's Annual Conference, June 12-15, 2009, hosted by Guangxi University in Nanning, Guangxi, China. 2008 1. Xu, D., J. Knight and T. S. Wirjanto. An Improved Stochastic Conditional Duration Model. Presented at the 2008 China International Conference in Finance, July 2-5, 2008, Dalian, China;

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organized by China Center for Financial Research, Tsinghua University, China and Sloan School of Management, MIT, USA. 2. Ning, C., Xu, D. and T. S. Wirjanto. Modelling the Leverage Effect with Copulas and Realized Volatility. Presented at the 25th Annual Meeting of Canadian Econometrics Study Group (CESG), September 26-28, 2008; hosted by Department of Economics, Concordia University, Montreal, Quebec, Canada. 2007: Presenting at the 24th Annual Meeting of Canadian Econometrics Study Group (CESG) Conference, September 29-30, 2007; hosted by McGill University, Montreal, Quebec, Canada. 2000: Computational Economics Society, Barcelona. 1999: the Annual Meeting of Canadian Econometric Study Group (CESG), University of Montreal, Montreal, Quebec, Canada. 1998: John Deutsch Institute for the Study of Economic Policy, Queen's University, Kingston, Ontario, Canada. 1996: Institute of Mathematical Statistics (IMS) Special Topics Meeting, University of Georgia. Georgia, Atlanta, USA. 1992-2010: Annual Meetings of Canadian Economic Association. 1991-1994: Annual Meetings of the Canadian Macroeconomic Study Group. 1991: Conference on Aspects of Labour Market Behaviour, University of Guelph, Guelph, Ontario, Canada. At Universities Brock University, Dalhousie University, McMaster University, Princeton University, University of Guelph, University of Laval, UQAM at Montreal, University of Ryerson, University of Toronto, University of Windsor, Wilfrid Laurier University, York University. Refereeing Duty for Selected Journals Applied Economics, Applied Financial Economics, Bulletin of Economic Research, Canadian Journal of Economics, Canadian Journal of Statistics, Canadian Public Policy, Computational Statistics, Economic Journal, Economic Modelling, Economica, Emerging Markets Finance and Trade, Empirical Economics, European Economic Review, Finance Research Letters, Journal of Business and Economics Statistics, Journal of Economic Dynamics and Control, Journal of Econometrics, The Journal of Financial and Quantitative Analysis, the Journal of Futures Markets, Journal of International Money and Finance, Journal of Macroeconomics, Journal of Money, Credit and Banking, Management Science, The Journal of Portfolio Management, The Manchester School, Mathematical Finance, New Zealand Journal of Economics, Pacific Economic Review, Review of Economics and Statistics, Review of Finance, Review of Quantitative Finance and Accounting.

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Research Funds and Awards Research Funds 1993 UW-SSHRC Investigation into the Consumption Behaviour in Canada $5,000 1994 UW-SSHRC Empirical Investigation into the Petroleum Products $5,000 1994 SSHRC Econometric Study of Inventory Behaviour in Canada $38,000 1996 SSHRC 13th Annual Meeting of the Canadian Econometric Study Group $10,000 2000 UW-SSHRC Travel Grant $1,500 2004 UW-SSHRC The Utility of Regional-level Data to Studying Aggregate

Macroeconomic Behaviour $5,000 2006 UW-SSHRC Testing for External and Internal Habits on Regional

Consumption Data $5,000 2006 UW-IQFI Capital Structure and Corporate Governance in China $13,000 2007 UW- IQFI Regime Switches and International Asset Allocations $16,000 2007 UW- IQFI Estimation of Diffusion Processes with Jumps $20,000 2007 TD TD-UW Computational Finance Research Partnership Agreement $20,000 2008 SSHRC Earnings Quality - A Cross Country and Market Comparison of Chinese and Indian Firms $66,000 2008 SSHRC Information Technology Innovation: Persistence, Antecedents and Performance Implications $167,958 2008 SSHRC-4A On Asset Returns, Volatility, and Value at Risk $8,000 2010 SSHRC-4A Decomposing the Effects of Increased Volatility

Uncertainty of Firms $8,000 2010 SSHRC-4A Are Returns from Information Uncertainty Anomalous? An Explanation Based on Distress Risk and Firm Failure $8,000 2010 SSHRC-4A Asymptotically Efficient Estimation of Switching

Regression Models with Applications in Finance $8,000 2011 SSHRC Examining the Effects of Increased Volatility Uncertainty

of Firms $64,690 2011 SSHRC Determinants of Financial Analyst Following and Its Value

Indications $87,062. 2012 SA Integrated Risk Management: With Applications to

Insurance Companies and Other Financial Institutions $500,000 2012 GRI Low for Long $92,000 2012 RHHSS Reciprocity between Information Technology Innovative Firms and Senior Executives: A Source of Competitive

Advantage $5,500

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Notes: SSHRC=Social Sciences and Humanities Research Council; Note: SA = The Society of Actuaries; GRI=Global Risk Institute in Financial Services; RHHSS= Robert Harding Humanities and Social Sciences Endowment Award. Awards & Nominations 1994: Best Professor of the Year Award (Department of Economics). 2001: Nominated for the University Distinguished Teacher Award. 2004: University Special Merit and Outstanding Performance Award for 2004. 2006: University Outstanding Performance Award for 2005. 2008: University Outstanding Performance Award for 2007. 2009: Nominated for CIBC Chair in Financial Markets by DeGroote School of Business

at McMaster University, Hamilton, Ontario, Canada. 2011: Recipient of University Outstanding Performance Award for 2012 in recognition

of record of excellence in teaching and scholarship. 2012: Nominated by Zhejiang University (http://www.zju.edu.cn/english/), to be

considered by the Ministry of Education of China to receive a Chang Jiang Chair Professorship in Financial Econometrics.

2012: Nominated in 2012 by Zhejiang University (http://www.zju.edu.cn/english/) and, concurrently also by Nanjing University (http://www.nju.edu.cn/html/eng), to be considered by the Central Coordination Committee of the People's Republic of China for an award under the Thousand Talents Program, also known as the Recruitment Program of Global Experts.

Notes: (1) The Changjiang Chair Professorship () is considered the highest academic award ever in the People's Republic of China. It is run jointly by the Ministry of Education () and Li Ka Shing Foundation (). These awards are given to worlds foremost top leaders in advanced research in all areas of higher education, and chosen on the basis of nationwide competition among the nominees put forward by leading Chinese universities in all fields of academic studies. The objective of the award is to improve Chinas standard of education and intellectual competitiveness by rapidly developing Chinese research institutions through the engagement of Changjiang Scholars; and (2) Thousand Talents Program (), also known as the Recruitment Program of Global Experts () is run by the Central Coordination Committee of the People's Republic of China. Its aim is to recruit worlds best and foremost top scientists and talents back to China. It is hoped that the recruited talents will lead the innovative industries, improve key technologies and develop the high-tech industries in State Key Labs, National Key Innovative Programs, State Key Subjects, National Enterprises, State-Owned Commercial and Financial Institutes and High-tech Development Zones.

