VOL. 10, NO 19, OCTOBER, 2015 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2015 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 8648 INDIAN ELECTRICITY MARKET VOLUME AND PRICE CROSS- CORRELATION ANALYSIS Mayukha Pal 1, 2, 3 , P. Madhusudana Rao 2 and P. Manimaran 1 1 C R Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus, Prof. C R Rao Road, Hyderabad, India 2 College of Engineering, Jawaharlal Nehru Technological University, Hyderabad, India 3 India Innovation Center, General Electric Company, Secunderabad, India E-Mail: maran@crraoaimscs.res.in ABSTRACT We apply the multifractal detrended cross-correlation analysis method to investigate the cross-correlation and fractal behavior between the price and volume of the electricity market. For this purpose, we have collected the data from the Indian electricity energy exchange from 1 st April 2012 to 1 st April 2015 with time interval of 1 hour. From the analysis, we observe a cross over near the scale (~ 32) in the fluctuation function, and thus we have calculated the Hurst scaling exponents for the scale <= 32 (short term) and > 32 (long term). The cross-correlation is observed persistent in short term and anti-persistent in long term. The multifractal nature is present in both short and long term and the strength of the multifractality was measured from the calculated singularity spectrum. Keywords: electricity market, non-stationary time series, hurst exponent, multifractal detrended cross-correlation analysis, energy, power data. INTRODUCTION Generation of sufficient electricity to cater demand of the market is essential to the growth of any developing nation. Availability of different source of electricity power generation and grid efficiency plays an important part of demand and supply chain that fuels growth to the industry, employment, and infrastructure. Effective, efficient supply chain and utilization of various natural resources like hydro, thermal (coal, gas, diesel etc), nuclear, renewable (wind, solar etc) etc. plays an important part in electricity generation (Wang et al., 2011), (Ramirez et al., 2009), (Uritskaya et al., 2008), (Uritskaya et al., 2015). For smooth supply chain and to provide competitive business environment, electricity power industry across the world is deregulated and liberalized. Due to deregulation in electricity markets diverse participants are involved like producers, intermediates, speculators, traders, consumers hence the electricity market price and traded volume is the result of many interacting factors combined within complex and nonlinear forms. Unlike any other traded commodities, electricity power cannot be stored hence any un-utilized demand is lost which is the unique feature in this market that drives extreme volatility and large stochastic dynamics into the future electricity price hence forecasting becomes very difficult (Ramirez et al., 2010), (Malo et al., 2009), (Wang et al., 2013), (Norouzzadeh et al., 2007), (McArthur et al., 2013). Also additionally peak demand and supply shortages drive extremely high price volatility due to the complex, non-linear wholesale electricity market dynamics. Hence with all these factor combination, the electricity volume and price market becomes complex. Thus analysis of electricity volume and price data becomes very crucial to understand and retrieve critical information from its underlying dynamics for its exhibited behavior. Accurate forecast of demand in electricity volume and price would bring lot of stabilization in the electricity market with highly improved supply chain vs. the demand to cater the growing market. Hence any short-term volume demand and price forecasts would help the producers effectively plan the production, business efficiently (Fan et al., 2015), (Wang et al., 2012), (Erzgraeber et al., 2008). The forecast study and any future analysis are possible only if the market behavior is well analyzed. Fractal and correlation study has been an efficient tool in analyzing the behavior of complex system. Study of such complex dynamical system through the time series data of the traded market using nonlinear dynamics models are essential to understand the market behavior (Peng et al., 1994), (Kantelhardt et al., 2002). Many studies had undertaken in understanding the electricity power market to study the correlation behavior and multifractal nature of the real world recordings which are in the form of time series so their multifractal properties are extensively investigated (Manimaran et.al., 2005 and refences there in). Similarly, the cross correlation behavior of two non-stationary time series signals were investigated using multifractal detrended cross correlation analysis (MF-X-DFA) approach (Podobnik et.al., 2008), (Zhou, 2008), (Jiang et al., 2011), (Feng et al., 2013), (Pal et al., 2014).