On the economic costs of value at risk forecasts

2003/01/01 by Tatiana Miazhynskaia, Miazhynskaia, Tatiana, Engelbert J. Dockner +3
Economics, Econometrics and Finance · #Financial Risk and Volatility Modeling #Market Dynamics and Volatility #Monetary Policy and Economic Impact

paper · pdf · doi:10.57938/1b7d6bfa-e397-4656-8530-1f7fbb9f2244

openalex publication_date 2003/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23

Abstract

We specify a class of non-linear and non-Gaussian models for which we estimate and forecast the conditional distributions with daily frequency. We use these forecasts to calculate VaR measures for three different equity markets (US, GB and Japan). These forecasts are evaluated on the basis of different statistical performance measures as well as on the basis of their economic costs that go along with the forecasted capital requirements. The results indicate that different performance measures generate different rankings of the models even within one financial market. We also find that for the three markets the improvement in the forecast by non-linear models over linear ones is negligible, while non-gaussian models significantly dominate the gaussian models.

Citations

• ARCH modeling in finance
• Generalized autoregressive conditional heteroskedasticity
• Neural Networks and the Bias/Variance Dilemma

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