Financial Econometrics and Statistics: Financial Econometrics and Statistics: Past, Present, and FuturePast, Present, and Future

ByBy

Dr. Cheng-Few LeeDr. Cheng-Few Lee

Distinguished Professor of Finance, Rutgers University, USA Distinguished Professor of Finance, Rutgers University, USA

Editor, Review of Quantitative Finance and AccountingEditor, Review of Quantitative Finance and Accounting

Editor, Review of Pacific Basin Financial Markets and PoliciesEditor, Review of Pacific Basin Financial Markets and Policies

To be presented at the “The 4th NCTU International Finance Conference ” on January 7, 2011.

Outline Outline 1.1. Introduction Introduction2.2. Single equation regression methods Single equation regression methods3.3. Simultaneous equation models Simultaneous equation models4.4. Panel data analysis Panel data analysis5.5. Alternative methods to deal with measurement error Alternative methods to deal with measurement error6.6. Time series analysis Time series analysis7.7. Spectral Analysis Spectral Analysis8.8. Statistical distributions Statistical distributions9.9. Principle components and factor analyses Principle components and factor analyses10. Non-parametric, Semi-parametric, and GMM analyses10. Non-parametric, Semi-parametric, and GMM analyses11. Path analysis11. Path analysis12. Cluster analysis12. Cluster analysis13. Summary and concluding remarks13. Summary and concluding remarks

1.1. IntroductionIntroduction Financial econometrics and statistics have become more important for Financial econometrics and statistics have become more important for

empirical research in both finance and accounting. Asset pricing and corporate empirical research in both finance and accounting. Asset pricing and corporate finance research have used both econometrics and statistics, such as single finance research have used both econometrics and statistics, such as single equation multiple regression, simultaneous regression, panel data analysis. equation multiple regression, simultaneous regression, panel data analysis. Portfolio theory and management have used different statistics distributions, Portfolio theory and management have used different statistics distributions, such as normal distribution, stable distribution, and log normal distribution. such as normal distribution, stable distribution, and log normal distribution. Options and futures have used binomial distribution, log normal distribution, Options and futures have used binomial distribution, log normal distribution, non-central chi square distribution, and so on. Auditing has used sampling non-central chi square distribution, and so on. Auditing has used sampling technique to determine the sampling error for auditing. The main purpose of technique to determine the sampling error for auditing. The main purpose of this handbook is to review financial econometrics and statistics used in the this handbook is to review financial econometrics and statistics used in the research of finance and accounting for last five decades. Some suggestions to research of finance and accounting for last five decades. Some suggestions to apply these techniques in future research are also recommended.apply these techniques in future research are also recommended.

The second section of this paper will discuss alternative single equation The second section of this paper will discuss alternative single equation regression estimation methods. Section 3 will discuss simultaneous equation regression estimation methods. Section 3 will discuss simultaneous equation models. Section 4 will discuss panel data analysis. Section 5 will discuss models. Section 4 will discuss panel data analysis. Section 5 will discuss alternative methods to deal with measurement error. Section 6 will discuss time alternative methods to deal with measurement error. Section 6 will discuss time series analysis. Section 7 will discuss spectral Analysis. Section 8 will discuss series analysis. Section 7 will discuss spectral Analysis. Section 8 will discuss statistical distribution. Section 9 will discuss principle components and factor statistical distribution. Section 9 will discuss principle components and factor analyses. Section 10 will discuss non-parametric, semi-parametric, and GMM analyses. Section 10 will discuss non-parametric, semi-parametric, and GMM analyses. Section 11 will discuss path analysis. Section 12 will discuss cluster analyses. Section 11 will discuss path analysis. Section 12 will discuss cluster analysis. Finally, section 13 will summarize the paper.analysis. Finally, section 13 will summarize the paper.