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Other Scholarly Contributions 2013: Organizing a Quantitative Behavioural Finance on behalf of the University of

Waterloo in collaboration with the Nanjing University, April 5. 2012: Track Chair of Asset Pricing for 2012 Midwest Finance Association Conference

held on February 21 24 in New Orleans. 2011: Program Committee for the 28th Annual Meeting of the Canadian Econometrics

Study Group at Ryerson University, on October 21-23. 2010: Co-host the SSHRC funded Conference on Financial Reporting in Emerging

Markets at the University of Waterloo, March. 1999 -2010: Organize the Annual Financial Econometrics Conference at the University of

Waterloo. 2006: Co-host the 23rd Annual Meeting of the Canadian Econometric Study Group,

October 19-21. 2006: Co-host Festkolloquium in Honour of Phelim Boyle, Conference to be held June

29-30, University of Waterloo -Waterloo, Canada. 1996: Host the 13th Annual Meeting of the Canadian Econometric Study Group,

September 20-22. 2001: Co-host the 18th Annual Meeting of the Canadian Econometric Study Group,

September 20-22. CONSULTING ACTIVITY

2012-2013: Global Risk Institute in Financial Services

2011: Mutual Life Financial 2007-2008: Organisation for Economic Co-operation and Development (OECD).

2003, 1999: Finance Canada. 2001-1990: Bank of Canada. 1999, 1998: Canadian Tourism Commission. 1997: Thailand Development Research Institute. 1997: Harvard Institute of Economic Development, Harvard University. COMMITTEE RESPONSIBILITY University Level 2011: Vice Presidents Representative for Faculty of Environment Tenure & Promotion Committee 2011, 2003, 2001: Member of Universitys Appeal Committee (Tribunal) for Tenure & Promotion. 2009-Present: Arts Faculty Council Representative to the Mathematics Faculty Council. Faculty Level

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2011-Present: Department of Statistics & Actuarial Sciences Representative to Mathematics

Faculty Council 2008-2009: Member of Faculty of Arts Tenure and Promotion Committee. 2005-2006: Nominating Committee for the Director of School of Accountancy. 2001-2002, 2004-2005: Nominating Committee for the Chair of Department of Economics. Departmental Level 2010-Present: Tenure & Promotion, School of Accounting & Finance. 2010: Performance Review Committee, Department of Statistics & Actuarial Science. 2009-Present: Tenure & Promotion, Department of Statistics & Actuarial Science. 2009-Present: Hiring Committee, Department of Statistics & Actuarial Science. 2000-2002: Associate Chair, Undergraduate Affair, Department of Economics. 1997-1998: Admission Officer for the Collaborative Master Program in Finance. 1997-1998: Seminar Coordinator, Department of Economics. 1996-1997: Seminar Coordinator, Department of Economics. 1996-2004: Tenure & Promotion Committee, Department of Economics. 1996-1998: Teaching Committee, Department of Economics. 1995-2000, 2004-2007: Graduate Committee, Department of Economics . 1992-2005: Computing Committee, Department of Economics. 1994-2003: Undergraduate Advisor, Department of Economics. 1991-1993, 1999-2009: Recruiting Committee, Department of Economics. 1992: Informal Sub-committee for Macroeconomics, Department of Economics. 1992: Informal Sub-committee for Econometrics, Department of Economics. SUMMARY OF CURRENTLY FUNDED PROJECTS Title: Integrated Risk Management: With Applications to Insurance Companies and Other Financial Institutions. Collaborators: Carole Bernard, Phelim Boyle, Mary Hardy, Joseph Kim, Adam Kolkiewicz, Johnny Li, David Saunders, and Ken Seng Tan. All collaborators are faculty members at the Department of Statistics & Actuarial Science, with an exception of Phelim Boyle, who is a faculty member at the School of Business and Economics, Wilfrid laurier University, Waterloo, Ontario, Canada Funded Agency: The Centers of Actuarial Excellence (CAE) Grants Committee (CGC) of the Society of Actuaries (SOA) Year of Award: 2012 Title: Determinants of Financial Analyst Following and Its Value Indications. Collaborator: Hong Ping Tan and Pat OBrien, School of Accounting & Finance, University of Waterloo

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Funded Agency: SSHRC Year of Award: 2011 Summary: The research program comprises three studies to provide empirical assessments of the determinants and consequences of financial analyst following. The first study investigates the impact of geographic proximity on the likelihood and timing of analyst coverage initiation for US IPO firms during 1996 2009. Abundant evidence reveals that analyst coverage affects price informativeness, earnings management, financing activities, firm value, and cost of capital. The consensus emerging from this literature is that analysts provide valuable information about the firms they cover and play an important role in monitoring managers, especially to those that are less transparent and with more severe agency problems. In fact, some managers value the analyst coverage so much that they are even willing to pay a fee-based research firm to cover their companies. Previous studies suggest that the cost of acquiring information increases with geographic distance. Thus we conjecture that analysts are more likely to cover proximate firms, ceteris paribus. Motivated to provide prompt coverage, analysts will initiate coverage on proximate firms earlier if proximity facilitates their information collection and processing capability. Finally we will explore the implication of value implication of local analyst coverage for the target firms. Given that IPO firms are usually small and less known to the public, we are especially interested in understanding whether local analyst coverage provides the covered firms a stepping stone to gain greater visibility in the future by attracting institutional investors and other financial analysts, especially those remote ones who otherwise would have neglected these firms. The second study includes two parts. The first part deals with the modelling of firm-level analyst following in a time-series and cross-sectional setting. We propose to use an autoregressive ordered probit (AOP) model to allow for an autoregressive time structure in the analyst data. The second part of this project uses the model we are going to develop to predict the expected analyst following for a firm-year so that we could compute the residual analyst following and relate it to the firm's future abnormal returns and accounting performance. The third study will examine the investment value and usefulness of target prices to presumably the most sophisticated investors and the major player in the market - institutional investors. To the extent that analyst target prices contains new information, we expect institutional investors to trade on these forecasts and profit from them. Based on this primary conjecture we hypothesize that target price changes of a stock are positively related to the changes in the institutional holdings and to the changes in the number of institutional investors holding the stock; that target price changes are positively related to the stock's future abnormal returns. Title: Examining the effects of increased volatility uncertainty of firms Collaborator: Alan G. Huang, School of Accounting & Finance, University of Waterloo Funded Agency: SSHRC Year of Award: 2011

