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An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange

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  • Vesna Bucevska

    (Faculty of Economics, University “Ss. Cyril and Methodius”, Skopje, Republic of Macedonia)

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    Abstract

    Background: In light of the latest global financial crisis and the ongoing sovereign debt crisis, accurate measuring of market losses has become a very current issue. One of the most popular risk measures is Value-at-Risk (VaR). Objectives: Our paper has two main purposes. The first is to test the relative performance of selected GARCH-type models in terms of their ability of delivering volatility estimates. The second one is to contribute to extend the very scarce empirical research on VaR estimation in emerging financial markets. Methods/Approach: Using the daily returns of the Macedonian stock exchange index-MBI 10, we have tested the performance of the symmetric GARCH (1,1) and the GARCH-M model as well as of the asymmetric EGARCH (1,1) model, the GARCH-GJR model and the APARCH (1,1) model with different residual distributions. Results: The most adequate GARCH family models for estimating volatility in the Macedonian stock market are the asymmetric EGARCH model with Student’s t-distribution, the EGARCH model with normal distribution and the GARCH-GJR model. Conclusion: The econometric estimation of VaR is related to the chosen GARCH model. The obtained findings bear important implications regarding VaR estimation in turbulent times that have to be addressed by investors in emerging capital markets.

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    Bibliographic Info

    Article provided by Society for Promotion of Business Information Technology (BIT) in its journal Business Systems Research.

    Volume (Year): 4 (2013)
    Issue (Month): 1 ()
    Pages: 49-64

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    Handle: RePEc:bit:bsrysr:v:4:y:2013:i:1:p:49-64

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    Postal: Udruga BIT, Faculty of Economics and Business, Trg J. F. Kennedy 6, 10000 Zagreb, Croatia.
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    Keywords: Value-at-Risk; GARCH models; forecasting volatility; financial crisis; Macedonia;

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