2.2. Single equation regression methodsSingle equation regression methodsIn this section, we will discuss important issues related to single equation rIn this section, we will discuss important issues related to single equation r

egression estimation method. They are (a) heteroskedasticity, (b) specifegression estimation method. They are (a) heteroskedasticity, (b) specification error, (c) measurement error, (d) quantile regression, and (e) tesication error, (c) measurement error, (d) quantile regression, and (e) testing structural change.ting structural change.

a.a. HeteroskedasticityHeteroskedasticity-- White methodWhite method-- Newey-West methodNewey-West method

b.b. Specification errorSpecification error-- Thursby, JASA (1985)Thursby, JASA (1985)-- “Alternative Specifications and Estimation Methods for Determining Ran“Alternative Specifications and Estimation Methods for Determining Ran

dom Beta Coefficients: Comparison and Extensions,” (with Robert C.R. dom Beta Coefficients: Comparison and Extensions,” (with Robert C.R. Rkok and David C. Cheng), Journal of Financial Studies, October 1996Rkok and David C. Cheng), Journal of Financial Studies, October 1996

-- “Power of Alternative Specification Errors Tests in Identifying Misspecifi“Power of Alternative Specification Errors Tests in Identifying Misspecified Market Models,” (with David C. Cheng), The Quarterly Review of Eced Market Models,” (with David C. Cheng), The Quarterly Review of Economics and Business, Fall, 1986.onomics and Business, Fall, 1986.

-- Cheng and Lee, QREB (1986)Cheng and Lee, QREB (1986)-- Maddala et al., Handbook of Statistics 14: Statistics Methods in Finance Maddala et al., Handbook of Statistics 14: Statistics Methods in Finance

(1996)(1996)

2.2. Single equation regression methodsSingle equation regression methodsc.c. Measurement errorMeasurement error-- Lee and Jen, JFQA (1978)Lee and Jen, JFQA (1978)-- Kim, JF (1995)Kim, JF (1995)-- Kim, Handbook of Quantitative Finance and Risk Management (2010)Kim, Handbook of Quantitative Finance and Risk Management (2010)-- Miller and Modigliani, AER (1966)Miller and Modigliani, AER (1966)

d.d. Quantile regressionQuantile regression

e.e. Nonlinear regressionNonlinear regressionBox-Cox transformationBox-Cox transformation-- Lee JF (1976)Lee JF (1976)-- Lee JFQA (1977)Lee JFQA (1977)-- Lee JFQA ()Lee JFQA ()-- “Generalized Financial Ratio Adjustment Processes and Their Implications,” (with “Generalized Financial Ratio Adjustment Processes and Their Implications,” (with

Thomas J. Frecka), Journal of Accounting Research, Spring, 1983.Thomas J. Frecka), Journal of Accounting Research, Spring, 1983.-- “A Generalized Functional Form Approach to Investigate the Density Gradient an“A Generalized Functional Form Approach to Investigate the Density Gradient an

d the Price Elasticity of Demand for Housing,” (with James B. Kau), Urban Studied the Price Elasticity of Demand for Housing,” (with James B. Kau), Urban Studies, April, 1976.s, April, 1976.

-- Liu (2005)Liu (2005)-- Kau, Lee, and Sirmans. Urban Econometrics: Model developments and empirical Kau, Lee, and Sirmans. Urban Econometrics: Model developments and empirical

results (1986)results (1986)

2.2. Single equation regression methodsSingle equation regression methodsf.f. Testing structural changeTesting structural change-- Yang (1989)Yang (1989)-- Lee et al. (2010) Optimal payout ratio under …Lee et al. (2010) Optimal payout ratio under …-- Lee et al. (2010) Threshold..Lee et al. (2010) Threshold..-- Chow test and moving chow testChow test and moving chow test(Chow, Econometrica, 1960)(Chow, Econometrica, 1960)(Strucchange: An R Package for Testing for Structural Change in Lineaer Regression Models, J(Strucchange: An R Package for Testing for Structural Change in Lineaer Regression Models, J

ournal of Statistical Software, 2002)ournal of Statistical Software, 2002)-- Threshold regressionThreshold regression(Hansen, Journal of Business & Economic Statistics, 1997)(Hansen, Journal of Business & Economic Statistics, 1997)(Hansen, Econometrica, 1996, 2000)(Hansen, Econometrica, 1996, 2000)(Journal of Econometrics, 1999, 2000). (Journal of Econometrics, 1999, 2000). -- Generalize fluctuation testGeneralize fluctuation test(Juan and Hornik, Eonometric Reviews, 1995)(Juan and Hornik, Eonometric Reviews, 1995)

g.g. Probit and Logit regression for credit risk analysisProbit and Logit regression for credit risk analysis-- Hwang, R.C.*, Cheng, K.F., and Lee, C.F. (2009). On multiple-class prediction of issuer creHwang, R.C.*, Cheng, K.F., and Lee, C.F. (2009). On multiple-class prediction of issuer cre

diting ratings. Journal of Applied Stochastic Models in Business and Industry, 25, 535-550. diting ratings. Journal of Applied Stochastic Models in Business and Industry, 25, 535-550. (SCI)(SCI)

