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The Application of M-Garch Model for Examining the Volatility of Financial Assets

100%
Krężołek D., Department of Statistics K. A. U. o. E. i. K.
Acta Universitatis Lodziensis. Folia Oeconomica
|
2009
|
vol. 228
PL
Większość ekonometrycznych modeli rynków finansowych konstruowanych jest w oparciu o wielkie i rozwinięte gospodarki światowe. Podejście takie nie zawsze znajduje zastosowanie w przypadku młodych i wschodzących rynków. Wynika to po pierwsze z dostępności, a po drugie z charakteru danych tworzących finansowe szeregi czasowe (skupiska danych, grube ogony, autokorelacja). Celem pracy jest zastosowanie modelu M-GARCH do analizy poziomu zmienności stóp zwrotu aktywów finansowych w przypadku, gdy badaniu poddane są portfele inwestycyjne (o więcej niż dwóch składnikach). Przedstawione zostaną różne podejścia do analizy warunkowej wariancji (modyfikacje M-GARCH). Wynikiem będzie ocena stosowalności tej klasy modeli.
EN
The majority of econometric financial market models are based on well run and highly developed economies and available financial time series are very wide, numerous, reporting some specific features as clustering of variance and outliers. Thus, the application of classical methods of the stochastic processes analysis can be biased. The purpose of this paper is to present the review of M-GARCH model to examine the volatility of asset returns in financial market. The analysis includes both individual stocks and portfolios. The most popular approaches of multivariate GARCH models estimation are considered. As a result, the applicability assessment of this class of models within emerging markets will be presented.
2

The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India

80%
Pradhan K.
South East European Journal of Economics and Business
|
2011
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vol. 6
|
issue 1
111-123
EN
This study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms of within sample and out of sample risk-return trade-off at various forecasting horizons. The analysis found that the VEC-GARCH time varying hedge ratio provides the greatest portfolio risk reduction and generates the highest portfolio returns.
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