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Summary: A salient feature of an economic downturn is the apparent increase in volatility of firms stock returns. Usually this increased level of volatility is attributed to the fact that the underlying business conditions have become increasingly more volatile. In this project we propose that learning about the underlying business conditions can also add to the observed volatility. To this end volatility of stock returns is decomposed into: (1) volatility due to fundamental change in firms - which we call fundamental uncertainty, and (2) estimation uncertainty that arises from learning about the parameters of the firms fundamentals - which we call learning uncertainty. We propose to evaluate the relative importance of each of these uncertainties in the increased volatility of young and mature firms stock returns in this research. Learning uncertainty arises because investors lack perfect foresight and have to constantly assess firms changing operating environments, which can be unrelated to their fundamentals. An example of this pertains to a relatively short listing history of a typical young firm, which can impede investors ability to better follow the firm; or a certain unorthodox event announced by a firm which leads to a wider divergence of opinion among investors in the market about the firm. As an example of this unorthodox announcement, during the internet bubbles period some companies changed their names that were related to dot.com. This action alone apparently raised their stock prices overnight without the companies actually changing any of their business activities. It is reasonable to argue that such an event is unrelated to the firms fundamentals, and, yet, it raises investors uncertainty in learning about this firm and, in the process, renders the firms stock returns more volatile. Our proposed research has two parts. In the first part, we analytically decompose the volatility of a firms stock returns into fundamental uncertainty and learning uncertainty. The latter is specified as a declining function of time and intended to capture investors inaccuracy in making the forecast about the mean profitability level. In other words, the accuracy of investors forecasts is postulated to be an increasing function of the length of time the firm has been listed. While a positive association between increased fundamental uncertainty for young firms and its asset returns has been relatively well established in the literature, the impacts of learning uncertainty are still relatively unexplored. Using this framework, we quantify the increased return volatility into uncertainty about a firms fundamentals and learning uncertainty due to information asymmetry or differential information. In the second part, we use several proxies for a firms learning effect, including the public listing age of a company, firms foundation age and e-loadings to accounting quality factor. We then examine the relative importance of fundamental uncertainty versus learning uncertainty in the increased volatility uncertainty, and assess which effect dominates over a time horizon. Using firm listing age as example, we specifically investigate (i) whether there is an age pattern in stock return volatility for firms; (ii) whether there is a monotonic relationship between the volatility of stock returns and its listing age once we control for the time effect; (iii) whether the fundamental effect changes as the firm matures; that is, whether young firms profitability level deteriorates over the sample period of 1980-2008; and (iv) whether young firms have lower profitability level than old firms, and whether

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young firms have much higher variability in growth rate of earnings and higher volatility of profitability than old firms. We believe that our research output will contribute to better understanding of the causes of the increased firm volatility in the past three decades, in particular, during economic downturns. Title: Decomposing the effects of increased volatility uncertainty of firms Collaborator: Alan G. Huang, School of Accounting & Finance, University of Waterloo Funded Agency: UW-SSHRC Year of Award: 2010 Summary A salient feature of economic downturns is the apparently increase in volatility of firms' stock returns. Usually this increase is attributed to the fact that the underlying business conditions have become more volatile. In this project we propose that "learning" about the underlying business conditions can also add to the observed volatility. To this end, volatility of stock returns is decomposed into two components: (1) volatility due to fundamental change in firms, which we call "fundamental" uncertainty, and (2) estimation uncertainty that arises from learning about the parameters of the firms' fundamentals, which we call "learning" uncertainty. In this research, we aim to evaluate the relative importance of each of these uncertainties in the increased volatility of firms' stock returns. Learning uncertainty arises because investors lack perfect foresight and have to constantly assess firms' changing operating environments, which can be unrelated to the firms' fundamentals. An example of this is a relatively short listing history of a typical young firm, which can impede investors' ability to better trace the firm; or a certain "unorthodox" event announced by a firm which leads to a wider divergence of opinion among investors about the firm. As an example, during the internet bubbles period some companies changed their names that were related to dot.com. This action alone apparently has raised their stock prices overnight without the companies actually changing any of their business practices. It is reasonable to argue that such an event is unrelated to the firm's fundamentals, and, yet, it can raise investors' uncertainty in learning about this particular firm and, in the process, renders the firm's stock returns more volatile. Our research consists of two parts: (i) we analytically decompose the volatility of a firm's stock returns into fundamental uncertainty and learning uncertainty. The latter is specified as a declining function of time and is intended to capture investors' inaccuracy in making the forecast about the firm's mean profitability level; i.e.,the accuracy of investors' forecasts is postulated to be an increasing function of the length of time the firm has been listed; and (ii) while a positive association between increased fundamental uncertainty of young firms and its asset returns is well established, the impacts of learning uncertainty are still relatively unexplored. Using this framework, we quantify the increased return volatility into uncertainty about a firm's fundamentals and learning uncertainty due to information asymmetry or differential information.

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In the analysis, we use several proxies for a firm's learning effect, including the public listing age of a company, firm's foundation age and e-loadings to accounting quality factor. We then examine the relative importance of fundamental uncertainty versus learning uncertainty in the increased volatility uncertainty, and which effect dominates over a time horizon. We specifically investigate (i) whether there is an age pattern in stock return volatility for firms; (ii) whether there is a monotonic relationship between the volatility of stock returns and its listing age once we control for the time effect; (iii) whether the fundamental effect changes as the firm matures; that is, whether young firms' profitability level deteriorates over the sample period of 1983-2008; and (iv) whether young firms have a lower profitability level than old firms, and whether young firms have much higher variability in growth rate of earnings and higher volatility of profitability than old firms. Title: Are Returns from Information Uncertainty Anomalous? An Explanation Based on Investor Learning and Firm Survival Collaborator: Alan G. Huang, School of Accounting & Finance, University of Waterloo Funded Agency: UW-SSHRC Year of Award: 2010 Summary: Recent literature documents a negative cross sectional association between historical information uncertainty proxies and future returns. These historical information uncertainty proxies include analyst forecast dispersion [Diether, Malloy and Scherbina (2002)], idiosyncratic return volatility [Ang et al. (2006)], and cash flow volatility [Huang (2009)]. In this strand of literature, the negative association tends to be large, unable to be explained by the existing risk factors in the literature, and long lasting to horizons as long as five years. These results stand in stark contrast with the traditional risk-return tradeoff and hence are called the information uncertainty anomaly. This research evaluates current explanations offered for the above phenomenon and aims to provide an equilibrium-based explanation. We first evaluate two existing explanations: (1) risk-return tradeoff, and (2) limits to arbitrage [Miller (1977), Zhang (2006), and Sadka and Scherbina (2007)]. For the risk-return tradeoff explanation, we investigate the persistence of the above information uncertainty proxies. We make the conjecture that the information uncertainty proxies are is highly persistent and takes considerable amount of time to mean revert; so the risk-return tradeoff does not appear to provide an adequate explanation for the anomaly. As to the limits to arbitrage explanation, we make the conjecture that limits to arbitrage are less binding over longer horizons, so again they are less than convincing in explaining the long-term aspect of the anomaly. As a result, we propose an alternative explanation based on (1) Pastor and Veronesi's (2003) findings of convexity pricing, in combination with (2) the risk of firm distress and failure. Pastor and Veronesi (2003) show that firms with higher profitability uncertainty require higher contemporaneous market to book ratio (but not subsequent lower returns). Based on this result, we predict that firms with higher profitability uncertainty also suffer from high distress risks and,

