EMPIRICAL RESULTS FROM VAR PREDICTION USING PEARSON?S TYPE IV DISTRIBUTION

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2010-09-02
Keywords: 
Risk Management
GARCH
Pearson?s Type IV Distribution
Value-at-Risk
Volatility Forecast
Backtesting
FRM
Abstract: 

Two most important characteristics of equity returns time series data are volatility clustering and non-normality. GARCH model has been widely used to forecast dynamic volatilities and hence has been used for value-at-risk (VaR) estimation. (Bhattacharyya et al 2008) has developed a new VaR estimation model for equity return time series using a combination of the Pearson?s Type IV distribution and the GARCH(1,1) approach which showed superior predictive abilities. This new model was tested on indices of eighteen countries [3] on daily return up to March 1st, 2005. In this project, we replicate the results in [3], and test the model for its predictive power over a more volatile period (i.e. 350 trading days prior to July 18th, 2008). We backtest the validity of the VaR estimations and compare the predictive power of this model over both of the above time periods on indices of eight countries. We discover that the Pearson?s type IV model still remains a good predictive ability during the more volatile period.

Description: 

Research Project (M.B.A.) - Simon Fraser University

Language: 
English
Document type: 
Thesis
Senior supervisor: 
Peter Klein
Department: 
Business Administration
Thesis type: 
Research Project (M.B.A.): Master of Financial Risk Management
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