Deepika Patwardhan

8/8/2019 Deepika Patwardhan

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DECLARATIONI, Deepika Patwardhan, hereby declare that this submission is my own work and any

contributions or materials by other authors used in this thesis have been appropriatelyacknowledged. This thesis has not been previously submitted to any other university orinstitution as part of the requirements for another degree or award.

DEEPIKA PATWARDHAN

26th OCTOBER 2009


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ACKNOWLEDGEMENTS

I would like to thank my supervisor Glenn Otto for his wise counsel, unwavering andmore than generous support, as well as for his insightful and critical engagement withthe research and writing of this thesis. Glenn has been a wonderful mentor, and hasdealt most graciously with all the dramas of my candidature! An enormous amount ofgratitude is also owed to Valentyn Panchenko for all his help with the econometrics andfor his ideas and suggestions for presenting my thesis results.

One friend is owed a special debt and acknowledgement. Sujatha has, more than anyoneelse, endured the quotidian trials of my honours year and on a daily basis reminded meof the humour, humility and perspective that was necessary to successfully completethis thesis. For her friendship, loyalty, most of the innumerable provocations, andunfaltering belief in my ability, I am very grateful. I would also like to thank Piyush andDevshree for helping me edit my thesis and for being constant and ever enthusiastic

companions throughout the year.Fellow honours students and staff at the School of Economics made this journey mostenjoyable! I would especially like to thank Rahul, Dave, Shasheen, Hien, Michael,Andrew, Gordon and Spiro for their help and support. Guys, your constant andundivided attention was indeed flattering!

Finally, I would like to thank my father for all his help, support and advice that made mywork a lot easier. I would like to thank my mother for her emotional support and mysister for distracting me when I needed it the most!


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TABLE OF CONTENTSDECLARATION .................................................................................................................. .................................. 2

ACKNOWLEDGEMENTS............................................................................................................................ ...... 3ABSTRACT ............................................................................................................................................................ 71. INTRODUCTION .................................................................................................................... .................... 91.1 Stock Prices and News ............................................................................................................. ...... 91.2 Macroeconomy and Stock Prices ............................................................................................ 101.3 Econometric Modelling ............................................................................................................... 12

2. LITERATURE REVIEW .......................................................................................................................... 132.1 Theoretical models ....................................................................................................................... 13

2.2 Early Research ................................................................................................................................ 142.3 Econometric Challenges ............................................................................................................. 162.4 Development of NonLinear Models ..................................................................................... 172.5 Asymmetric ARCH and GARCH models ................................................................................ 20

3. DATA ......................................................................................................... ................................................... 233.1 Index data ....................................................................................................................... .................. 233.2 Announcement dummy variables .......................................................................................... 243.3 Macroeconomic variables: ......................................................................................................... 253.4 Expectations dummy variables ............................................................................................... 284. PRELIMINARY DATA ANALYSIS ...................................................................................................... 30

4.1 Introduction ............................................................................................................. ................................. 304.2 Stylized facts .................................................................................................................................. ........... 304.3 Model diagnostics ................................................................................................................................... 315. METHODOLOGY .................................................................................................................... .................. 406. EMPIRICAL RESULTS ............................................................................................................................ 48

6.1 Impact of news announcements ............................................................................................. 486.2 Impact of news content on the stock market .................................................................... 537. CONCLUSION .......................................................................................................................... .................. 628. BIBLIOGRAPHY ..................................................................................................................... .................. 669. APPENDIX .................................................................................................................................. ................ 72

APPENDIX A .............................................................................................................................................. 72APPENDIX B .............................................................................................................................................. 72


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APPENDIX C .............................................................................................................................................. 74APPENDIX D ............................................................................................................................ .................. 77


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LIST OF TABLES AND FIGURES

Table 1 : Summary characteristics of macroeconomic indicator announcements...........25

Table 2: Definition of expectations dummy variable..........................................................28Table 3 Summary statistics of ASX200.................................................................................31Table 4 LjungBox Qstatistics for equation 1......................................................................35Table 5 Breusch Godfrey Test for Serial Correlation..........................................................36Table 6 BDS Independence Test for equation 1...................................................................37Table 7 ARCH LM TEST for equation 1.................................................................................38Table 8 Correlation between ASX200 and SP500................................................................38Table 9 Sign bias test results for GARCH1,1.....................................................................45Table 10 Mean equation estimates for equation 10............................................................50Table 11 Variance equation estimates for equation 10......................................................51

Table 12 Mean equation estimates of equation 11..............................................................55

Table 13 Estimates of the variance equation of equation 11.............................................58

Figure 1 Returns on ASX200....................................................................................................................... 23Figure 2 Return on SP500 .......................................................................................................... .................. 24Figure 3 Distribution of the Returns on ASX200 .............................................................................. 32Figure 4 Daily Squared Returns on ASX200 ........................................................................................ 33


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ABSTRACTThe research conducted in this thesis entails analysing the impact of certain

macroeconomic variables on the Australian stock market for the period 19992008.Using data from the Australian Bureau of Statistics Release Calendar, newsannouncement dates were recorded for four key economic indicators in Australia,namely, CPI, Unemployment, GDP and Retail Trade. In addition, positive news andnegative news variables were constructed to examine the impact of news content on theequity markets. The presence of asymmetry was also investigated. These models

primarily comprised of GJRGARCH 1, 3 specifications.

Key results indicate that the Australian equity markets respond significantly toinformation spillovers from the US stock markets. The impact of US stock markets wasfound to be statistically significant at 1% in both the mean as well as the variance

equations. However, it seems that Australian equity markets do not respond to purenews announcements of CPI, GDP, Retail Trade or Unemployment. None of theannouncements have a statistically significant impact on the mean return or theconditional variance of the stock market. This suggests that market participants do notrespond to the act of releasing news.

Conversely, some evidence was found for the reaction of Australian market participantsto the content of news released. Results suggest that good news about the CPI causes asignificant and negative reaction in the conditional variance of the Australian stockmarket. Good news about the CPI would suggest that inflationary pressures in the


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economy were less than those expected by market participants. This shows that marketparticipants consider inflation as a leading indicator signalling the health of theeconomy. This seems to be intuitive since future interest rate decisions are closelylinked to inflationary pressures within the economy because of the Reserve Bank ofAustralias inflation targeting regime.Results also reflect that bad news on retail trade figures cause a significant and positivereaction in the mean returns of the Australian stock market. This positive reaction wasunexpected, but is effectively explained by the dataperiod. It is possible that such a

reaction only occurred during this dataperiod because the Australian economysustained high levels of economic growth. There is also a strong presence of asymmetryin the data, with the asymmetry term being positive and significant, suggesting thatnegative shocks to the equity market cause greater volatility as opposed to positiveshocks.The significance of the content of CPI and Retail Trade news suggests that marketparticipants monitor the level of inflation and economic activity within the economy,and adjust their positions in the market accordingly. It suggests that macroeconomicvariables have an impact on aggregate stock market returns as well as on theconditional variance of the market.


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1. INTRODUCTION

The impact of news releases on financial markets is a common subject of enquiry. Inparticular, researchers have attempted to analyse the impact of certain specific news onstock markets, bond markets as well as foreign exchange and options markets. Thisresearch aims to analyse the impact of the news releases of macroeconomic variables onthe Australian stock market.1.1StockPricesandNews

Market performances are rarely entirely datadriven. Trade in todays markets isinfluenced by numerous extraneous factors. These multitudes of extraneous factorsinclude news events. It is believed that news events dominate the markets on any givenday. Furthermore, as technology reaches new heights the ease of access to details ofworldwide and local news is easier. However, the question remains, exactly what type

of news affects the financial markets of a particular economy?Stock prices are believed to reflect all available information at a particular point of timeFama E. F., 1970. Theoretical models derive the price of a stock at time t as:

1

The formula suggests that stock prices reflect the present value of discounted value ofexpected future earnings dividends, given all information available at timet The discount rate can be divided into two components namely, the riskfree rate and therisk premium. 3 primitive factors that have an important impact on stock prices aresuggested Campbell & Mei, 1993. These factors are the riskfree rate of interest,


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growth expectations this is derived from the expected rate of growth of corporateearnings and dividends, and the equity risk premium. Any news that has an impact onthe stock market must be because of the information conveyed on one of these 3primitive factors. News brings with it additional information that can be exploited bymarket participants to further understand the movement of prices, allowing them tohedge their portfolios and thus take positions in the market. This attempt atexploitation by market participants in order to increase their personal profits byreacting to certain news events makes this a very interesting topic to examine.1.2MacroeconomyandStockPricesMacroeconomic news released about some of the key indicators of the economy affectsall firms and marketparticipants. As marketwide measures, scheduled macroeconomicnews announcements may cause significant changes in the price generating processesof different assets, which can then be priced as risk factors see e.g. Flannery &

Protopapadakis, 2002. News of many different types can affect the market, forexample, the September 11 attacks triggered a stock market collapse, as did the GlobalFinancial Crisis in mid2008.Macroeconomic news often signals market participants about the level of economicactivity and inflationary pressures. Inflation is considered as an important economicindicator. An increase in inflation levels would lead to a decline in the value of the

investors assets. The level of economic activity signals the prospects of future growthand the overall level of production within an economy. It is therefore expected that anynews that can be interpreted for containing additional information on either the level ofeconomic activity or inflation will be priced by market participants.