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according to Campbell et al. (2008), from low future returns due to their high distress risks. We propose to modify the model in Pastor and Veronesi (2003) to incorporate firm failure risk (which naturally entails low returns). We also propose to empirically test the model predictions by dissecting the information uncertainty anomaly. We plan to carry out the empirical investigation in two steps. First we establish that the anomaly is primarily driven by smaller/younger firms and firms with high failure risk. After we characterize the driving portfolios for the anomaly, we show that the information uncertainty is resolved or, at the very least, considerably alleviated after controlling for firm failure. Any unexplained anomaly, we hope to show, are primarily due to limits to arbitrage. Our research addresses one important anomaly in finance that essentially says high beta but low returns. In this project we consider investor learning and firm survival as a potential answer to this anomaly. We hope that from this project we can better understand factors that affect future stock returns. Our idea can also be used to explain settings such as the earnings momentum anomaly and IPO overpricing and long term under-performance. References: Ang, A., R. J. Hodrick, Y. Xing and X. Zhang (2006), The Cross-Section of Volatility and Expected Returns, Journal of Finance, 51 (2006), 259299. Diether, K., C. Malloy and A. Scherbina (2002), Differences of Opinion and the Cross-Section of Stock Returns, Journal of Finance 57, 21132141. Huang, Alan G. (2009), The Cross Section of Cash Flow Volatility and Expected Stock Returns, Journal of Empirical Finance 16, 409-429 Miller, Edward M. (1977), Risk, Uncertainty and Divergence of Opinion, Journal of Finance 32, 1151-1168. Pastor, L. and P. Veronesi (2003), Stock Valuation and Learning about Profitability, Journal of Finance 58, 17491790. Sadka, R and A. Scherbina (2007), Analyst Disagreement, Mispricing and Liquidity, Journal of Finance 62, 2367-2403. Zhang, X. F. (2006), Information Uncertainty and Stock Returns, Journal of Finance 61, 105136. Title: Regime Switching Models in Finance Collaborator: Dinghai, Xu, Department of Economics, University of Waterloo Funded Agency: UW-SSHRC Year of Award: 2010 Summary: A distributional assumption of returns on financial assets is known to play an important role in both financial modeling and its applications. The most convenient assumption, until recently, has been that asset returns follow a stationary Gaussian/normal process. This is partly motivated by

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the view that in the long run, asset returns are approximately normally distributed. However, the distribution of returns on financial asset has been found to exhibit substantial leptokurtosis (fat tails) and, in many cases, also skewness (asymmetry around the mean) relative to those of a Gaussian distribution. One way to accommodate this stylized fact is to introduce a more flexible distribution model. In recent years, increasing attention has been focused on the Gaussian mixture family since any continuous distribution can be approximated arbitrarily well by an appropriate finite Gaussian mixtures. In addition, finite-mixture models can also accommodate discontinuous jumps or shifts among regimes in the data. The most intuitive underlying assumption is that the sample data is drawn from different sub-group components. In other words, the population can be viewed as a mixture of distinct sources. In finance applications, for example, the stock returns can be viewed as one that arises from a multiple source of information, such as a firm-specific information component, a market-wide information component, and a non-information component, see Kon (1984). Alternatively, Xu and Wirjanto (2008) propose that the financial asset returns could be drawn from a two-regime (bear / bull) DGP in the empirical applications. More recently, empirical evidence has indicated that financial asset returns display not only excess skewness and leptokurtosis, but also time-varying characteristics. As a natural extension, incorporating the dynamic element into the mixture model structure is needed for accommodating more stylized facts on the financial market. However, there are a number of challenging issues that we note. The mixture models have an intuitive appeal and flexible structures, but empirical applications have been limited due to the difficulties in the parameter-estimations. As a more generalized mixture models with time-varying components, it would lead to more complicated computational problems. These include the expansion of the parameter space, the efficiency of the estimation approach and etc. Secondly, most of the recent regime-switching literature focuses on the dynamics of the volatility behavior. However, accommodating the time-varying mean components (or both) would interestingly result in a more realistic model framework. Furthermore, finding suitable models for financial asset returns is the first step in risk management. Once the evolution of the asset returns is successfully modeled, financial institutions can construct the risk measures based on the appropriate models. How to design risk measures implied from the proposed models would be a further interesting question to investigate. This research project is preliminarily designed in three main stages. In the first stage, a more generalized model which accommodates aforementioned features will be developed and a feasible estimation methodology will be designed according to the proposed model. The second stage will consist of various simulation experiments. The purpose of this simulation exercise is to test the validity of the proposed model and the feasibility and efficiency of the corresponding estimation procedure. In the last stage, a large group of empirical analysis will be involved to demonstrate the advantages of the proposed model in capturing the dynamic features of the real-life financial data. In addition, the associated risk analysis will be constructed to provide some implications regarding to the risk policy perspectives, such as bank capital standards for market risk and the reporting requirements for the risks associated with derivatives used by corporations. As a result, this project will provide the financial institutions with a more

