Wavelet Denoised Value at Risk Estimate
Xie Chi, Kaijian He · 2006
With the deregulation movement spreading across global electricity market, investors are facing increasing level of price volatility and higher risks. As the proper measurement and management of risks are crucial to both investors and government regulators, this paper attempts to measure risks in the electricity market using value at risk (VaR) theory. To estimate VaR at higher accuracy and reliability, this paper proposes wavelet denoised value at risk (WDNVaR) estimates. Empirical studies based on the traditional ARMA-GARCH approach and the proposed WDNVaR approach are conducted in three Australian electricity markets. Performances of both approaches have been tested and compared using Kupiec backtesting procedures. Experiment results confirm that WDNVaR improves the accuracy and reliability of VaR estimates over traditional ARMA-GARCH approach significantly, which results from its capability to clean up the data and alleviate distortions introduced by outliers