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Estimation Of The Value At Risk Of World Indices Portfolio Using Vector Autoregression Models

Author

Listed:
  • Boyan Lomev

    (Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski)

  • Nikolay Netov

    (Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski)

Abstract

The main objective of this paper is to test whether approaches that model cross-correlation structure of the multivariate data can lead to more accurate estimates of the market risk of a portfolio in comparison with classical methods like empirical Cumulative Distribution Function (CDF) or RiskMetricsTM IGARCH(1,1) process without drift. The data contains daily closing values for S&P 500, DAX and Nikkei 225 Indexes and the period is 2006 – 2022. Forecasts about portfolio with equal weights Value at Risk one month in the future are sequentially calculated starting from 2010 on the basis of all available data up to that moment. The actual quantile (5% and 1%) of the portfolio return is then used as a benchmark for the forecast’s accuracy. Obtained results show that Vector Autoregression Models outperform the other considered methods although they do not directly grasp heavy tails and volatility clustering.

Suggested Citation

  • Boyan Lomev & Nikolay Netov, 2024. "Estimation Of The Value At Risk Of World Indices Portfolio Using Vector Autoregression Models," Yearbook of the Faculty of Economics and Business Administration, Sofia University, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria, vol. 23(1), pages 179-186, June.
    • Handle: RePEc:sko:yrbook:v:23:y:2024:i:1:p:179-186
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