Non-linearities, GARCH Effects and Emerging Stock Markets

Raj S. Dhankar ()
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Raj S. Dhankar: University of Delhi

Chapter Chapter 3 in Risk-Return Relationship and Portfolio Management, 2019, pp 39-62 from Springer

Abstract: Abstract Up to the beginning of the last decade, financial economics was dominated by linear paradigm, which assumed that economic time series conformed to linear models or could be well approximated by a linear model. However, there is increasing evidence that asset returns may be better characterized by a model which allows for non-linear behaviour. Though more efforts are now being directed towards the Asian stock markets in the light of their increasing importance to the investment world and the world economy, there is an extremely sparse literature, which utilizes recent advances in non-linear dynamics to examine the data generating process of the South Asian stock markets. This study investigates the presence of non-linear dependenceNon-linear dependence in three major markets of South Asia: India, Sri Lanka and Pakistan. It was, however, realized that merely identifying non-linear dependenceNon-linear dependence was not enough. Previous research has shown that the presence of non-linear characteristics usually takes the form of ARCHAutoregressive Conditional Heteroscedasticity (ARCH) /GARCHGeneralized Autoregressive Conditional Heteroscedasticity (GARCH) (Autoregressive Conditional HeteroscedasticityAutoregressive Conditional Heteroscedasticity (ARCH) or Generalized Autoregressive Conditional HeteroscedasticityGeneralized Autoregressive Conditional Heteroscedasticity (GARCH) ) type conditional heteroscedasticityConditional heteroscedasticity . Keeping this in view, this study investigates whether the non-linear dependenceNon-linear dependence is caused by predictable conditional volatilityConditional volatility . It has been found that the simple GARCHGeneralized Autoregressive Conditional Heteroscedasticity (GARCH) (1, 1) model has fitted all the market return series adequately and accounted for the non-linearity found in the series.

Date: 2019
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DOI: 10.1007/978-81-322-3950-5_3

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