-- Hwang, R.C.*, Wei, H.C., Lee, J.C., and Lee, C.F. (2008). On prediction of financial distress Hwang, R.C.*, Wei, H.C., Lee, J.C., and Lee, C.F. (2008). On prediction of financial distress using the discrete-time survival model. Journal of Financial Studies, 16, 99-129. (TSSCI)using the discrete-time survival model. Journal of Financial Studies, 16, 99-129. (TSSCI)

-- Cheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using the discrete-Cheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using the discrete-time semiparametric hazard model. Accepted by Quantitative Finance. (SSCI)time semiparametric hazard model. Accepted by Quantitative Finance. (SSCI)

3.3. Simultaneous equation modelsSimultaneous equation models In this section, we will discuss alternative methods to deal with simultaneous equation In this section, we will discuss alternative methods to deal with simultaneous equation

models. There are (a) 2 stage least square (2SLS) method, (b) seemly uncorrelated models. There are (a) 2 stage least square (2SLS) method, (b) seemly uncorrelated regression (SUR) method, (c) 3 stage least square (3SLS) method, and (d) regression (SUR) method, (c) 3 stage least square (3SLS) method, and (d) disequilibrium estimation method.disequilibrium estimation method.

a.a. 2 stage least square (2SLS) method2 stage least square (2SLS) method-- Lee JFQA (1976)Lee JFQA (1976)-- M&M AER (1966)M&M AER (1966)-- Chen et al., Corporate Governance and International Review (2007)Chen et al., Corporate Governance and International Review (2007)

b.b. Seemly uncorrelated regression (SUR) methodSeemly uncorrelated regression (SUR) method-- Lee JFQA (1981)Lee JFQA (1981)

c.c. 3 stage least square (3SLS) method3 stage least square (3SLS) method-- Chen et al., Corporate Governance and International Review (2007)Chen et al., Corporate Governance and International Review (2007)

d.d. Disequilibrium estimation methodDisequilibrium estimation method-- Tsai (2005)Tsai (2005)-- CW Sealy JF (1979)CW Sealy JF (1979)-- Lee, Tsai, and Lee, subjected to revision for Quantitative Finance (2010)Lee, Tsai, and Lee, subjected to revision for Quantitative Finance (2010)-- WJ Mayer, Journal of Econometrics, 1989WJ Mayer, Journal of Econometrics, 1989-- RW David, JBF, 1987RW David, JBF, 1987-- C Martin, Review of Economics and Statistics, 1990C Martin, Review of Economics and Statistics, 1990

4.4. Panel data analysisPanel data analysis In this section, we will discuss important issues related to panel data anaIn this section, we will discuss important issues related to panel data ana

lysis. There are (a) fixed effect model, (b) random effect model, and (c) clysis. There are (a) fixed effect model, (b) random effect model, and (c) clustering effect model.lustering effect model.

- Wooldridge, Econometric Analysis of Cross Secion and Panel Data, MIT P- Wooldridge, Econometric Analysis of Cross Secion and Panel Data, MIT Press (2002)ress (2002)

- BalTagi, Econometric Analysis of Panel Data, Wiley (2008)- BalTagi, Econometric Analysis of Panel Data, Wiley (2008)- Hsiao, Analysis of Panel Data, Cambridge University Press (2002)- Hsiao, Analysis of Panel Data, Cambridge University Press (2002)

a.a. Fixed effect modelFixed effect model-- Lee JFQA (1977)Lee JFQA (1977)-- Lee et al. JCF (2010)Lee et al. JCF (2010)

b.b. Random effect modelRandom effect model-- Lee JFQA (1977)Lee JFQA (1977)

c.c. Clustering effect model of panel data analysisClustering effect model of panel data analysis-- Thompson (2006)Thompson (2006)-- Cameron, Gelbach, and Miller (2006)Cameron, Gelbach, and Miller (2006)-- Petersen (2009)Petersen (2009)

5.5. Alternative methods to deal with Alternative methods to deal with measurement errormeasurement error

In this section, we will discuss Alternative methods to deal with measuremenIn this section, we will discuss Alternative methods to deal with measurement error problem. They are (a) LISREL model, (b) multi-factor and multi-indt error problem. They are (a) LISREL model, (b) multi-factor and multi-indicator (MIMIC) model, and (c) partial least square method.icator (MIMIC) model, and (c) partial least square method.

