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Heteroscedasticity in stock market indicator return data: volume versus GARCH effects

Jandhyala L. Sharma, Mbodja Mougoué, Ravindra Kamath

Open publisher page 75 citations

Abstract

This paper tests for the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) effects in stock market indicator returns using the NYSE daily return and volume data for four years. The findings strongly suggest that the market indicator returns are best described by the GARCH model in the absence of volume as a mixing variable. The inclusion of volume as a proxy for information arrival in the conditional variance model helps in explaining the GARCH effects in stock returns, however, the GARCH effects do not completely vanish as a result of this inclusion.

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What this paper is about

This paper tests for the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) effects in stock market indicator returns using the NYSE daily return and volume data for four years. The findings strongly suggest that the market indicator returns are best described by the GARCH model in the absence of volume as a mixing variable. The inclusion of volume as a proxy for information arrival in the conditional variance model helps in explaining the GARCH effects in stock returns, however, the GARCH effects do not completely vanish as a result of this inclusion.

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OpenAlex reports 75 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper tests for the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) effects in stock market indicator returns using the NYSE daily return and volume data for four years. The findings strongly suggest that the market indicator returns are best described by the GARCH model in the absence of volume as a mixing variable. The inclusion of volume as a proxy for information arrival in the conditional variance model helps in explaining the GARCH effects in stock returns, however, the GARCH effects do not completely vanish as a result of this inclusion.

Key concepts: Autoregressive conditional heteroskedasticity, Heteroscedasticity, Econometrics, Conditional variance, Economics, Stock (firearms), Autoregressive model, Proxy (statistics)

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