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Risk management in the electric power industry involves measuring the risk for all instruments owned by a company. The value of many of these instruments depends directly on electricity prices. In theory, the wholesale price in a real-time market should reflect the short-run marginal cost. However, most markets are not perfectly competitive, therefore by understanding the degree of correlation between price and physical drivers, electric traders and consumers can manage their risk more effectively and efficiently. Market data from two power-pool architectures, both pre-2003 ISO-NE and Australia's NEM, have been studied. The dynamic character of electricity price is mean-reverting, and consists of intra-day and weekly variations, seasonal fluctuations, and instant jumps. Parts of them are affected by load demands. Hourly signals on both price and load are divided into deterministic and random components with a discrete fourier transform algorithm. Next, the real-time price-load relationship for periodic and random signals is examined. In addition, time-varying volatility models are constructed on random price and random load with the GARCH (1,1) model, and the correlation between them analysed. Volatility plays a critical role on evaluating option pricing and risk management.