-- Lee (1973)Lee (1973)a.a. LISREL modelLISREL model-- Titman and Wessal JF (1988)Titman and Wessal JF (1988)-- Chang (1999)Chang (1999)-- Chang and Lee QREF (2008)?Chang and Lee QREF (2008)?

b.b. Multi-factor and multi-indicator (MIMIC) modelMulti-factor and multi-indicator (MIMIC) model-- Lee et al. QREB (2009)Lee et al. QREB (2009)-- Wei (1984)Wei (1984)

c.c. Partial least square methodPartial least square method-- JE Core - Journal of Law, Economics, and Organization (2000)JE Core - Journal of Law, Economics, and Organization (2000)-- Ittner et al. AR (1997)Ittner et al. AR (1997)-- Lambert and Lacker ()Lambert and Lacker ()

6.6. Time series analysisTime series analysis In this section, we will discuss important models in time series analysis. They are (a) In this section, we will discuss important models in time series analysis. They are (a)

ARIMA, (b) ARCH, (c) GARCH, and (d) Fractional GARCH.ARIMA, (b) ARCH, (c) GARCH, and (d) Fractional GARCH.-- Anderson, T. W., The statistical Analysis of Time Series (1994), Wiley-Interscience.Anderson, T. W., The statistical Analysis of Time Series (1994), Wiley-Interscience.-- Hamilton, J. D., Time Series Analysis (1994), Princeton University Press.Hamilton, J. D., Time Series Analysis (1994), Princeton University Press.

a.a. ARIMAARIMA-- Myers, JFM (1991)Myers, JFM (1991)

b.b. ARCHARCH-- Lien and Shrestha, JFM (2007)Lien and Shrestha, JFM (2007)

c.c. GARCHGARCH-- Lien, JFM (2010)Lien, JFM (2010)

d.d. Fractional GARCHFractional GARCH-- Leon and Vaello-Sebastia, JBF (2009)Leon and Vaello-Sebastia, JBF (2009)

e.e. Combined forecastingCombined forecasting-- Lee (1996)Lee (1996)-- Lee and Cummins (1998)Lee and Cummins (1998)

7.7. Spectral AnalysisSpectral Analysis

In this section, we will discuss the spectral In this section, we will discuss the spectral analysis.analysis.

-- Chacko and Viceira, Journal of EconometriChacko and Viceira, Journal of Econometrics (2003)cs (2003)

-- Heston, RFS (1993)Heston, RFS (1993)

-- Anderson, T. W., The statistical Analysis oAnderson, T. W., The statistical Analysis of Time Series (1994)f Time Series (1994)

8.8. Statistical distributionsStatistical distributions

In this section, we will discuss different statistical distributions. They are (a) binomial In this section, we will discuss different statistical distributions. They are (a) binomial distribution, (b) poisson distribution, (c) normal distribution, (d) log normal distribudistribution, (b) poisson distribution, (c) normal distribution, (d) log normal distribution, (e) Chi-square distribution, (f) non-central Chi-square distribution, (g) Wishation, (e) Chi-square distribution, (f) non-central Chi-square distribution, (g) Wishart distribution, (h) stable distribution, and (i) other distributions.rt distribution, (h) stable distribution, and (i) other distributions.

a.a. Binomial distributionBinomial distribution-- Cox, Ross, and Rubinstein (1979)Cox, Ross, and Rubinstein (1979)-- Rendleman and Barter (1979)Rendleman and Barter (1979)b.b. Poisson distributionPoisson distributionc.c. Normal distributionNormal distributiond.d. Log Normal distributionLog Normal distribution-- Chu (1984)Chu (1984)e.e. Chi-square distributionChi-square distributionf.f. Non-central Chi-square distributionNon-central Chi-square distribution-- M. Schroder, Journal of Finance (1989)M. Schroder, Journal of Finance (1989)g.g. Wishart distributionWishart distribution-- Chen and Lee, Management Science (1981)Chen and Lee, Management Science (1981)h.h. Stable distributionStable distribution-- E. Fama, JASA (1971)E. Fama, JASA (1971)i.i. Other distributionsOther distributions

9.9. Principle components and factor Principle components and factor analysesanalyses

In this section, we will discuss principle comIn this section, we will discuss principle components and factor analyses.ponents and factor analyses.