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Intuitive expectations suggest that an increase in the level of economic activity duringan expansionary phase would lead to an increase in market volatility. Increasing activityduring an expansion could lead market participants to revise future inflationaryexpectations as well as future expectations of monetary policy in the economy.Similarly, dampening consumer demand in the same scenario would lead to an increasein the mean returns of the market, as this would possibly be considered to temporarilyrelieve future inflationary pressures in the economy. Variables such as GDP, RetailTrade, and Consumer Price Index CPI have been found to be important in previousresearch undertaken.Since macroeconomic announcements affect price generating processes, it is likely thatthey also affect the entire return distributions. Over the recent years, the impact ofmacroeconomic news announcements on financial markets has received considerableattention in the literature. The overall opinion is that asset prices and volatilities inexchange markets see e.g. Andersen & Bollerslev, 1998; bond markets see e.g.

Balduzzi, Elton, & Green, 2001 and stock markets see e.g. Becker, Finnerty, &Friedman, 1995; Jones, Lamont, & Lumsdaine, 1998; Veronesi, 1999 are affectedby macroeconomic news announcements. The general conclusion is that asset pricesand volatilities react almost instantaneously to macroeconomic news announcementswith employment and inflationary news announcements having the greatest impact.Furthermore, the studies show that GARCH Generalized Autoregressive ConditionalHeteroskedasticity or other timeseries volatilities remain high for the following fewhours and gradually decline after the news announcement see e.g. Ederington & Lee,1996.


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1.3EconometricModellingThe modelling strategy has developed significantly in the past two decades. Early

researchers used simple Ordinary Least Squares OLS methodology to examine theimpact of news on a aggregate stock returns. As econometric knowledge grew, andproblems associated with timeseries data and tests to check data for these issues weredeveloped, new models were devised to examine this impact. AutoregressiveConditional Heteroskedasticity ARCH models Engle R. F., 1982 and GeneralizedAutoregressive Conditional Heteroskedasticity GARCH models Bollerslev, 1987

were shown to be incredibly effective in modelling financial time series since they werepowerful enough to model the stylized facts of the data and Furthermore, thedevelopment of extensions to ARCH and GARCH models allowed researchers to accountfor a large variety of stylized factors such as I have used the most current and up to datemethods and models to account for certain observed features of the data. ARCH/GARCHmodels have been used widely in finance and as such are well understood and easy to

estimate. In addition to distinguishing the news announcement from the news content,this research also considers the differential impact of good and bad news.Extensive research has been carried out on the impact of news on all markets in the USand up to a certain extent for markets in Europe also. However, minimal research hasbeen carried out on the impact of news announcements on the Australian stock

markets. This thesis attempts to bridge this gap in research by conducting an empiricalanalysis of the impact of news announcements on the volatility of the Australian stockmarket ASX200 for the period 19992008.


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2. LITERATURE REVIEW

The impact of news releases and news content has been a popular subject of enquiryamongst researchers in both economics and finance. This impact has been subjected toextensive research, and has led to the formulation of contesting theories and a variety ofdifferent results. Studies have been incredibly dynamic in nature, with researchersusing a variety of different datasets, employing several different econometrictechniques and varying these over the type of markets, the time period and in different

economies.2.1Theoretical modelsNofsinger & Prucyk, 2003 provide a discussion of the different theoretical modelsused to examine the effect of anticipated news announcements, such as macroeconomicnews announcements. These models are based on different assumptions and therefore

predict different reactions. For example, Kim & Verrecchia, 1994 provide a model inwhich it is assumed that traders cannot acquire private information before theannouncement. This further causes volatility to increase after the announcement until aconsensus is reached on the outcome. In another model, Kim & Verrecchia, 1991assume that traders are able to collect private information and use this information totrade according to their opinions before the announcement. After the announcement,

price changes are caused proportionally by the unexpected part of the news. This causesvolatility to increase after the announcement until a consensus is reached on theoutcome. In another model, Kim & Verrecchia, 1991 assume that traders are able tocollect private information and use this information to trader according to theiropinions before the announcement. After the announcement, prices changes are caused


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proportionally by the unexpected part of the news. In one additional model, theyassume that the traders collect private information and that the information on thenews is highly anticipated. They suggest that the variance declines as the quality ofannouncement increases.Closely related to Kim and Verrecchias research, Ederington & Lee, 1996 derivedtheir hypothesis based on a model in which it was assumed that investors gatheredprivate information, but there was still some uncertainty before the announcement.Their empirical results on the options markets show that the implied volatilities

increase before and decrease after the announcement as the uncertainty is resolved bythe market participants. This finding is consistent with the increase of realized volatilityafter the news announcement, as Ederington & Lee, 1996 show.2.2Early ResearchThe earliest research in the arena of the impact of news on financial markets has

concentrated on analysing the impact of macroeconomic variables on the mean returnsof bond markets, stock markets as well as foreign exchange markets.For example, Pearce & Roley, 1985 used a model such that a change in stock priceswas hypothesized to be dependent on unexpected news announcements and onanticipated news announcements based on information known as of close on theprevious trading day. They hypothesized the model:

. . .

Where is the change in stock prices from the closing of trading on day t1 to theclose of trading day t in percent; 1 X k vectors of unanticipated components of


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economic data announcements, computed as the difference between announcedvalues,, and expected values, ; , 1 X k vector of expected announced values ofeconomic data based on information known as of close of trading day on day t1; 1 X k vector of surprises.Pearce & Roley, 1985 used the model above to assess the impact of newsannouncements. Additionally, they also tested the Efficient Markets Hypothesis byassessing the impact of anticipated news versus news surprises on stock prices. TheEfficient Market Hypothesis as envisioned by Fama E. F., 1970 suggested that if

markets were efficient, any information that could be used by market participants topredict stock prices would be immediately incorporated into the stock prices. Ifinvestors could predict stock prices, then they would reap endless benefits bypurchasing stocks that were predicted to increase in price and by selling those thatwere predicted to fall. Pearce and Roley suggested that if the markets were efficientthen the anticipated component of information presented by economic data would be

incorporated into the price and only news surprises would have an impact on theaggregate returns. Their empirical results suggested that the anticipated components ofeconomic announcements did not significantly affect daily stock prices, therebyproviding evidence for the efficient markets hypothesis.Chen, Roll, & Ross, 1986 used a four factor model to explore a set of economic state

variables as systematic influences on stock market returns and examined their influenceon asset prices using a similar model. They found that industrial production, changes inthe risk premium, and twists in the yield curve to be significant variables in explainingreturns. They also discuss that the changes in expected inflation and unanticipated


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inflation were found to be marginally significant during periods when these variableswere highly volatile.

Other studies found that aggregate stock returns were negatively related to inflationand money growth Bodie, 1976, Fama E. F., 1981, Geske & Roll, 1983. Evidencefrom these studies suggests early researchers found increases in inflation to lead to adecrease in the average return on the market considered. Subsequently, otherresearchers Chan, Chen, & Hsieh, 1985, Chen, Roll, & Ross, 1986; and Ferson &Harvey, 1993 attempted to identify other macroeconomic variables that were possibly

related to aggregate stock returns. Cutler, Poterba, & Summers, 1989 also found thatindustrial production growth was significantly positively correlated with real stockreturns over the period 19261986. This is a major result, and may also indicate theinterplay of expectations and news variables. However, Cutler, Poterba, & Summers,1989were not able to find any evidence for inflation, money supply and the long terminterest rate argument.2.3Econometric ChallengesA common theme amongst the earliest econometric approaches was the use of linear,structural models to develop an analysis of the impact of macroeconomic variables onstock returns. However, as the understanding of the nature of timeseries data grew, theproblems associated with such linear and structural models became more obvious.

