Modelling volatility clustering in electricity price return series for forecasting value at risk

Abstract

SUMMARYModelling of non-stationary time series using regression methodology is challenging. The wavelet transforms can be used to model non-stationary time series having volatility clustering. The traditional risk measure is variance and now a days Value at Risk (VaR) is widely used in finance. In competitive environment, the prices are volatile and price risk forecasting is necessary for the market participants. The forecasting period may be 1 week or higher depending upon the requirement. In this paper, a model is developed for volatility clustering in electricity price return series and its application for forecasting VaR is demonstrated. The first model is using GARCH (1, 1). The VaR of variance rate series, that is worst-case volatility is calculated using variance method using wavelet transform. The model is used to forecast variance rate (volatility) for a sample case of 1-week half-hourly price return series. The second model developed is for forecasting VaR for price return series of 440 days. This model is developed using wavelets via multi-resolution analysis and uses regime-switching technique. The historical data of daily average prices is obtained from 100% pool type New South Wales (NSW), a zonal market of National Electricity Market (NEM), Australia.

Estimating Portfolio Value at Risk in the Electricity Markets Using an Entropy Optimized BEMD Approach

“…Empirical studies using these methods have reported positive performance [14]. Meanwhile, the recently emerging empirical mode decomposition (EMD) takes an empirical, intuitive, direct and self-adaptive data approach as the alternative [15,16].…”

Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

“…Most of current approaches use the multi-resolution analysis capability of wavelet analysis to look into the past price or correlation behaviours in the capital markets [21,22], e.g., it has been used to analyze the risk distribution across differences the scales [23][24][25][26][27][28][29][30]. Some researches such as that by Karandikar et al [31] has gone a step further to estimate VaR with this information taken into account. VaR estimated following the traditional approaches is adjusted by the proportion of risks that corresponds to the investment time horizon [31].…”

“…Some researches such as that by Karandikar et al [31] has gone a step further to estimate VaR with this information taken into account. VaR estimated following the traditional approaches is adjusted by the proportion of risks that corresponds to the investment time horizon [31]. These approaches are unique in that VaRs are tailored to investor's investment profile [24].…”

Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology

“…have recently attracted significant research attention in the risk measurement literature. For example, [12] combined the wavelet analysis and regime switching model to estimate electricity VaR. However, the performance of the wavelet-based approach is constrained by the limited amount of wavelet basis available in the literature.…”