-- Anderson, T. W., An Introduction to MultivAnderson, T. W., An Introduction to Multivariate Statistical Analysis (2003), Wiley-Intariate Statistical Analysis (2003), Wiley-Interscience.erscience.

a.a.Principle componentsPrinciple components

b.b.Factor analysesFactor analyses

10.10. Non-parametric, Semi-parametric, and Non-parametric, Semi-parametric, and GMM analysesGMM analyses

In this section, non-parametric, semi-paprmetric, and GMM analyses will be discussIn this section, non-parametric, semi-paprmetric, and GMM analyses will be discussed.ed.

a.a. Non-parametric analysisNon-parametric analysis-- Ait-Sahalia and Lo, Journal of Econometrics (2000)Ait-Sahalia and Lo, Journal of Econometrics (2000)

b.b. Semi-parametric analysisSemi-parametric analysis-- Hwang, R.C.*, Chung, H., andChu, C.K. (2009). Predicting issuer credit ratings uHwang, R.C.*, Chung, H., andChu, C.K. (2009). Predicting issuer credit ratings u

sing a semiparametric method. Accepted by Journal of Empirical Finance. sing a semiparametric method. Accepted by Journal of Empirical Finance. -- Cheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using tCheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using t

he discrete-time semiparametric hazard model. Accepted by Quantitative Finanche discrete-time semiparametric hazard model. Accepted by Quantitative Finance. e.

-- Hwang, R.C.*, Cheng, K.F., and Lee, J.C. (2007). A semiparametric method for pHwang, R.C.*, Cheng, K.F., and Lee, J.C. (2007). A semiparametric method for predicting bankruptcy. Journal of Forecasting, 26, 317-342. redicting bankruptcy. Journal of Forecasting, 26, 317-342.

c.c. GMM analysisGMM analysis-- Chen et al., Corporate Governance and International Review (2007)Chen et al., Corporate Governance and International Review (2007)-- Brick et al. “The Motivations for Issuing Putable Debt: An Empirical Analysis” fortBrick et al. “The Motivations for Issuing Putable Debt: An Empirical Analysis” fort

hcoming for Handbook of Quantitative Finance and Econometrics, 2011.hcoming for Handbook of Quantitative Finance and Econometrics, 2011.

11.11. Path analysisPath analysis

In this section, path analysis will be discusseIn this section, path analysis will be discussed.d.

12.12. Cluster analysisCluster analysis

In this section, Cluster analysis will be discIn this section, Cluster analysis will be discussed.ussed.

-- Brown and Goetzmann (JFE, 1997)Brown and Goetzmann (JFE, 1997)

-- Finding Groups in Data: An Introduction to Finding Groups in Data: An Introduction to Cluster Analysis, L Kaufman, Peter J RousCluster Analysis, L Kaufman, Peter J Rousseeuw, Wiley, 2005seeuw, Wiley, 2005

13.13. Summary and concluding remarksSummary and concluding remarks

In this paper, we have review both financial In this paper, we have review both financial econometrics and statistics methods which econometrics and statistics methods which has been used in finance and accounting has been used in finance and accounting research for last four decades. In this research for last four decades. In this handbook, we include research papers in handbook, we include research papers in both finance and accounting which present both finance and accounting which present different methodologies in detailed. different methodologies in detailed. Therefore, it will be very useful to Therefore, it will be very useful to researcher when they try to perform similar researcher when they try to perform similar kind of research.kind of research.

ReferencesReferences

Chang, C. F., 1999. “Determinants of capital structure and management compensation: the partial least Chang, C. F., 1999. “Determinants of capital structure and management compensation: the partial least squares approach,” Ph.D. Dissertation, Rutgers University. squares approach,” Ph.D. Dissertation, Rutgers University.

Cheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using the discrete-time semipCheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using the discrete-time semiparametric hazard model. Accepted by Quantitative Finance. (SSCI) arametric hazard model. Accepted by Quantitative Finance. (SSCI)

Chu, C. C., 1984. “Alternative methods for determining the expected market risk premium: theory and Chu, C. C., 1984. “Alternative methods for determining the expected market risk premium: theory and evidence,” Ph.D. Dissertation, University of Illinois at Urbana-Champaign. evidence,” Ph.D. Dissertation, University of Illinois at Urbana-Champaign.

Cox, J. C., S. A. Ross, and M. Rubinstein, 1979. “Option Pricing: a simplified approach,” Journal of FiCox, J. C., S. A. Ross, and M. Rubinstein, 1979. “Option Pricing: a simplified approach,” Journal of Financial Economics, 7, 229-263. nancial Economics, 7, 229-263.

Davis, P., 2010. “A firm-level test of the CAPM,” Working paper. Davis, P., 2010. “A firm-level test of the CAPM,” Working paper. Hwang, R.C.*, Cheng, K.F., and Lee, C.F. (2009). On multiple-class prediction of issuer crediting ratinHwang, R.C.*, Cheng, K.F., and Lee, C.F. (2009). On multiple-class prediction of issuer crediting ratin

gs. Journal of Applied Stochastic Models in Business and Industry, 25, 535-550. (SCI) gs. Journal of Applied Stochastic Models in Business and Industry, 25, 535-550. (SCI) Hwang, R.C.*, Cheng, K.F., and Lee, J.C. (2007). A semiparametric method for predicting bankruptcy. Hwang, R.C.*, Cheng, K.F., and Lee, J.C. (2007). A semiparametric method for predicting bankruptcy.

Journal of Forecasting, 26, 317-342. Journal of Forecasting, 26, 317-342. Hwang, R.C.*, Chung, H., and Chu, C.K. (2009). Predicting issuer credit ratings using a semiparametriHwang, R.C.*, Chung, H., and Chu, C.K. (2009). Predicting issuer credit ratings using a semiparametri

c method. Accepted by Journal of Empirical Finance. (SSCI) c method. Accepted by Journal of Empirical Finance. (SSCI) Hwang, R.C.*, Wei, H.C., Lee, J.C., and Lee, C.F. (2008). On prediction of financial distress using the Hwang, R.C.*, Wei, H.C., Lee, J.C., and Lee, C.F. (2008). On prediction of financial distress using the

discrete-time survival model. Journal of Financial Studies, 16, 99-129. (TSSCI) discrete-time survival model. Journal of Financial Studies, 16, 99-129. (TSSCI) Ittner, C. D., Larcker, D. F., and Rajan, M. V., 1997, “The choice of performance measure in annual boIttner, C. D., Larcker, D. F., and Rajan, M. V., 1997, “The choice of performance measure in annual bo

nus contracts,” Accounting Review 72, 231-255. nus contracts,” Accounting Review 72, 231-255. JE Core - Journal of Law, Economics, and Organization, 2000 “The directors' and officers' insurance prJE Core - Journal of Law, Economics, and Organization, 2000 “The directors' and officers' insurance pr

emium: an outside assessment of the quality of corporate governance” emium: an outside assessment of the quality of corporate governance” Kim, D., 1997. “A reexamination of firm size, book-to-market, and earnings price in the cross-section oKim, D., 1997. “A reexamination of firm size, book-to-market, and earnings price in the cross-section o

f expected stock returns,” Journal of Financial and Quantitative Analysis, 32(4), 463-489. f expected stock returns,” Journal of Financial and Quantitative Analysis, 32(4), 463-489. Kim, D., 2010. “Issues related to the errors-in-variables problems in asset pricing tests,” Handbook of Kim, D., 2010. “Issues related to the errors-in-variables problems in asset pricing tests,” Handbook of

Quantitative Finance and Risk Management. Quantitative Finance and Risk Management.