Perhaps because they focused on a linear, timeinvariant relationship between theaggregate returns and macroeconomic variables, early studies found it difficult to showthis impact empirically. Shanken & Weinstein, 1990 showed that Chen, Roll, & Ross,1986 results were dependent on the econometric methodology they used to test


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portfolios and the statistical importance of the macroeconomic variables woulddecrease when their standard error estimates are corrected for errorsinvariables.

Mcqueen & Roley, 1993 claimed that the reason why macroeconomic factors seemedto have an insignificant effect was due to the constant coefficient models used ingeneral. They argue that the use of models which assume investor response to news isthe same over different stages of the business cycle is limiting. They estimated a modelthat allowed investor expectations to vary according to the stage of the business cycle.In their model, they allowed their macroeconomic variables to depend on overall

economic conditions which they defined according to the monthly growth of industrialproduction.2.4Development of NonLinear ModelsParallel to the development of this literature, a large amount of research was beingcarried out into defining and modelling empirical properties observed in time series

data. Tests for non linearity as well as models to incorporate non linearity weredeveloped subsequently. One of the major assumptions of ordinary least squares OLSmethodology was the assumption of constant variance over time, known ashomoskedasticity. The violation of this assumption made the standard ttests and Ftests applied during inference procedures to be invalid. Time series analysis howeverwas subject to changing variances in variables over time.

Engle R. F., 1982 formulated the Auto Regressive Conditional HeteroskedasticityARCH model such that the error term in an equation could be given a structure. Thismodel prescribed that the conditional variance of the error term to be dependent on theimmediately preceding value of the squared error. Since the introduction of the ARCH


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model, several papers thus began using such autoregressive processes to model dailyinformation events see for example, Pagan & Schwert, 1990 and Andersen T. G.,1996Episodes of volatility are generally characterized as the clustering of large shocks to thedependent variable. The conditional variance function is formulated to mimic thisphenomenon. In the ARCH regression model, the variance of the current error ,conditional on the realized values of lagged errors 1,2,3 is anincreasing function of the magnitude of the lagged errors, irrespective of their signs.

Hence, large errors of either sign tend to be followed by a large error of either sign. Andsimilarly, small errors of either sign tend to be followed by a small error of either sign.The order of lag q determines the length of time for which a shock persists inconditioning the variance of subsequent errors.In the first empirical observations of ARCH to the relationship between the level and thevolatility of inflation, Engle R. F., 1982found that a large q was required in theconditional variance function. This would necessitate estimating a large number ofparameters subject to inequality restrictions. In order to overcome this weakness,Bollerslev, 1987proposed an extension of the conditional variance function, known asthe Generalized Autoregressive Conditional Heteroskedasticity GARCH. In their mostgeneral form, univariate GARCH models make the conditional variance at time a

function of exogenous and lagged endogenous variables, past residuals and conditionalvariances, time, parameters. GARCH has proven to be extremely useful in empiricalwork. The GARCH model allows the conditional variance to be dependent upon previousown lags. The conditional variance is the oneperiod ahead estimate for the variancecalculated based on any past information thought relevant.


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Studies show that ARCH, GARCH or other timeseries volatilities are higher onimportant news announcement days see e.g. Bollerslev et al., 2000; Ederington &Lee, 1993, 1995; Flannery& Protopapadakis, 2002; Jones, Lamont & Lumsdaine,1998, Kim, McKenzie and Faff, 2004. Hamilton & Susmel, 1994 also estimateGARCH models of monthly US equity returns. They used macroeconomic conditions asthe indicator to switch from highvolatility to lowvolatility regimes. They conclude thatmacroeconomic conditions significantly affect equity returns. Flannery &Protopapadakis, 2002looked at seventeen macroeconomic variables and found that sixvariables could be priced by markets. Estimating a GARCH model of daily equity returnsand allowed the realized returns and their conditional volatility to depend on thesemacroeconomic variables. In their specification tests, they replicate and expand theanalysis of Mcqueen & Roley, 1993defining alternative economic regimes; and theyalso added explanatory variables to their original specification.Similar models have been adopted by economists to study the Australian market.

Particularly, Kim & In, 2002 investigate the impact of movements in the US, UK andJapanese stock markets on the Australian stock market. They also investigate the impactof US macroeconomic news and Australian macroeconomic news on the Australianstock market. Their results are in line with expectations with some US and a fewAustralian macroeconomic news announcements having an impact on the first andsecond moments of the Australian equity markets. They also document a significantimpact of the movements of the US, UK and Japanese stock markets on the Australianstock market.


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2.5Asymmetric ARCH and GARCH modelsTimevarying models such as ARCH and GARCH were extremely effective in modeling

the timevarying volatility seen in asset returns. However, neither of these models wasable to model asymmetry. A key feature of scheduled news arrivals however, is that themarket and the people that participate in it formulate expectations about theupcoming scheduled information release. Traders take positions in the markets basedon their expectations of future events, thus the anticipated estimate for the upcomingscheduled news announcement is significant in determining the reaction of the market.

Thus, it is possible to expect that the act of releasing information might not beconsidered important by market participants, rather the market participants mightactually react to the content of news. News thus can be labeled as good or bad,depending on these expectations.This asymmetry was modeled by Barberis, Shleifer, & Vishny, 1998in a behavioral

model. They assumed that good news is expected to be followed by good news and viceversa. This indicates that high low volatility will follow after bad good news.Behavioral models formulated by Shefrin & Statman, 1985and Hong & Stein,1999suggested that investors trade and react differently after good vs. bad newsannouncements. These two models suggest that investors may trade after good news,but not after bad news and that the reaction is slower in the case of a bad news

announcement.Many researchers have found evidence for the presence of asymmetry in stock returndata. Black, 1976, Christie, 1982, Nelson, 1991, Pagan & Schwert, 1990,Sentana, 1995, and Engle & Ng, 1993all find evidence to suggest that a negativeshock to stock returns would cause much more volatility compared to a positive shock


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of equal magnitude. There are two contesting theories about the cause of this observedasymmetry in financial markets. The first major theory is the leverage effect. Theleverage effect explains the asymmetry by suggesting that when the price of an assetdecreases, the financial leverage of the asset increases as does the probability ofbankruptcy. This causes the asset to become riskier, hence leading to an increase in thevolatility of the market. This leverage effect was hypothesized by Black, 1976 andChristie, 1982. Although the leverage effect was mainly applied to firms, this effect hasalso been applied to stock market indices. The second explanation for asymmetry isknown as the volatility feedback effect see for example, Pindyck, 1984and French,Schwert, & Stambaugh, 1987. This hypothesis basically suggests that if volatility ispriced, an anticipated increase in volatility would increase the required rate of returnthus implying an immediate fall in stock price to allow for higher future returns.The earliest generation of GARCH models, such as the seminal ARCH p model of EngleR. F., 1982 the GARCH p,q of Bollerslev, 1987, and their inmean generalization

Engle, Lilien, & Robins, 1987are able to capture the volatility clustering exhibited byfinancial asset returns, however they can only account for the magnitude of the shock,but not the sign affecting conditional variance. Hence, this first generation of timevarying volatility models is unable to capture the differences in impact on volatilitycaused by bad news as opposed to good news. In order to overcome this limitation,more flexible specifications of the conditional variance were introduced. Some of themost popular models used in empirical studies to take this asymmetry into account arethe Exponential GARCH EGARCH model by Nelson, 1991, the Asymmetric GARCHAGARCH by Engle & Ng, 1993, the threshold GARCH GJRGARCH by Glosten,Jagannathan, & Runkle, 1993, the threshold GARCH TGARCH by Zakoin, 1994, and


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the quadratic GARCH of Sentana, 1995. These models have been widely used to modelthe asymmetry commonly observed in returns.