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accurate measure that they need to manage the trading risk on the market. References: Kon, S. (1984), Models of Stock Returns - A Comparison, The Journal of Finance, 39, 147 165. Xu, D. and T. S. Wirjanto (2008), An Empirical Characteristic Function Approach to VaR under a Mixture of Normal Distribution with Time-Varying Volatility, Working paper, University of Waterloo, forthcoming in the Journal of Derivatives. Title: Earnings Quality - A Cross Country and Market Comparison of Chinese and Indian Firms. Collaborators: Ranjini Jha, Sati Bandyopadhyay and Alan G. Huang, School of Accounting & Finance, University of Waterloo, and Yao Tian, School of Business, University of Alberta Funded Agency: SSHRC Year of Award: 2008-09 Summary: Investigate the quality of net income reported by Indian and Chinese firms versus those of North American firms, since net income is probably the most important accounting number investors tend to focus on. Also compare earnings quality of Indian versus Chinese firms, particularly those that are cross-listed in the U.S. and in Canadian stock exchange. In our analysis of earnings quality, we control for institutional differences such as investor protection rights, and firm-specific differences such as government/family holdings and quality of monitoring. We expect this study to provide useful information to American and Canadian investors and regulators and standard setters in all four jurisdictions. Research Output: 1. Organizing a one-day conference on Financial Reporting in Emerging Markets at the University of Waterloo, March 19, 2010. 2. The Consequences of Market Reforms in the Presence of ROE-Based Listing and Financing Requirements: Earnings Quality Changes in China, Power Point Slides for Presentation. 3. Market Reforms, Regulations, and Earnings Quality in Emerging Markets: the Case of China, Unpublished Manuscript. Title: Information Technology Innovation: Persistence, Antecedents and Performance Implications. Collaborators: Jee-Hae Lim and Theo C. Stratopoulos, School of Accounting & Finance, University of Waterloo Funded Agency: SSHRC Year of Award: 2007-2010 Summary: We show that innovation with IT is a cumulative and path-dependent capability not easily replicated. Thus, companies that take a systematic approach towards IT innovation and companies that develop the capability to innovate with IT over time, are likely to sustain this capability and maintain their status as systematic innovators when compared to their peers. Then

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we explore firm characteristics of systematic IT innovators. We look at corporate and IT governance to support our claim that the higher the understanding of the role of IT at the board and executive level the higher the chances that the organization will take a systematic approach towards IT innovation and sustain this capability. Lastly, we establish the link between the IT innovation capability (i.e., a firms ability to innovate with IT over time) and financial and market measures of firm performance. More specifically, we argue that companies that take a systematic approach to innovation with IT are likely to outperform their competitors. Research Output: Lim, J. H. and T. C. Stratopoulos and T. S. Wirjanto (2010), Durable Heterogeneity of Dynamic IT Capability: Theoretical Framework and Empirical Evidence, Revised and Resubmitted. Notes: SSHRC stands for Social Sciences and Humanities Research Council, which is Canada's federal funding agency for university-based research and student training in the social science. Website: http://www.sshrc-crsh.gc.ca/home-accueil-eng.aspx SUMMARY OF SELECTED PAST FUNDED PROJECTS Title: The Transition from Physical to Risk-Neutral Measures for Options Pricing Valuation (2007-2008) Collaborator: Shan Chen, PhD student, Department of Economics, University of Waterloo Funded Agency: IQFI Year of Award: 2007-2008 Summary: The purpose of this project is to bridge two important strands of the literature in Finance, hitherto relatively unexplored; one pertains to the objective or physical measure used to model the underlying asset and the other to the risk-neutral measure used to price derivatives. However, building a bridge between the objective and risk-neutral measures raises several important issues which need to be addressed first. In addition, it also opens up possibilities for comparing the information in the underlying fundamental and options data, a theme which has been the subject of some research in the past. Our goal for the project is [i] to learn more about the informational content of option prices; and [ii] to know how to improve the statistical precision of diffusion parameters by incorporating options. We propose to investigate the above questions in a unifying framework, by using a stochastic volatility model with jumps (SVJ). The estimation and appraisal of the model take place by exploiting a joint distribution of fundamentals and options. Specifically, we propose a generic procedure for estimating and pricing options using simultaneously the fundamental price, and a set of option contracts involving the Black-Scholes implied volatility. In principle, we manage a panel of options, i.e. a time series of cross sections. We note that our analysis is not limited to any particular model. The choice of the SVJ model is particularly convenient because [i] it has closed-form option pricing formula, which represents a

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considerable computational advantage; and [ii] because the SVJ model has received much attention in the literature, which makes our analysis comparable with results previously reported. In addition, Finance theory also suggests that, for stochastic volatility models with two state variables, we should consider the fundamental and its derivative contracts jointly to estimate diffusion parameters and price options simultaneously. Thus there are compelling theoretical reasons to pursue this approach, as in a stochastic volatility economy options must be added to create a complete market model. The complete market model guarantees the existence and uniqueness of the risk-neutral probability density used to price the option contracts. If done judiciously, the proposed approach should dominate the existing approach of using either options or fundamental. The procedure we plan to use in this project is efficient method of moments (EMM), extended to incorporate option prices and fundamentals simultaneously. We show that the EMM procedure, which is a simulation-based estimation technique, allows us to estimate the model parameters under both the objective and risk-neutral probability measures by using simultaneously implied volatilities and the underlying asset data. Indeed, time series of the underlying asset provide parameters under the objective probability measure while risk-neutral parameters can be retrieved from options. Since the model we adopt has a closed-form option pricing formula, we can obtain the volatilities implied by the Black-Scholes formula from the simulated data and contrast them with their counterparts from the real data via the EMM framework. This procedure yields parameter estimates under the risk-neutral measure. Having estimated the risk-neutral and objective measures separately allows us to appraise the typical risk-neutral representations used in the literature. In particular, in order to obtain closed-form solutions, the standard approach assumes that the linearity of the volatility drift is preserved. We are able to determine if this assumption is consistent with the data. Title: Optimal Asset Allocation under Regime Switching Collaborator: None Funded Agency: IQFI Year of Award: 2007 Summary: This research investigates international asset allocations using a novel statistical model that incorporates correlation breakdown which leads to elaborate regime switches. A good model, at the very least, must include persistent time-varying correlations in addition to regime switches if it is to reproduce most of the other stylized facts of asset returns. This project consists of two stages. The first stage will inquire into optimal asset allocation under regime switches. Here, I will first solve the portfolio selection problem defined by continuous-time regime switching models (RSMs). Within this class of models, the return generating process is a function of a discrete state process. In each state, the returns are drawn from a different geometric Brownian motion. The state process thereby generates a stochastic opportunity set. Thus investors hold an intertemporal hedge portfolio under regime-switching models. I will present two models which differ only by the information the investors have on the

