VALUE-AT-RISK (VAR) ANALYSIS OF THE UK BANKING STOCKS

Abstract

Purpose. COVID-19's spread and worldwide efforts to contain it are having a significant influence on UK economic activity. Investor concerns about the coronavirus pandemic intensified, resulting in a decline in the value of listed shares and heightened market volatility. In this context, it is interesting to look into the considerable banking stocks in the UK to assess the risk of an investment over a set amount of time. The study's primary goal is to apply analytical and simulation VaR methodologies to five UK banking stocks, which has never been done before in the literature. Methodology. A quantitative research design focused on data synthesis was adopted for this study. Specifically, we conducted a quantitative (VaR) analysis of five UK banking stocks, including HSBC Holdings Plc (HSBA.L), Barclays Plc (BARC.L), Standard Chartered Plc (STAN.L), Llyods Banking Group Plc (LLOY.L), and NatWest Group Plc (NWG.L), to estimate the risk of an investment portfolio. In addition to a historical VaR simulation and the variance-covariance method, we used a Monte Carlo simulation, following the GBM approach, to predict probable investment loss. Findings. Results show that the high magnitude of VaR would be primarily due to a rise in the confidence interval (i.e., higher VaR at 99% than 95%). Since we made no distributional assumptions, the predicted loss based on historical simulation is smaller than the other two methods. The scenarios used in VaR computation are confined to those found in the historical sample. Returns do not always follow a normal distribution in the variance-covariance approach, especially during times of crisis, causing variances and covariances to change over time. The assumption of a completely normal distribution cannot be applied to the Monte-Carlo approach. Conclusion. This paper proposes a paradigm for analyzing portfolio performance using VaR analysis. Based on data for five UK banking equities, we revealed that the portfolio was at high risk at the start of the pandemic. The value of measuring a portfolio's VaR over time lies in both the speed with which a change in the risk profile is identified and the reflective process of analyzing why. A limitation of this research, however, is that it did not identify the maximum loss.

Keywords

• VaR Analysis
• historical simulation
• variance-covariance
• Monte-Carlo simulation
• COVID-19 pandemic

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