Impact of information arrivals has been considered extensively in economics andfinance. With theories, models and observations constantly being updated, research inthis field is as dynamic as ever. The contribution of this thesis is the empirical study ofthe impact of such information arrivals on the Australian equity market. This aim of thisthesis is to extend the work of the research in this field and to understand the impact ofmacroeconomic news releases as well as market participant expectations to examine if

the Australian stock market behaves in line with those in the US, Japan and the UK.


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3. DATA

In order to effectively measure the impact of news announcements on the Australianequity markets, several macroeconomic and financial market variables were utilized.The following section provides a summary of the variables used and created.3.1 INDEX DATADaily closing stock prices on the ASX200 index and the S&P500 Index were used tocalculate the returns, which were calculated using the formula:Returns LN /The closing prices were downloaded from DataStream. Figures 1 and 2 below plot thereturns on both indices for the sample period 19992008Figure 1 Returns on ASX200

0.1

0.08

0.06

0.04

0.02

0

0.02

0.04

0.06

0.08

1/01/1

999

1/06/1

999

1/11/1

999

1/04/2

000

1/09/2

000

1/02/2

001

1/07/2

001

1/12/2

001

1/05/2

002

1/10/2

002

1/03/2

003

1/08/2

003

1/01/2

004

1/06/2

004

1/11/2

004

1/04/2

005

1/09/2

005

1/02/2

006

1/07/2

006

1/12/2

006

1/05/2

007

1/10/2

007

1/03/2

008

1/08/2

008

RETURNONASX200

RETURNONASX200


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Figure 2 Return on SP500

3.2ANNOUNCEMENT DUMMY VARIABLESThe Australian Bureau of Statistics publishes dates and makes official announcements ofall economic indicators for the Australian economy. Four variables were considered inthis analysis, those being CPI, GDP, Retail Trade and Unemployment. CPI and GDP areannounced quarterly by the ABS. Using the ABS Historical Releases information, fourseparate dummy variables were created for these four indicators. These dummies tookthe value of unity if an announcement regarding the macroeconomic variable was madeat 11:30am on a given day. For example, if CPI figures were released on a 23rd June2008, then the announcement dummy for CPI equalled unity on 23rd June 2008. Table 1

below presents a succinct summary of the variable, the frequency of announcementsand the measure.

0.15

0.1

0.05

0

0.05

0.1

0.15

1/01/1999

1/06/1999

1/11/1999

1/04/2000

1/09/2000

1/02/2001

1/07/2001

1/12/2001

1/05/2002

1/10/2002

1/03/2003

1/08/2003

1/01/2004

1/06/2004

1/11/2004

1/04/2005

1/09/2005

1/02/2006

1/07/2006

1/12/2006

1/05/2007

1/10/2007

1/03/2008

1/08/2008

RETURNonS&P500


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Table 1 : Summary characteristics of macroeconomic indicator announcements

GDP CPI RETAILSALESUNEMPLOYMENTRATE

FREQUENCY QUARTERLY QUARTERLY MONTHLY MONTHLY

SOURCE ABS ABS ABS ABS

UNITOF MEASUREMENT $A BILLION % CHANGE IN CPIFROMPREVIOUS YEAR% CHANGEIN GROSSRETAILSALES FROMPREVIOUSMONTH

UNEMPLOYMENTRATE %

TIMEOFANNOUNCEMENT

11:30AM 11:30AM 11:30AM 11:30AM

TOTAL NUMBER 40 41 108 120

3.3MACROECONOMIC VARIABLES:1. CPI The consumer price index CPI is one of the main indicators of the

purchasing power of money. One of the main goals of Australian monetary policyis the maintenance of this purchasing power. The CPI measures the cost of arepresentative basket of goods and services relative to the same basket of goodsand services in a fixed year base year. This basket consists of typical goods andservices used by a typical Australian household. CPI figures are announced by


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the ABS at the end of each quarter three months ending June, March, Septemberand December.

2. GDP Most market participants consider GDP as a primary indicator of theprevailing economic conditions and it is keenly monitored. GDP is the gross valueadded of all resident producers for a particular period. GDP is usually measuredin three different ways. It can be derived as the sum of factor incomes and nettaxes on production and imports; as the sum of all final expenditures byresidents, changes in inventories and exports less imports of goods and services.The ABS rebases every year, and introduced chain volume measures in 1998 toremove the discrepancy between the three approaches. Since this change incalculating methodology of GDP was introduced in 1998 it does not affect ourdata.

3. Unemployment

The unemployment rate is a sensitive indicator of conditions inthe labour market. Unemployment is considered a significant macroeconomicissue. Each month the ABS conducts the Labour Force Survey surveying about3000 randomly selected households. Every person in the household, over 15years of age is placed into one of 3 categories:

Employed: A person is employed if he or she worked fulltime or parttimeduring the past week or is on vacation or sick leave from a regular job.

Unemployed: A person is considered unemployed if he or she did not workduring the preceding week but made some effort to find work.


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Out of Labour Force: A person is considered to be out of the labour force if heor she did not work in the past week and was not actively seeking work.

Unemployment rate announcements are made every month at 11:30am. Therehave been no changes to the structure of the Labour Force Survey or to thedefinitions used in the Labour Force Survey between 1999 and 2008.

4. Retail Trade Retail trade is a measure of the total sales of goods and services byretail stores in Australia. Retail sales are a very important measure of consumerspending and inflationary pressures in the Australian economy. The ABS releasesa Retail Trade Series presenting estimates of the value of turnover of retailtrade businesses classified by industry, and by state and territory. Theseestimates of turnover are compiled from the monthly Retail Business Survey and

are in current price terms. These figures are also released by the ABS at11:30am.As seen in the variable definitions above, all the news releases are made at 11:30am bythe ABS. Information about all four of the variables were collected through surveys atsome point making them subject to measurement error. It is important to note that ABSutilizes methods to correct these biases however, all data is subject to biases resultingfrom misreporting of data items, deficiencies in coverage, nonresponse and coverageerrors.


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3.4EXPECTATIONS DUMMY VARIABLES

Market expectations data was derived from theEconomic Outlook Reports

releasedmonthly by the St. George Bank. These report both the actual value of the indicator aswell as the consensus expectation of the variables. I have used these consensusexpectations, and by comparing them with the actual value released I have eitherlabelled news as positive or negative. Subsequently, dummy variables for eachmacroeconomic indicator GDP, CPI, Retail Trade and Unemployment were created.

Table 2 below lists the individual variable definition and the parameters according towhich they were defined as positive and negative.Table 2: Definition of expectations dummy variableVARIABLE POSITIVE NEWS IF NEGATIVE NEWS IFCPI % Change in CPI from

previous quarter expected% change in CPI fromprevious quarter

% change in CPI from previous quarter

expected % change in CPI from previousquarter

GDP % Change in GDP since lastquarter expected %change in GDP since lastquarter

% change in GDP since last quarter expected % change in GDP since lastquarter

UNEMPLOYMENT Unemployment rate lastmonthexpectedunemployment rate

unemployment rate last month expected unemployment rate


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RETAIL TRADE % Change in retail salesfrom previous month expected % change in retailtrade from previous month

% change in retail sales from previousmonth expected % change in retail tradefrom previous month

As seen in Table 2 above, the positive dummy variable equalled unity if the newsreleased was good and zero otherwise. The negative dummy variable equalled unity ifthe news released was bad and zero otherwise. Actual numbers were not used due to

difference in the units of measurement for each of the variables. Using actual numberswould have involved using some sort of standardization procedure. The process is madesimpler by just classifying the positive and negative news. This method also takes intoaccount those days on which no news was released for any of the macroeconomicindicators.


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4. PRELIMINARY DATA ANALYSIS4.1 Introduction

The major goal of this research is to assess the impact of scheduled macroeconomicnews releases on Australian equity markets.In order to determine the type of model that needs to be used, it is important toexamine the data set. When dealing with financial time series data, certain stylized factsare known to exist. Running diagnostic tests and carrying out certain checks will enableus to determine the nature of modelling required to assess the impact of newsannouncements on Australian equities.

