Abstract
Abstract In this paper, the CAViaR model was used to measure the HS300, HSI and N225 risk levels and result showed that CAViaR model was able to measure the sample risk value at the 5% confidence level. However, at the 1% confidence level, the CAViaR model severely underestimated the sample risk value. In order to solve this question, this paper introduces the POT model and CAViaR model to construct the extreme risk measure model based on POT-CAViaR model and the test shows that the improved model can effectively improve the accuracy of extreme risk prediction and the model validity is strengthened.
Highlights
The value at risk (VaR) emerged as the times required in the 1990s to deal with the financial market crisis like the Wall Street stock market crushed in 1987 and European Monetary System collapsed in 1992
To test the effect of POT-CAViaR model, this paper adopts the posteriori test method proposed by Kupiec, the basic concept of which is: to compare the estimated value and actual value measured by the model; it fails if it is larger than the actual loss, the failure rate can be obtained by dividing the total days of observation by the total days of failure and comparing it with the preset VaR; the closer it is to the actual VaR, the better the effect is
Considering that the favorable characteristics of distribution shape and parameter needn t be assumed in the quantile regression model, in this paper the CAViaR model is adopted firstly to measure the risk levels of HS300, HSI and Nikkei225 Index (N225); it turns out that under the 5% significance level, the CAViaR model can effectively measure the risk value of samples while under the 1% significance level, the risk value of samples have been seriously underestimated by the CAViaR model
Summary
The value at risk (VaR) emerged as the times required in the 1990s to deal with the financial market crisis like the Wall Street stock market crushed in 1987 and European Monetary System collapsed in 1992. The primary weakness of this method is the distribution of the yield residual to be assumed in advance [5] Both the GARCH and ARCH models are assumed as in normal distribution at the very beginning; afterwards, taking the leptokurtosis and fat-tail characteristics of financial income series into consideration, i.e. to be replaced by Student-t distribution, is widely accepted as effective [6, 7]. There emerged a new method of measuring the financial risk through quantile regression in the recent years This method has been deeply researched by scholars at home and abroad as it considers no distribution of residual sample at the measurement of risk while reflecting the retail characteristics to some extent, which has provided a satisfying statistical method for fitting the financial data with the leptokurtosis and fat-tail characteristics. The innovation of the article is mainly reflected in the following two aspects: first, in order to avoid the assumption of the overall distribution of the sample and fully reflect the characteristics of the tail of the sample, a risk measurement model based on the CAViaR-POT model is constructed; secondly, the application of CSI 300 index, Hang Seng index and N225 index test the effect of the model
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