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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Magyar Nemzeti Bank (The Central Bank of Hungary) in its series MNB Working Papers with number
2004/11.
Find related papers by JEL classification: G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data) G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)