4.2Stylized Facts

It is likely that many relationships in finance are intrinsically nonlinear. As Campbell,Lo and McKinley 1997 state, the payoffs to options are nonlinear in some of the inputvariables, and investors willingness to trade off returns and risks are also nonlinear.These observations provide clear motivations for consideration of nonlinear models ina variety of circumstances in order to capture better the relevant features of the data.There are several common features of financial data that cannot be explained by linear

structural or univariate time series models. Time series models are usually atheoretical, implying that their construction and use is not based upon any underlyingtheory, rather they are an attempt to capture empirically relevant features of theobserved data. Examining the stylized features of the data will allow us to determine


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what type of model should be used to investigate the impact of news announcements onthe ASX200.

4.3Model Diagnostics

Table 3 presents the summary statistics of the daily returns data. The mean representsthe average percentage daily return on the ASX200. The distribution of the returnsappears to be negatively skewed as evidenced by a negative coefficient of 0.645.Table 3 Summary statistics of ASX200

ASX200Observations 2609Mean 0.00012058Standard Error 0.000193747Standard Deviation 0.009896279Sample Variance 9.79363E05

Kurtosis 8.893888324Skewness 0.645080962Range 0.14332508JacqueBera Test Statistic 8742.358

LEPTOKURTOSIS: An important stylized fact concerning financial data is that there arefrequent extreme observations in both tails of the empirical distribution of manyfinancial series, which are not consistent with the assumption of normality. Thedistributions exhibit fatness in tails which corresponds to points in time where largemovements in returns have been excessive relative to the normal distribution. The


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0

50

100

150

200

250

300

350

400

450

500 RETURNSONASX200

RETURNS

sharp peaks correspond to periods when there is very little movement in the return

series. Figure 3 below represents the returns distribution of the ASX200. The returns

distribution of the ASX200 is fattailed as evidenced by the high coefficient of kurtosis

8.893. This feature is also supported by the Jacque Bera test which yields a statistic of

8742.358 thus rejecting the null hypothesis of normality.

VOLATILITY CLUSTERING: This is the tendency for volatility in stock markets to

appear in bunches. Thus large returns of either sign are expected to follow large

returns, and small returns of either sign are expected to follow small returns. A

plausible explanation for this phenomenon, which seems to be an almost universal

feature of asset return series in finance, is that the information arrivals which drive

price changes themselves occur in bunches rather than being evenly spaced over

time. The important point to note from Figure 4 is that volatility occurs in bursts.

Figure 3 Distribution of the Returns on ASX200


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There appears to have been a prolonged period in the market in the early 2000s,

evidenced by only small positive and negative returns. On the other hand, post 2005;

the ASX200 seems to have many large positive and large negative returns, and

thereby far greater volatility.

The figures present above also suggest the presence of heteroskedasticity. The returns

data is negatively skewed coefficient of 0.645 and also points towards the likely

presence of heteroskedasticity. One of the most important assumptions of the Classical

Figure 4 Daily Squared Returns for January 1999December 2008

0

0.001

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1/06/1999

1/11/1999

1/04/2000

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1/02/2001

1/07/2001

1/12/2001

1/05/2002

1/10/2002

1/03/2003

1/08/2003

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1/02/2006

1/07/2006

1/12/2006

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1/03/2008

1/08/2008

SQUAREDRETURNONASX200SQUAREDRETURNONASX200

Figure 4 Daily Squared Returns on ASX200


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Linear Regression Model is the assumption of constant variance homoskedasticity.Although heteroskedasticity does not destroy the unbiasedness and consistencyproperties of Ordinary Least SquaresOLS, in the presence of heteroskedasticity, OLSestimators are no longer minimum variance or efficient i.e. they are no longer BLUE Best Linear Unbiased Estimators. If we use OLS estimators in this scenario, the tand Ftests based on them can be misleading and may result in erroneous conclusions.In order to test for heteroskedasticity a BreuschPagan test for heteroskedasticity wasconducted on a preliminary model. The estimated model was:

1In equation 1 above reprsented the returns on the ASX200, X represents thematrix of the dummy variables created for announcement effects and is the error.The BP test emphatically rejected the null hypothesis of constant variance, therebyconfirming the presence of heteroskedasticity1. We can therefore conclude that the

initial analysis of the returns data through graphs and the heteroskedasticity testssuggest that the variances of the returns series are not constant over time.Kendall & Hill, 1953 found that stock market prices could not be predicted. He foundthat weekly changes in prices could not be predicted either from past changes in theseries or from past changes in other series. This suggests that prices follow a randomwalk. The random walk suggests that changes in prices of financial assets areindependent. Autocorrelation tests are a way of detecting departure from a randomwalk. Financial applications often need to be tested for the presence of serialcorrelation.

1REFERTOAPPENDIXA


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The LjungBox Q statistic, presented in Table 4 was computed to check forautocorrelations within the data. The results suggest that there exists higher order ofautocorrelation in the data, with Qstatistic being significant for a large number of lagsfor equation one.Table 4 LjungBox Qstatistics for equation 1LAG AC PAC QSTATISTIC1 0.042** 0.042** 4.65652 0.023** 0.024** 5.9884

3 0.046* 0.049* 11.6334 0.016** 0.011** 12.2655 0.033* 0.032* 15.184* Means coefficient is significant at 1%** Means coefficient is significant at 5%

Table 4 above lists the Autocorrelations AC and Partial Autocorrelations PAC of theresiduals of equation 1. The Qstatistic represents the LjungBox Qstatistic for theLjungBox Q test. This test is implemented to check for the presence of autocorrelationsbetween the error terms of equation 1. The test rejects the null hypothesis of noautocorrelations emphatically, with at least five lags of AC and PAC being significant.Since the LjungBox Q test suggested that there was a presence of higher order serial

correlation in the data and hence the BreuschGodfrey test was used. The BreuschGodfrey test for serial correlation detects if error terms in a regression are correlatedover time. Table 5 below presents the results of the BreuschGodfrey test. Thecoefficient is significant at 1%, and it can be clearly seen that higherorder serial


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correlation exists in the data, with the null hypothesis of no correlation between errorsbeing emphatically rejected.

Table 5 Breusch Godfrey Test for Serial CorrelationFstatistic 3.0209 Probability 0.010069Obs*Rsquared 15.05198 Probability 0.010143Testing for nonlinearity in financial asset returns has been an important area of

econometric research in the last couple of decades as a consequence of the rapiddevelopment in the econometric methodologies necessary to compute the tests, and as aconsequence of the increasingly widely held view that the world is probably not linear.A common finding is that there is substantial evidence in favour of some nonlinearstructure see for example, Scheinkman & LeBaron, 1989, Hseih, 1991 forapplications to stock returns.

There are numerous tests for nonlinear patterns in time series that are available. Thesetests can be broadly split into two types: general test and specific tests. General tests, ,are usually designed to detect many departures from randomness in data. Theimplication is that such tests will detect a variety of nonlinear structures in data.However, these tests do not indicate the type of nonlinearity that is present. The BDStest tests the null hypothesis that the data is pure noise. It has been argued that the BDStest has the power to detect a wide variety of departures from randomness. It follows astandard normal distribution under the null hypothesis. Most applications of the abovetests conclude that there is nonlinear dependence in financial asset returns series, but


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that the dependence is best characterized by a GARCHtype process. For the purposes ofthis research, the BDS test for independence was applied to equation 1. The resultsfrom this test are listed below in Table 6. It can be clearly seen that there still existsconsiderable nonlinearity in the data. This confirmation of the nonlinearity hypothesiscan lead to our modelling strategy.Table 6 BDS Independence Test for equation 1Dimension BDS Statistic Standard Error zStatistic2 0.027406* 0.00179 15.30822

3 0.052946* 0.002843 18.620994 0.070989* 0.003384 20.97665 0.082427* 0.003526 23.379116 0.087449* 0.003399 25.7309* Means coefficient is significant at 1%

This confirms that the data is nonlinear in nature and hence an appropriate modelwould be required to effectively assess the impact of scheduled macroeconomic newsannouncements on the Australian equity market. Prior to proceeding further, an ARCHeffects test was also conducted on the preliminary OLS regression equation 1. Theresults of this test are shown in Table 7 below. The ARCHLM test was devised by EngleR. F., 1982. The ARCHLM test has the null hypothesis of homoskedasticity, i.e. constantvariance of the error term and an alternative hypothesis of heteroskedasticity. The testprocedure is to run the regression: 2


