ARCH models Research Papers - Academia.edu (original) (raw)

Forecasting the volatility of financial markets is one of the important issues in empirical finance that absorbed the interest of many researchers in the last decade. As it is known, there has been many studies uncovering the properties... more

Forecasting the volatility of financial markets is one of the important issues in empirical finance that absorbed the interest of many researchers in the last decade. As it is known, there has been many studies uncovering the properties of competing volatility models. In this study, both traditional (unconditional) and conditional volatility models, which have the implications for finance that investors can predict the risk, are analyzed. In this study, Box-Jenkins model (ARIMA) and ARCH-type models (ARCH-GARCH-EGARCH-TARCH and GARCH-M) are discussed for the time–dependence in variance that is regularly observed in financial time series and various classical volatility forecasting approaches are compared using ISE-100 Stock Index for the time period between the years 1987 and 2009. As a result, it is found that IMKB-100 returns series include; leptokurtosis, leverage effects, volatility clustering (or pooling), volatility smile and long memory and TGARCH (1,1) is the best fitting mo...

Emerging stock markets of Asia have become a matter of interest for international financial researchers and policy-makers during the last couple of decades. Series of reforms, increasing financial transparency and decreasing restrictions... more

Emerging stock markets of Asia have become a matter of interest for international financial researchers and policy-makers during the last couple of decades. Series of reforms, increasing financial transparency and decreasing restrictions on transactions have made these markets better diversification opportunities for international investors. This paper examines independently as well the linkages of stock markets across the selected Asian countries. The volatility spillover is modelled through an asymmetric multivariate generalized autoregressive conditional heteroscedastic model. In large number of empirical studies of risk return analysis, it is observed that economic stability and good perspectives have been key assets for the development of emerging markets. Diversification of funds to reduce portfolio risk is also one of the point of attraction to domestic and foreign institutional investors. In this work, risk and uncertainty is studied for selected stock markets of emerging economies of Asia. Data of daily stock prices of selected markets is collected for recent decade and detail autoregressive conditional heteroskedasticity (ARCH) and its generalised models are used to estimate conditional and asymmetric volatilities. Keywords Volatility spillovers, emerging market, diversification, unexpected volatility, ARCH effect Asia-Pacific Journal of Management Research and Innovation 13(1&2) 13-33

The volatility clustering frequently observed in financial/economic time series is often ascribed to GARCH and/or stochastic volatility models. This paper demonstrates the usefulness of reconceptualizing the usual definition of... more

The volatility clustering frequently observed in financial/economic time series is often ascribed to GARCH and/or stochastic volatility models. This paper demonstrates the usefulness of reconceptualizing the usual definition of conditional heteroscedasticity as the (h = 1) special case of h-step-ahead conditional heteroscedasticity, where the conditional volatility in period t depends on observable variables up through period t-h. Here it is shown that, for h > 1, h-stepahead conditional heteroscedasticity arises-necessarily and endogenously-from nonlinear serial dependence in a time series; whereas one-step-ahead conditional heteroscedasticity (i.e., h = 1) requires multiple and heterogeneously-skedastic innovation terms. Consequently, the best response to observed volatility clustering may often be to model the nonlinear serial dependence which is likely causing it, rather than 'tacking on' an ad hoc volatility model. Even where such nonlinear modeling is infeasible-or where volatility is quantified using, say, a model-free implied volatility measure rather than squared returns-these results suggest a reconsideration of the usefulness of lag-one terms in volatility models. An application to observed daily stock returns is given.