Time Series Econometrics: Heteroskedasticity in Stock Return Data: Volume and Number of Trades versus GARCH Effects

The result of Lamoureux and Lastrapes and Omran and McKenzie are extended to the Swedish stock market, and this paper examines their findings that GARCH modelling captures the serial dependence in information flow into the market. Moreover, this paper also examines if (as a proxy for information flow) the number of trades can challenge the volume of trade in order to explain GARCH effects in financial time series. Using data on 25 large stocks that are traded on The Nordic Stock Exchange, this paper finds that even though the parameter estimates of the GARCH model becomes significantly lower for about half of the companies in this study when volume of trade or the number of trades is used in the conditional variance of return equation, the autocorrelation of the standardized residuals still exhibit a highly significant GARCH effect in more than 1/3 of the companies when these two additional explanatory variables are included in the conditional variance equation. The serial dependence in volume of trade and number of trades does not eliminate the need for GARCH modelling of volatility.

Contents

1. Introduction.
2. Background
2.1 The information flow hypothesis
2.2 Previous studies
2.3 This study
3. Market efficiency.
3.1 Theory of ARCH and GARCH models
4. Data and methodology
5. Empirical results and analysis
6. Conclusions
7. References
Appendix

Author: Rosén, Christer

Source: Uppsala University Library

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Time Series Econometrics: Heteroskedasticity in Stock Return Data: Volume and Number of Trades versus GARCH Effects