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The test statistic is and is asymptotically distributed as a chisquared distribution.If any of the coefficients in that series are significant then there may be potential ARCHeffects in the regression. The model showed strong signs of potential ARCH effects andthereby made the case for using a nonlinear model stronger.Table 7 ARCH LM TEST for equation 1Variable Coefficient Std. Error tStatistic

3.07E05* 6.16E06 4.978323u 0.079748* 0.019572 4.074575

u

0.181182* 0.019376 9.351014u 0.193687* 0.019329 10.02064u 0.16167* 0.019376 8.343826u 0.070524* 0.019571 3.603414

*means coefficient is significant at 1%

It is well known that US stock markets influence stock markets in most economies of theworld. In order to confirm this observation, correlations between the closing prices onthe ASX200 in Australia and the US SP500 were checked. As can be seen in Table 7below, the correlation between the closing prices of both the indices is very high. Thissuggests that the US stock market is very likely to have a significant impact on the

Australian stock market.Table 8 Correlation between ASX200 and SP500ASX200 SP500ASX200 1 0.534111SP500 0.534111 1


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In this chapter, several important features of the dataset have been seen. It has beenshown that the data exhibits leptokurtosis, volatility clustering, heteroskedasticity andalso the presence of ARCH effects.The next chapter will consider the most effective methodology required to model thedata.


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5. METHODOLOGYIn this section I will consider the econometric methodology adopted to analyse the

impact of news announcements and news content on the mean returns and thevolatility of the Australian stock market.Over many decades and in countless papers, certain observations about the nature offinancial time series data have been made. Known as stylized facts these observationsmotivate the adoption of certain types of econometric models. A variety of diagnostictests were conducted in chapter 4 of this thesis to explore the returns data on theAustralian equity market and to confirm the presence of such observations. As seen inchapter 3, the data for the ASX200 for the period between 1999 and 2008 exhibitsvolatility clustering, skewness and peakedness in the distribution of returns as well asleptokurtosis. In addition, tests such as the BreuschGodfrey test for serial correlation,BreuschPagan test for heteroskedasticity and the ARCHLM test confirmed the

presence of serial correlation, heteroskedasticity as well as ARCH effects in the data. Inthis section, I will attempt to formulate a modelling strategy such that the model utilizedto derive results will incorporate and adjust for as many features of the data as possible.In chapter 4, the Qstatistic with a statistically significant lag1 autocorrelation suggeststhat the lagged return on ASX200 might be useful in predicting the return at timet.Therefore, I have used a simple mean equation for models postulated, the equation tookthe form:

3Where is the return on the ASX200, is the constant, is the last periods return on the ASX200 and is the error term


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Additionally, in chapter 3, it was also identified that the closing prices on the ASX andthe previous days closing price on the SP500 were highly correlated the correlationcoefficient took the value of 0.534. This coupled with extensive literature oninformation spillovers between economies suggested that the return on the US SP500could be a significant variable in the modelling process. Hence, the mean equation for allmodels was altered to take the form: Madelbrot, 1963

4An important parallel development in literature was the introduction of nonlinear time

series models. Such non linear models can take into account the changing variances thatseem to be a stylized fact for this particular dataset. Uncertainty is central to modernfinance theory. According to most asset pricing theories the risk premium is determinedby the covariance between the future return on the asset and one of more benchmarkportfolios. It has long been recognized that the uncertainty of speculative prices, asmeasured by the variances and covariance, are changing through time see for example,Madelbrot, 1963 Fama E. , 1965. Subsequently, applied research started explicitlymodelling time variation in second or higherorder moments. Foremost amongst thesewas the Auto Regressive Conditional Heteroskedasticity ARCH model developed byEngle R. F., 1982. The advantage of the ARCH model is that it allows us toparameterize volatility clustering. The model allows the conditional variance of the

error term,

to depend on the immediately previous value of the squared error,therefore accounting for volatility clustering. The mean equation of an ARCH q modelcan take any structure. I only impose the ARCH q structure on the variance equation.


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For example, a basic ARCH 1 model to assess the impact of macroeconomic newswould be:

5 In the equation above, represents the returns in Australia at timet, X representsthe vector of covariates that includes all explanatory variables and n represents theerror term. The variance equation has a standard ARCH structure such that the

conditional variance of the error term,

to depend on the immediately previousvalue of the squared error .The data was fitted with a basic ARCH structure with up to 5 lags2. All these 5 lags werevery highly significant at the 1% level. However, choosing an appropriate lag length inan ARCH model is challenging. It is possible that the value of q, the number of lags onthe squared error that are required to capture all of the dependence in the conditional

variance, might be very large. The ARCH model is therefore not parsimonious. Anotherissue is that the ARCH model imposes nonnegativity constraints on the model. Otherthings being equal, the more the parameters in the variance equation the more likely itis that these nonnegativity constraints are violated.Bollerslev, 1987 extended Engles original work by developing a technique that allows

the conditional variance to be an ARMA process. The generalized ARCH p, q modelcalled GARCH p, q allows for both autoregressive and moving average components inthe heteroskedastic variance. Clearly, the GARCH model is more parsimonious andentails fewer coefficient constraints. The GARCH p, q model allows the conditional2REFER TO APPENDIX B


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variance to be dependent upon its previous own lags. A basic GARCH 1, 1 model toassess the impact of macroeconomic news would be:

6In the equation above, represents the returns in Australia at timet, X representsthe vector of covariates that includes all explanatory variables and represents theerror term. The variance equation has a standard GARCH structure such that the

conditional variance

is dependent on its own lag

.Several basic models were run to estimate the best model for the data. For every model,a test of model adequacy was performed. The main test used in this context was theARCHLM test to check for the appropriateness of the ARCH or GARCH model run3. TheARCHLM test checks data for any remaining ARCH effects that might require modelling.It provides a means of checking the number of lags of ARCH and GARCH used in the

postulated model.It is plausible that an increase in stock market volatility raises required stock returns,and thus lowers stock prices. An asymmetric model would be able to capture thediffering impacts of good and bad news in the Australian stock market. GARCH 1,1models appear to fit financial data really well, however GARCH models do not allow for

asymmetries such as the leverage effect, and it has been this shortcoming which has ledto nonlinear GARCH specifications which allow positive and negative news to havedifferent effects on volatility.

3REFERTOAPPENDIXC


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The models for the impact of news announcements have known to display anasymmetric impact. Hence the above models were tested for the presence ofasymmetry. Engle & Ng, 1993 proposed a set of tests for asymmetry in volatilityknown as sign and size bias tests. These Engle and Ng tests were used to determinewhether an asymmetric model was required to assess the impact of macroeconomicnews announcements on the ASX200, or whether the symmetric GARCH model wasadequate. The EngleNg tests are usually applied to the residuals of a GARCH fit to thereturns data.

Defining

as an indicator dummy that takes the value of 1 is 0 and zerootherwise. The test for sign bias is based on the significance or otherwise of in 7

Where is an iid error term. If positive and negative shocks to impact differentlyupon the conditional variance, then will be statistically significant. It could also be

the case that the magnitude or size of the shock will affect whether the response ofvolatility to shocks is symmetric or not. In this case, a negative size bias test would beconducted, based on a regression where is now used as a slope dummy variable.Negative size bias is argued to be present if is statistically significant in theregression:

8Significance of indicates the presence of sign bias, where positive and negativeshocks have differing impacts upon future volatility, compared with the symmetricresponse required by the standard GARCH formulation.


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The GARCH 1, 1 models for evaluating the impact of announcements as well as forevaluating the differential created by bad and good news was tested for sign bias. If signbias existed, then GARCH 1, 1 models would not be valid as they will not be able tocapture the asymmetric impact that exists. Table 9, below presents the coefficientestimates of the signbias test conducted on both the models.Table 9 Sign bias test results for GARCH1,1

Announcement impactGARCH 1,1

News Content ImpactGARCH 1,1

Signbias test 8.56E05*6.39E06 8.56E05*6.34E06

Negative signbias test 0.015034*0.000698

0.014934*0.000693

* means coefficient is significant at 1%

As seen in Table 9 above, for the model hypothesized in this research, there was foundto be a significant negative asymmetric impact. It seems from the preliminary resultsthat a linear GARCH specification will not suffice. Hence, the model was further alteredto include terms that would allow for asymmetry.There are several competing models which have been devised by researchers over thepast few years. One of the most commonly used asymmetric GARCH model is the GJRGARCH model devised by Glosten, Jagannathan, & Runkle, 1993.


