Article2010

One-day-ahead value-at-risk estimations with dual long-memory models: evidence from the Tunisian stock market

23 citations0/0 files storedInternational Journal of Financial Services Management

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Complex Systems and Time Series Analysis

Abstract

In this paper, we assess the one-day-ahead Value-at-Risk (VaR) performance for the Tunisian Stock Market (TSE). Using the ARFIMA-FIGARCH and ARFIMA-FIAPARCH models under three alternative innovation distributions: normal, Student and skewed Student, we show that the ARFIMA-FIAPARCH with skewed Student innovations outperforms the other models since it jointly considers the asymmetry, long-range memory and fat-tails in the TSE return behaviour. This model provides the better results for in and out-of-sample VaR estimations for both short and long trading positions.

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References

1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1988Testing for a Unit Root in Time Series Regression 1992Testing the null hypothesis of stationarity against the alternative of a unit root 1992Efficient Tests for an Autoregressive Unit Root 1980AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING 1983THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS 1996Fractionally integrated generalized autoregressive conditional heteroskedasticity

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2014Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?

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