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active regime. The first model assumes that investors have full information on the active regime. The second model treats the state process as hidden and investors must infer the probability of the regimes being active. The first-order conditions in this second case cannot be solved in closed-form; so I will resort to Monte-Carlo simulation to obtain the optimal weights. Then I will develop numerical algorithm to investigate the empirical portfolios of RSMs and their performance in an out-of-sample horse race. In the second stage, I will tackle international asset allocations under regime switches. The RSMs constructed in the first stage will be applied to three stock markets. I will provide evidence on the existence of correlation breakdown in international stock return data and use this to motivate the choice of RSMs to investigate the effect of correlation breakdown on international portfolio holdings. (Both economically and statistically) significant differences among the regimes will have a potentially strong effect on optimal asset allocations. Following the first stage, I will adopt two approaches to examine international asset allocations. In the first approach, I will assume that the investors have full knowledge of the active regime and derive the resulting optimal international portfolios. In the second approach, I will assume that the state process is hidden from the investors and propose a Monte-Carlo simulation approach to solve the optimization problem with hidden regime switches. In particular I will consider a method based on a set of difference equations to solve the optimization problem for a number of assets and calculate resulting optimal portfolios. In addition I will also focus on the optimal hedging of currency risk and analyze three model specifications with each representing a particular currency hedging policy. The first specification bears full exchange rate risk, the second hedges currency risk completely, and the third specification applies an optimal hedging policy. The performances of the three model specifications will be evaluated against a domestic and an international benchmark model with one regime. Then I will evaluate the performances by adopting two approaches. In the first approach, I will calculate the compensations required to make an investor indifferent between an optimal strategy and a sub-optimal strategy. In the second approach, I will perform an out-of-sample horse race to test the strategies in real market situations. With this particular setup of RSMs and linear benchmark models, I will be able to investigate a number of important and, in some cases, open questions in Finance; that is, this setup will enable me to evaluate the benefit of international diversification, investigate the effect of regime switching on optimal weights and performances, study the optimal currency hedging policy, and investigate the optimal level of foreign investments and relate it to so-called home-bias puzzle. Title: Capital Structure and Corporate Governance in China (2005-2007) Collaborators: Yao Tian, School of Business, University of Alberta and Yanmin Qian, School of Economics, Zhejiang University Funded Agency: IQFI Year of Award: 2005-2007 Research Output: 1. Qian, Y. Y. Tian and T. S. Wirjanto (2009), Do Publicly Listed Chinese Companies Adjust

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their Capital Structure toward a Target Level? The China Economic Review, 20, 662676. 2. Qian, Y., Y. Tian and T. S. Wirjanto (2008), Capital-Structure Determinants of Publicly Listed Chinese Companies, submitted Title: Neural Network Modeling of Noisy Financial Time Series (2005) Collaborators: Peter Kim, Department of Statistics, University of Guelph and Lin Pan, Bank of Montreal.. Year of Award: 2005 Research Output: 1. Neural Network Models of the Spot Canadian/U.S. Exchange Rate Summary: This paper proposes several predictive nonlinear transfer function models between short-term interest-rate spread and daily spot Canadian/US foreign exchange rate, using multi-layer feed-forward neural networks with back-propagation learning algorithm. A comparative pre-test of the neural network model is constructed to evaluate the network performance and to select the best model. All of the testing models yield about 55% - 60% accuracy of the directional forecast on the out-of-sample test set. Comparing with the linear predictive models, a 2% to 5% gain is obtained by using neural network models. In particular, one of the models proposed in this paper, namely the separate neural networks model, is able to explore the nonlinear relationship between the spot Canadian/US foreign exchange rate and short-term interest-rate spread during a period of negative interest rate spread. Furthermore it is able to capture a corrective mean reversion when the Canadian dollar is under or over-valued in the market. The comparative pre-test also demonstrates the impact changes in the interest rate spread have on changes in the spot rate. As an aside the pre-test provides numerical evidence on the stable relationship between the short term interest rate spread and the spot Canadian/US foreign exchange rate. 2. Local Stability Analysis of Neural Network Models with Application to Exchange- Rate Data Summary: In this paper we discuss the stability property of a predictive neural network model from a deterministic point of view. In particular, the stability property of linear and nonlinear causal transmission link models of daily spot Canadian/US foreign exchange rate is analyzed using a local stability analysis based on a nonlinear dynamical systems framework. This analytical result enables a numerical analysis of the stability to be fully testable on the data set. Also the stability of the interval prediction of a general neural network model is studied in this paper. 3. Jackknife Learning Algorithms for the Neural Network Model of Exchange Rate Summary: In this paper, we propose two grouped jackknife algorithms and apply them to a separate multi-layer feed-forward neural-network model of the spot Canadian/US foreign exchange rate. The integrated method delivers a reasonably reliable forecast of the spot rate along with a large amount of statistical information associated with the historical data. 4. Bootstrapping Neural-Network Models of Exchange Rate Summary: In this paper, we provide a framework to quantify a forecast of noisy financial time series through an interval prediction by integrating two computationally oriented methods, namely neural network and bootstrap. In particular, we develop parametric and non-parametric

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bootstrap cross-validation learning algorithms and apply them to a multilayer feed-forward neural-network model of the spot Canadian/US foreign exchange rate, exploiting the existence of a stable transmission link between the spot rate and the short term interest-rate Using the integrated method, we are able to uncover a hidden nonlinear structure between the spot rate and the short-term interest-rate spread during the period of negative interest-rate spread. Also, using this method, we are able to capture a corrective mean reversion when the Canadian dollar is under or over-valued in the market. Lastly, this method allows us to obtain a reliable forecast of the spot rate along with a large amount of statistical information associated with the historical data. GRADUATE SUPERVISORY ACTIVITY POST-DOCTORAL FELLOW 2012 2013: Dr. Chris Zhongxian Men, Statistics. PHD THESES (AS SUPERVISOR AND CO-SUPERVISORS) 2012-2013: Lichen Chen, Statistics (in progress). 2011-2013: David Wilson, Statistics (in progress). 2011-2013: Amir Memartoluie, Computer Science (in progress). 2009-2012: Abdullah Almansour, Economics, Essays in Risk Management for Crude Oil Markets, Assistant Professor, Department of Finance & Economics, College of Industrial Management, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. 2007-2010: Shan Chen, Economics, Modelling the Dynamics of Commodity Prices for Investment Decisions under Uncertainty, Manager, Market and Trading Credit Risk, RBC. PHD THESES (AS INTERNAL/EXTERNAL EXAMINER OR COMMITTEE MEMBER) 1. Shane Rolands, Statistics and Actuarial Science, Sensitivity analysis of simulations and the Monte Carlo optimization of stochastic systems, 1993. 2. Amin Mawami, School of Accountancy, Cancellation of executive stock options: tax and accounting income considerations, 1995. 3. Paul Andre, School of Accountancy, Special items, earnings announcements, and divergence of analyst beliefs, 1995. 4. David X. Li, Statistics and Actuarial Science, The estimating function approach to credibility theory, 1995. 5. C. Nadeau, Statistics and Actuarial Science, Inference for point processes through estimating functions, 1995. 6. Kathryn Bewley, School of Accountancy, The economic consequences of financial 5. reporting standards: the market valuation of environmental liabilities, 1998.