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They specify the variance equation as: 9

1 0 0 If the future variance of the series is not a function solely of the squared innovation ofthe current return, then a simple GARCH p, q model would be misspecified and theempirical results derived from such an estimation procedure will not be entirely

reliable. Looking at the coefficients of the BDS test presented in Table 6 earlier in thissection, we can clearly see that some nonlinearity still exists in the data even though aGARCH model has been specifically used to correct for this non linearity. This combinedwith the sign bias test suggests that the series of Australian stock returns is not solely afunction of the squared innovation of the current return.Several competing models exist for modelling asymmetries. However, EGARCH devised

by Nelson, 1991and GJRGARCH devised by Glosten, Jagannathan, & Runkle, 1993are the only models that EViews runs. Both models were estimated for this dataset,however the GJRGARCH was chosen over the EGARCH, since it achieved convergencethrough the BHHH optimization algorithm. EGARCH did not converge. The reason forthis nonconvergence can be traced to the presence of news dummy variables in thevariance equation, which causes issues for optimization algorithms Doornik & Ooms,2003. This suggested that the estimates obtained through GJRGARCH would be muchmore reliable.It can thus be concluded that the GJRGARCH specification is optimal to assess theimpact of macroeconomic news announcements and the impact of expectations on the


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Australian Stock Market. Subsequent sections will discuss the nature of results obtainedand the implications of these results.


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6. EMPIRICAL RESULTS

Following from the methodology presented in chapter 4, I will now use the variablesdiscussed in chapter 3 to estimate the impact of macroeconomic news announcementson the conditional mean and volatility of the returns on the Australian stock market. Allestimation presented has been done using EViews5.In chapter 3, we identified several stylized features of the data which strongly suggestedthat the data was nonlinear in structure. Hence, all regressions presented in this

section have been estimated using the BHHH optimization algorithm Berndt, Hal, Hall,& Hausman, 1974.Chapter 4 showed the basic econometric techniques that were used to estimate theresults. The GJRGARCH model was employed in analysing the dataset owing to themore parsimonious nature of the model as well as its ability to explain the asymmetry inthe response of stock returns to news. In reaching this conclusion, several differentmodels were postulated and estimated4. All postulated models were tested for anyremaining ARCH effects postestimation. This was done to ensure that the actual modelused was adequate, and robust.6.1 IMPACT OF NEWS ANNOUNCEMENTS

The following results correspond to the announcement impact of news on theAustralian equity market. It is hypothesized that news arrivals cause market volatility toincrease. Macroeconomic news is released by the ABS at 11:30am. The ABS has anembargo on such announcements, and as such our model assumes that market4REFER TO APPENDIX D


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participants have no private information about the content of this news, they are onlyaware that figures for a particular macroeconomic indicator, will be released at11:30am on a certain date.Using data extracted from the ABS historical release calendar and the announcementdummy variables hence created, the following model was estimated to assess theimpact of news announcements on the Australian stock market: 10

Where represents the returns on the Australian stock market, , representsthe one period lagged return on the US stock market, are the dummy variables, suchthat j1GDP, j2CPI, j3Retail Sales and j4 Unemployment. These dummyvariables take the value of one on those days in which a scheduled news announcementoccurs for each of the four economic variables described in Section 3 and 0 otherwiseand is the error term which is assumed~0, . is the indicator dummy variablewhich takes the value of one if 0 and zero otherwise. The coefficient accountsfor the asymmetric impact caused by news.The model above was run to estimate the maximum likelihood estimates of the impact

of news announcements on the four economic variables. The results were obtained byjointly estimating the mean and variance equations shown above. These results arepresented in Table 10 below:


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Table 10 Mean equation estimates for equation 10Variable Coefficient Standard Error zStatisticR 0.072033 0.01771 4.067

R 0.342452 0.012004 28.529CPI 0.000529 0.000915 0.578Unemployment 0.000339 0.000541 0.627Retail Trade 0.000249 0.000607 0.409GDP 0.000313 0.00134 0.233

means coeficient is signiicant at 1%The coefficients on the lagged US and Australian stock market variable are highlysignificant, proving the importance of their inclusion in this model. The significance ofthese lags suggests that past returns on the Australian and US stock markets areinfluential in determining current returns on the Australian stock market. This provides

evidence that foreign stock markets have an impact on the returns of the Australianstock market.The result obtained is similar to those that have investigated information spilloversbetween markets. Particularly, Kim & In, 2002 found that major stock markets UK,US and Japan have a significant impact on the returns of the Australian stock and

futures market. This result is not entirely unexpected since these countries representsome of Australias major trading partners and the high degree of financial marketintegration has increased the significance of these markets.The announcement impact is estimated by the coefficients of CPI, GDP, Unemploymentand Retail Trade. None of these variables are statistically significant for the mean


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equation. This suggests that there is no impact of a news announcement on the CPI,GDP, Unemployment rate or Retail Trade being made on the mean returns of theASX200. Studies undertaken by Kim, McKenzie, & Faff, 2004 and Kim & In,2002found similar results, with the announcement effect of macroeconomic variablesbeing insignificant.Examining the variance equation will allow us to assess the impact of newsannouncements on volatility of the market. Table 11 below presents the results of thevariance equation of the model.

Table 11 Variance equation estimates for equation 10Variable Coefficient Standard Error zStatisticC 1.38E06* 4.34E07 3.181 0.028065** 0.017294 1.622 0.125343* 0.036769 3.409 0.423878* 0.175275 2.418 0.460049* 0.16058 2.865CPI 9.18E07 4.95E06 0.185Unemployment 2.85E06 4.52E06 0.630Retail Trade 1.02E06 4.41E06 0.231GDP 1.02E05 9.26E06 1.105R 0.000304* 9.77E05 3.106 means coeficient is signiicant at 1% means coeficient is signiicant at 5%


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All of the ARCH and GARCH terms are significant, as shown in Table 11. Modelestimates can also provide insight into the degree of persistence of the volatility shocksin the market. For shocks to be highly persistent, the sum of the coefficients on theARCH and GARCH terms should be close to unity. For this equation, thecoefficients , , is approximately 0.92, suggesting that volatility shocks arepersistent in this particular case. This result is consistent with that obtained by Kim &In, 2002Table 11, also provides evidence that the returns on US stock markets cause volatility in

the Australian markets. The coefficient on the variable is

is highly statisticallysignificant at 1% and has a negative sign. This proves that a decrease in the mean returnon the US stock market leads to an increase in the volatility of the Australian stockmarket. This impact could be seen as Australias reaction to a loss of confidence in theUS stock markets. This suggests an increase in the risk in the global economy, andthereby is reflected through increased volatility episodes in the domestic market. For

example, in the period between 1999 and 2008, two major stock market crashes tookplace in the US 2001 and 2008 triggering large falls in the asset prices in Australia.The returns on the US stock market also seem to be significantly increasing volatility inthe Australian equity markets. The coefficient on the US stock returns is negative,suggesting that a fall in the mean return on the US SP500 leads to an increase in the

volatility of the Australian ASX200. This result may be explained by the increase inuncertainty caused due to the fall of the US SP500. A fall in the US SP500 may result insignificant falls in other share markets worldwide due to the increased globalintegration amongst countries. This domino effect causes the risk of investing in


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smaller markets to increase exponentially, and market participants will move to hedgetheir positions in the market, thereby increasing the volatility of the market.

