The Effects of Risk Modelling: Assessing Value-at-Risk Accuracy
Keywords:
Backtesting, Value-at-Risk, Volatility ModellingAbstract
This study examines Value-at-Risk (VaR) models that are integrated with several volatility representations to estimate the market risk for seven nonfnancial sectors traded on the frst board of the Malaysian stock exchange. In a sample that spanned 19 years from1993 until 2012 for construction, consumer product, industrial product, plantation, property, trade and services and mining sectors, the expected maximum losses are quantifed at 95% confdence level. For accuracy determination, assessments using Kupiec test and Christoffersen test have provided evidence that almost every model are found to be accurate for all sets of occurrence. However, using the Lopez test which takes into consideration the magnitude of the impact of exceptions, the most accurate model is the VaR which is integrated with GARCHt. This study found that fat tails and asymmetries are important issues that need to be considered when estimating VaR in managing financial risks.
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