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7. Steve Fortin, School of Accountancy, Derivatives recognition and hedge-accounting treatment: an empirical study of the rules prescribed by SFAS 133 and some alternatives, 1999. 8. Jeff Pittman, School of Accountancy, The influence of firm maturation on firms tax-induced financing and investment decisions, 2000. 9. Zejiang Yang, Statistics and Actuarial Science, Multiple roots of estimating functions and applications, 2000. 10.Yufei Yin, Agricultural Economics, University of Guelph, Hedging financial and business risks in commodity-linked bonds, 2002. 11. Thomas Matthews, School of Accountancy, Does home country taxation of foreign earnings affect cross-jurisdictional income shifting? 2002. 12. Sandy Hilton, School of Accountancy, The role of regulation and enforcement in securities market interpretation of accounting information, 2003. 13. Shenghai Zhang, Statistics and Actuarial Science, The statistical analysis of clustered data, 2003. 14. Flora Niu, School of Accountancy, Earnings quality, off-balance-sheet risk, and accounting for transferring of financial assets: an analysis of FAS125, 2004. 15. John Pham, School of Planning, Optimization schemes for Vietnams electric power generation planning, 2005. 16. Peng Zhang, Statistics and Actuarial Science, Contributions to Mixed Effects Models for Longitudinal Data, 2006. 17. Devan Mescall, School of Accounting & Finance, How do tax and accounting policies affect cross-border mergers and acquisitions? 2007. 18. Hyun Tae Kim, Statistics and Actuarial Science, Estimation and allocation of insurance risk capital, 2008. 19. Yao Tian, School of Accounting & Finance, The impact of earnings management and expectations management on the usefulness of earnings and analyst forecasts in firm valuation, 2008. 20. Thomas Schneider, School of Accounting & Finance, Is there a relation between the cost of debt and environmental performance? An empirical investigation of the U.S. Pulp and Paper industry, 1994-2005, 2008. 21. Jie Zhang, David R. Cheriton School of Computer Science, Promoting honesty in E-marketplaces: combining trust modeling and incentive mechanism design, 2009. 22. Mingcui Su, Department of Economics, Three Chapters on the Labour Market Assimilation of Canadas Immigrant Population, 2010. 22. Mario Ghossoub, Department of Statistics & Actuarial Science, Contracting under Heterogeneous Beliefs, 2011. 23. Claymore James Marshall, Department of Statistics & Actuarial Science, Financial Risk Management of a Variable Annuity Option: The Guaranteed Minimum Income Benefit, 2011. 24. Bei Chen, Department of Statistics & Actuarial Science, Linearization in Nonstationary and Nonlinear Time Series, 2011 25. Yutao, Li, School of Accounting & Finance, Accounting Conservatism and the Consequences of Covenant Violations, 2011.

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26, Dian Zhu, Department of Statistics & Actuarial Science, Mean-Variance Portfolio Selection and Hedging with Continuous Portfolio Insurance and Trading Constraints, 2011. In Progress. 27. Yanqiao (George) Zhang, Department of Statistics & Actuarial Science, 2013, In Progress. 28. Chris Zhongxian Men, Department of Statistics & Actuarial Science, 2012. 29. Wai Hong Choi, Department of Economics, 2013, In Progress. 30. Louise Hayes, School of Accounting & Finance, Determinants of Undetected, Unintentional Errors in Audited Financial Statements, 2013. In Progress. 31.Zhaoxia Ren, Department of Statistics & Actuarial Science, Estimation and Testing of the Jump Component in Levy Processes, 2013, In Progress. PHD THESES (AS CHAIR) 1. Johnny Li, Department of Statistics & Actuarial Science, 2008. 2. Yingbin, Liu, David R. Cheriton School of Computer Science, 2008. 3. Andrea Scott, Department of Mechanical and Mechatronics Engineering, 2008. PHD THESES (AS EXTERNAL EXAMINER) 1. LingXue Pan, Department of Mathematics and Statistics, University of Guelph, Resampling in neural networks with application to financial time series, 1998. 2. Irene Chen, Department of Economics, Queens University at Kingston, In search of liquidity effects, 1998. 3. Kim Phouc Huyhn, Department of Economics, Queens University at Kingston, Dynamic diversifications and transactions, 2004. 4. Qiao (Cathy) Ning, Department of Economics, University of Western Ontario, Segmentation, duration and dependence in financial markets, 2006. 5. Linlan Xian, Department of Economics, University of Western Ontario, Estimation and forecasting stochastic volatility models with volatility observable, 2010. 6. Ivan Medovikov, Department of Economics, University of Western Ontario, News, Copulas and Independence, 2013. MASTER THESES (AS SUPERVISOR)) 2009: Feiran Tao, Statistics (Finance), Portfolio Optimization with Transaction Costs, Associate at Deutsche Bank, Hong Kong. 2009: Chao Yang, Statistics (Finance), MQF, Pricing and design timer style options under stochastic volatility, Vice President, Quantitative Engineering and Development at TD Securities. 2011: Chris Mu Yang, MQF, Position Hedging in FX Spot Trading From A Market Makers Perspective and FX Tail Risk Analysis (Thesis), Associate (FX Electronic Trading) at CIBC World Markets. 2012: Mingyu (Bruce) Fang, Actuarial Science, A Lognormal Mixture Model for Option Pricing

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with Applications to Exotic Options, Actuarial Associate at Manulife Financial. 2013: Yin-Hei (Michael) Cheng, MQF, Pricing Derivatives by Gram-Charlier Expansions. Financial Analyst at Manulife Financial. 2013: Ziqun (Lyndia) Ye, Statistics, The Black-Scholes and Heston Models for Option Pricing. 2013: Anyi Zhu, MQF (in progress), Analyst in Global Analytics and Financial Engineering (FO Quant) at Scotia Capital. Note: MQF= Master in Quantitative Finance, Dean of Mathematics Office. MASTER ESSAYS (AS SUPERVISOR) 2000: Jennifer Page, Statistics (Finance), The term structure of interest rates: a look at a discrete time - two factor model, Associate Vice President at TD Bank. 2000: Shyam Nagarajan, Statistics (Finance), Estimating the parameters of a stochastic volatility model. Vice President - The Bank of Nova Scotia, Group Audit at DBS Bank. 2006: Alberto Romero, Statistics (Finance), MCMC methods for one and two-factor models of short-term interest rates. A PhD candidate at the Sauder School of Business - Finance Division, University of British Columbia. 2008: Kristian Bauer, Statistics (Finance), Volatility prediction using stochastic volatility models with implied volatility, Analyst, Quantitative Research at Picton Mahoney Asset Management. 2008: Matthew Black, Statistics (Finance), Adaptive neuro-fuzzy inference system and post-earnings announcement drift, Senior Software Engineer at Knowledge Funds Ltd. 2008: Dong Song, Statistics (Finance), Pairs trading, Manager at Scotiabank, Credit (Derivatives) Portfolio Risk Analysis. 2009: Chun-Kai Kenny Liao, Statistics (Finance), Modeling energy commodity prices with regime-switching and jumps. 2009: Felix Qi Zhou, Statistics (Finance), On the LIBOR market model, Senior Manager (Quantitative Developer, Interest Rate Derivative and Trading Desk) at CIBC. 2009: Hongliang Wu, Statistics (Finance), Value-at-Risk forecasting models: parametric and non-parametric approaches, Financial validation Engineer at Numerix. 2009: SzeTsun Ip, Statistics (Finance), Smoothing of implied volatility surface and application on volatility derivatives. 2009: Tao Chen, Statistics (Finance), Credit default swap valuation with counterparty default risk, Senior Analyst at Bank of Montreal. 2009: Yu-Li Chu, Statistics (Finance), The valuation of currency options with jumps and stochastic volatilities, Quantitative Analyst at Bloomberg, NY. 2009: Yuan Ma, Statistics (Finance), The optimal life-time asset allocation and consumption, CiticBank ( Zhongxin Bank), Shanghai, China. 2009: Xiaoyu Chu, Statistics (Finance), The pricing of Asian option with weighted variance reduction, Citic Jintong Securities, Shanghai, China . 2009: Li Xiao, Statistics (Finance), Specific risk modeling, Portfolio Analyst at CPPPIB.

