There is ample empirical evidence on the presence of structural changes in financial time series. Structural breaks are also shown to contribute to the leptokurtosis of financial returns and explain at least partly the observed persistence of volatility processes. This paper explores whether detecting and taking into account structural breaks in the volatility model can improve upon our Value at Risk forecast. VAR is used by banks as a standard risk measure and is accepted by regulation in setting capital, which makes it an issue for the central bank guarding against systemic risk.
This paper investigates daily BUX returns over the period 1995-2002. The Bai-Perron algorithm found several breaks in the mean and volatility of BUX return. The shift in the level of unconditional mean return around 1997-1998 is likely to be explained by the evolving efficiency of the market, but most of all by the halt of a strong upward trend in the preceding period. Volatility jumped to very high levels due to the Asian and Russian crisis. There were longer lasting shift too, most likely due to increasing trading volume. When in-sample forecasts are evaluated, models with SB dummies outperform the alternative methods. According to the rolling-window estimation and out-of-sample forecast the SB models seem to perform slightly better. However the results are sensitive to the evaluation criteria used, and the choice on the probability level.
JEL classification numbers: G10, G21, C22, C53
Keywords: Structural Break tests, volatility forecasting, Value-at-Risk, backtest