ENERGY PORTFOLIO RISK MANAGEMENT USING VAR MODEL

Abstract

In most circumstances, the degree of risk associated with a specific investment is directly proportionate to the potential future returns. It is difficult for investors, shareholders, and financial managers to assess the overall loss of their asset portfolio in the present environment since standard deviation is inadequate to depict the real total loss. The notion of Value at Risk (VaR) is discussed in this study since it has been demonstrated to be an excellent risk measuring tool in quantifying the complete loss of investment in precise currency that would be borne by the investors over a period of time. A portfolio's greatest probable loss in value over a specific time for a particular confidence interval under normal market conditions is defined as the volatility of a portfolio's value over a set period. This paper compares the COVID-19 crisis to the worldwide financial crisis of 2008 from the standpoint of a retail investor, utilizing a VAR model analysis. In addition, study demonstrates which method of VAR is better to find risk associated with portfolio for the retail investors. The study will anticipate the Value at Risk using a variety of parametric and nonparametric models, as well as explain why business risk assessment and management are critical for financial institutions and retail investors. The three methodologies of Delta Normal, Historical Simulation, and Monte Carlo Simulation are used to measure the VAR for a hypothetical portfolio of stocks, respectively. Finally, the research demonstrates that, when compared to the other two techniques, the Monte Carlo Simulation methodology is the most relevant and adaptable in determining Value at Risk (VAR). According to research, sophisticated VAR type models for predicting future variation are critical for effective energy portfolio risk management. Despite this, there has been a failure to offer a clear picture of the amount of money at risk on behalf of the shareholders or any other stakeholder immediately affected by price changes in specific or multiple energy commodities. As a consequence, risk managers must go a step further and discover the most reliable and accurate technique for precisely monitoring and accurately calculating the overall portfolio value-at-risk (VAR), which, by essence, provides a good evaluation of the whole real amount at risk. Using a strategy that takes into consideration the specific features of the vi energy product transaction is the most effective approach to decrease risk and correctly predict future prospective losses.