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2010: Zhao Lu, MQF, Pricing interest rate swap under counterparty risk. 2010: Xiaoyu Ouyang, MQF, Constant Proportion Debt Obligations (CPDO), Financial Engineer at Bloomberg. 2010: Michael Yu Han, MQF, Interest rate option pricing under market models, Quantitative Risk Analyst at CIBC World Markets. 2010: Li Li, MQF, Copulas and their Applications to credit risk models, Manager (Model Vettter), at Scotia Capital. 2010: Garry Guangrui Li, MQF, A review of variance reduction techniques for a Monte Carlo method, Associate, Financial Engineer at Scotia capital. 2010: Yunuan Zhang, MQF, Comparison of Interest-Rate Derivative Pricing under LIBOR Market Model and Hull and White model. 2010. Yiqi Wang, MQF, Counterparty Credit Risk Measured by a Credit Valuation Adjustment, Financial Engineering Specialist at PwC. 2010: Josh A. Mayer, MQF, the Hurst Exponent: Applications in Finance, Senior Portfolio Analyst at CPP Investment Board. 2010: Hailong Liu, MQF, Applications of Important Sampling in Portfolio Credit Risk Models and Copula Calibrations, Senior Analyst, Model Risk & Vetting at Bank of Montreal. 2011: Oluwarotimi Akeredolu, MQF, Pricing American Option under Kous Double Exponential Jump Diffusion, Liquidity Risk Specialist at RBC. 2011: Brendon Freeman, MQF, The price of Immediacy: Applications and Extensions, Analyst at Canada Pension Plan Investment Board. 2011: Khary Redwood, Actuarial Science, A Comparison of Non-Parametric and Semi-Parametric Methods for Diffusion Function Estimation of Short-Term Interest Rates, Enterprise Risk - Quantitative Risk Analyst at ING DIRECT Canada. 2012: Zhiyong Zhang, MQF, Vasicek Interest Model, Option pricing and Variance Reduction Techniques, Senior Specialist (Model Risk Vetting) at Bank of Montreal. 2012: Machel Forde, MQF, The Calibration of Log-normal Mixture Dynamics and Option Pricing Trading Products, Associate at BMO Capital Markets. 2012 : Lei Wan, MQF, Portfolio Selection with Higher Moments, Manager (Model Validation and Approval) at Scotiabank. 2012: Dan Xu, MQF, Bilateral Counterparty Credit Risk Valuation Model, Risk Specialist at Bank of Montreal. 2012: Wenjun Cai, MQF, Pricing of Correlation-Dependent Credit Derivatives Using a Structural Model Associate (Model Vetting Group) at CIBC. 2012: Tingting Gu, MQF, Forecasting Equity Return with Mixture of Normal Distributions under Structural Breaks, Associate (Mortgage Trading, Office of the Chief Investment Officer )at CIBC. 2012: Yuxi Wang, MQF, Monte Carlo Valuation of American Options, Risk Rotation at BMO. 2012: Bing Liu, MQF, On Monte Carlo Simulation for Pricing American Options using Least Squares Method. Manager (Model Validation and Approval) at Scotiabank. 2012: Lichen Chen, Statistics, Bayesian Estimation of the MN(2)-GARCH(1,1) Model. 2013: Matthew Gilbert, MQF, An Analysis of Risk Arbitrage Probabilities. 2013: Xin Lei (Lauren) Fu, MQF, Mean-Variance Analysis of Self-Financing Portfolios.

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2013: Siyuan Sheng, MQF, The Vanna-Volga Method and Its Applications to FX Market, Model Risk Specialist at BMO Model risk and vetting(MRV). 2013: Michael Lam, MQF, Assessing the Performance of the Method of Maximum Likelihood in Estimating Corporate Bond Prices, Senior Analyst (Market Risk) at BMO . 2013: Sangsang Tu, MQF, Critical Assessment of the ASRF Model. 2013: Volodymyr Lozynskyy, MQF, Pricing and Hedging Arithmetic Average Asian Options, Senior Analyst at RBC. 2013: Lunzhi Cai, MQF, Assessing the Properties of some Coherent Risk Measures, Business Analyst, Product Validation Control Team, Market Risk at BMO. 2013: Qianyi (Emma) Zhang, Actuarial Science, A Study of Systemic Risks in the Financial and Insurance Sectors. 2013: Saad Ali Khan, Statistics, Financial Volatility and Long Memory/Fractional Integrated Processes. Note: MQF= Master in Quantitative Finance, Dean of Mathematics Office. OTHER GRADUATE SUPERVISION ACTIVITIES (AS MAIN SUPERVISOR) Department of Economics 1992 1. David P. Dallaire, Modeling the aggregate agricultural supply sector of Canada, using a nested CES production function. 2. Shannon D. Sanford, Unemployment in Canada and selected provinces: simultaneous modelling of the sectoral shifts and aggregate demand hypothesis. 1993 3. Jennifer Howard, Excess volatility of long term interest rates. 4. Andrew J. Hughes, Testing the long-run purchasing power parity hypothesis: Canada-U.S. 5. Jan Willems, An essay on credit markets. 6. Pascal Renaud, Empirical analysis on interest rates on savings deposits in Canada. 1994 7. R. Anandhi, An empirical investigation into government spending and private-sector behavior. 1995 8. Rupinder Sandhu, Warrantys role in the automobile industry. 9. Charleen Adam, The effects of credit market imperfections on investment spending. 10. Seymour A. Rowe, On the real exchange rate variability in selected LDC countries. 11. Geoff Bowlby, The economics of teacher supply in Canada and Ontario. 12. Michael M. Kruchten, Conditional demand estimates for natural gas appliances. 1996 13. Angela Spiro, Forecasting motor vehicle fatalities, injuries and acc