ReferencesReferencesLee, A. C. and J. D. Cummins (1998), “Alternative models for estimating the cost of capital for propertLee, A. C. and J. D. Cummins (1998), “Alternative models for estimating the cost of capital for propert

y/casualty insurers,” Review of Quantitative Finance and Accounting, 10(3), 235-267. y/casualty insurers,” Review of Quantitative Finance and Accounting, 10(3), 235-267. Lee, A., 1996. “Cost of capital and equity offerings in the insurance industry,” Ph.D. Dissertation, The Lee, A., 1996. “Cost of capital and equity offerings in the insurance industry,” Ph.D. Dissertation, The

University of Pennsylvania in Partial. University of Pennsylvania in Partial. Lee, C. F. and F. C. Jen, 1978. “Effects of measurement errors on systematic risk and performance meaLee, C. F. and F. C. Jen, 1978. “Effects of measurement errors on systematic risk and performance mea

sure of a portfolio,” Journal of Financial and Quantitative Analysis, 13(2), 299-312. sure of a portfolio,” Journal of Financial and Quantitative Analysis, 13(2), 299-312. Lee, C. F., 1973. “Errors-in-variables estimation procedures with applications to a capital asset pricing Lee, C. F., 1973. “Errors-in-variables estimation procedures with applications to a capital asset pricing

model,” Ph.D. Dissertation, The State University of New York at Buffalo, 1973. model,” Ph.D. Dissertation, The State University of New York at Buffalo, 1973. Liu, B., 2006. “Two essays in financial economics: I: Functional forms and pricing of country funds. II: Liu, B., 2006. “Two essays in financial economics: I: Functional forms and pricing of country funds. II:

The term structure model of inflation risk premia,” Ph.D. Dissertation, Rutgers University. The term structure model of inflation risk premia,” Ph.D. Dissertation, Rutgers University. Maddala, G. S., C. R. Rao, and G. S. Maddala, (1996) “Handbook of Statistics 14: Statistical Methods iMaddala, G. S., C. R. Rao, and G. S. Maddala, (1996) “Handbook of Statistics 14: Statistical Methods i

n Finance,” Elsevier Science & Technology. n Finance,” Elsevier Science & Technology. Miller, M. H. and F. Modigliani, 1966. “Some estimates of the cost of capital to the electric utility indusMiller, M. H. and F. Modigliani, 1966. “Some estimates of the cost of capital to the electric utility indus

try,” American Economic Review, 56(3), 333-391. try,” American Economic Review, 56(3), 333-391. Rendleman, R. J., Jr. and B. J. Barter, 1979. “Two-state option pricing,” Journal of Finance, 24, 1093-1Rendleman, R. J., Jr. and B. J. Barter, 1979. “Two-state option pricing,” Journal of Finance, 24, 1093-1

110. 110. Rubinstein, M., 1994. “Implied binomial trees,” Journal of Finance, 49, 771-818. Rubinstein, M., 1994. “Implied binomial trees,” Journal of Finance, 49, 771-818. Thursby, J. G., 1985. “The relationship among the specification error tests of Hausman, Ramsey and ChThursby, J. G., 1985. “The relationship among the specification error tests of Hausman, Ramsey and Ch

ow,” Journal of the American Statistical Association, 80(392), 926-928. ow,” Journal of the American Statistical Association, 80(392), 926-928. Tsai, G. M, 2005. “Alternative Dynamic Capital Asset Pricing Models: Theories and Empirical Results,Tsai, G. M, 2005. “Alternative Dynamic Capital Asset Pricing Models: Theories and Empirical Results,

” Ph.D. Dissertation, Rutgers. ” Ph.D. Dissertation, Rutgers. Wei, K. C., 1984. “The arbitrage pricing theory versus the generalized intertemporal capital asset pricinWei, K. C., 1984. “The arbitrage pricing theory versus the generalized intertemporal capital asset pricin

g model: theory and empirical evidence,” Ph.D. Dissertation, University of Illinois at Urbana-Chamg model: theory and empirical evidence,” Ph.D. Dissertation, University of Illinois at Urbana-Champaign. paign.

Yang, C. C., 1989. “The impact of new equity financing on firms’ investment, dividend and bebt-financYang, C. C., 1989. “The impact of new equity financing on firms’ investment, dividend and bebt-financing decisions,” Ph.D. Dissertation, The University of Illinois at Urbana-Champaign. ing decisions,” Ph.D. Dissertation, The University of Illinois at Urbana-Champaign.