None of the other variables are statistically significant, and hence represent no notablechange in the conditional variance of the returns on the ASX 200. The terms on theGARCH and ARCH are also highly statistically significant, hence representing evidencefor model acceptance.Additionally, a signbias test conducted on the GARCH 1, 1 model for the data inchapter 3 suggested a strong presence of negative sign bias. This suggested that there

exists considerable asymmetric impact in the data. The asymmetry term is positive andhighly statistically significant. This implies that a negative announcement has a biggerimpact on the market as opposed to a positive announcement. More importantly, itsuggests that the nature of announcement could be very important in understanding theimpact of news announcements on the mean returns and the volatility of the Australianstock market.6.2IMPACT OF NEWS CONTENT ON THE STOCK MARKET

The model presented above looked at the impact of announcements on the ASX 200; itdid not take into account the nature of news. It is intuitive that the nature of news couldalso have an impact on the mean and variance of the stock returns. The impact of newscontent on the stock market was modelled using the equation below:


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11

Where represents the returns on the Australian stock market, , representsthe one period lagged return on the US stock market, are the dummy variables suchthat j5positive CPI, k1negative CPI, j6 positive unemployment, k2negativeunemployment, k7positive retail trade, j3negative retail trade, j8positive GDPand k4negative GDP. These dummy variables take the value of one on those days inwhich a positive scheduled news announcement occurs for each of the four economicvariables described in Chapter 3 and 0 otherwise, are the dummy variables whichtake the value of one on those days in which a negative scheduled news announcementoccurs for each of the four economic variables described in Chapter 3 and is the errorterm which is assumed~0, . is the indicator dummy variable which takes the

value of one if

0 and zero otherwise. The coefficient accounts for theasymmetric impact caused by news.Generally it is expected that the conditional variance of the stock market would increasewhen negative news about certain macroeconomic variables is released. This increasein conditional variance occurs because market participants frame news as good or baddepending on their apriori expectations. For example, suppose market participants

expect the upcoming retail trade news to be an increase in xpercent. An increase in theretail trade figures represents an increase in consumption expenditure, and hencesuggests an improvement in economic activity. If the news released was not asexpected, it is likely, market participants will react unfavourably to it, and hence causethe conditional variance of returns to increase. In order to capture this impact of


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expectations, the model above was estimated, such that market participants reactionwas also taken into consideration when observing the effect of news on the market.

Table 12 Mean equation estimates of equation 11Variable Coefficient Standard Error zStatisticR 0.073415* 0.017715 4.144R 0.341414* 0.012042 28.351Positive CPI 0.000197 0.001236 1.238

Negative CPI 0.000636 0.00127 0.501Positive Unemployment 5.92E05 0.000858 0.069Negative Unemployment 0.000433 0.000805 0.538Positive Retail Trade 0.000934 0.000813 1.149Negative Retail Trade 0.000681*** 0.00043 1.584Positive GDP 0.001912 0.003354 0.570Negative GDP 0.000467 0.001124 0.416 means coeficient is signiicant at 1% means coeficient is signiicant at 15%

Table 12 above presents the maximum likelihood estimates of the mean equation of the

GJR GARCH 1, 3 regression of the above model. The GJRGARCH 1, 3 model wasused to capture the asymmetric impacts that were found to be present in the data afterconducting a signbias test, as detailed in chapter 4. Similar to Equation 10, thecoefficients on the lagged return on the ASX200 and the US SP500 are highly significant.The coefficient on the lagged return of the US SP500 is positive, suggesting that an


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increase in the mean return on the US SP500 index leads to an increase in the meanreturn on the ASX200. This result is similar to the result obtained for Equation 10.

The coefficient on the dummy variable for negative retail trade is also significant. Thisresult suggests that market participants react to the news content of the retail salesfigures rather than just the release of the announcement. The coefficient on variable ispositive, suggesting that the release of retail sales news leads to an increase of the meanreturn on the ASX 200. This could be explained by the notion that retail sales figuresdepict the level of consumer spending in the economy. Negative retail trade news can be

viewed by market participants as a signal for dampening consumer demand eventuallyleading to a fall in the level of economic activity in the country.For an economy such as Australia, where one of the most important goals of the ReserveBank of Australia RBA is inflationtargeting, the levels of economic activity caninfluence the RBA to take precautionary action against rising inflation by increasinginterest rates. This is seen by market participants as very significant, as increasinginterest rates leads to increased costs of borrowing and thereby leads to a slowdown ofeconomic activity. Since retail trade signals consumer demand we can view the newscontent leading to an increase in uncertainty in the economy. It increases the risk of aneconomic downturn and hence market participants react to this news leading to asignificant reaction in the mean returns on the ASX200. However, the sign on the

negative retail trade news is surprising. It was expected that bad news about the level ofconsumer spending in the economy would lead to a shortterm decrease in the marketreturn. Since the sign on the variable is positive, it suggests that release of bad newsregarding retail trade results in a positive jump in the mean returns on the market.


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If the jump in economic activity is considered to push Australian inflation out of theinflation target then it is possible that the Reserve Bank of Australia could increase theovernight cash rate. In this scenario it is possible that bad news could be in fact seen bythe market as good news. The news would effectively then have the same impact asgood news.None of the other variables are significant in the mean returns equation. It is ratherinteresting to note that the variable CPI has had no impact so far. This does not;however imply that markets were indifferent to the CPI news. Kim S.J. , 1996suggests

that prior to April 1988, higher future inflation expectations was the dominant responseto news about the CPI, however, now the anticipation of a tightening monetary policyresponse by the RBA is more relevant. The data used in this study only ranged from19992008, and thus the impact of the significance of the CPI announcement wasneutralized by the market participants reaction to the news on the overnight cash rate.Similarly, news regarding the unemployment rate in the economy also wasnt deemed

significant by market participants.The same variables have been added to the variance equation to examine the impact ofnews releases on the conditional variance of the ASX 200. Table 13 below presents theresults for the variance equation.


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Table 13 Estimates of the variance equation of equation 11Variable Coefficient Std. Error zStatisticC 1.13E06** 4.85E07 2.338

0.040591** 0.020911 1.941 0.15131* 0.043576 3.472 0.235603*** 0.142482 1.654 0.357966** 0.168585 2.123 0.262451*** 0.155507 1.687 0.000323 9.69E05 0.287Positive CPI 3.17E05* 2.56E05 1.238Negative CPI 4.60E07 6.31E06 0.626Positive Unemployment 3.58E06 5.61E06 2.861Negative Unemployment 3.81E06 5.66E06 0.638Positive Retail Trade 1.84E06 5.47E06 0.673

Negative Retail Trade 7.89E07 2.74E06 1.237Positive GDP 1.37E05 4.77E06 0.073Negative GDP 3.80E06 6.07E06 3.337 means coeficient is signiicant at 1% means coeficient is signiicant at 5%

means coeficient is signiicant at 10%

All of the ARCH and GARCH terms are significant, as shown in Table 13. Modelestimates can also provide insight into the degree of persistence of the volatility shocksin the market. For shocks to be highly persistent, the sum of the coefficients on theARCH and GARCH terms should be close to unity. For this equation, thecoefficients , , , is approximately 0.90, suggesting that volatility shocks are


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persistent in this particular case. This result is consistent with that obtained by Kim &In, 2002

Additionally, similar to equation 10, the coefficient on the lagged US return variable ishighly statistically significant, again implying that a fall in the return on US stockmarkets leads to increased volatility in the Australian stock market. This result isexpected, as follows through from the high correlation between these two markets.Unlike equation 10, we can clearly see that the coefficient on positive CPI news isnegative and highly statistically significant. This implies that a less than expected

increase in prices in the economy leads to a decrease in the volatility of the Australianstock markets. The coefficient is statistically significant at 1% level. This result isintuitive and very important. A variety of studies conducted on the US stock marketshave also found a similar result see for example, Kim, McKenzie, & Faff, 2004Positive news about the inflation leads to the adjustment of inflationary expectations. Asdiscussed before, the RBA monitors the rate of inflation within the economy andattempts to keep it between 23% on average. Positive news about the CPI would meanthat inflation in the economy was lower than expected and hence lead to a downwardrevision of inflationary expectations. This in turn would cause interest rates to remainconstant. Market participants price risk and this good news would lead to downwardrevisions of associated risk and lead the volatility within the economy to decrease. An

increase in inflationary pressures could possibly cause inflation to rise in the mediumterm which would cause the value of investments and assets to decline. It is alsopossible that the market participant reaction may be due to an expectation of a changein interest rates that might be taken by the Reserve Bank of Australia RBA. If the RBAfeels that the growth will be overheating the economy, it will take steps to dampen


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consumer spending in the economy by increasing interest rates. This increase ininterest rates will cause the costs of borrowing to increase, thereby affecting businesses.Hence, positive news about the CPI is seen to be so statistically significant. Moreimportantly, in combination with the marginal significance of the positive GDP news,these results suggest that market participants react to that information which conveyscertain facts about the inflationary pres

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Deepika Patwardhan