Nour ALSHAMALI, Khuloud M. ALAWADHI, Mansour ALSHAMALI, Fatemah M. BEHBEHANI

1814

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

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,

PDF

___

  • Aniūnas, P., Nedzveckas, J. and Krušinskas, R. (2009). Variance–covariance risk value model for currency market. Engineering economics, 61(1), 1-5.
  • Abidin, S.N.Z. and Jaffar, M.M. (2012). A review on Geometric Brownian Motion in forecasting the share prices in Bursa Malaysia. World Applied Sciences Journal, 17(1), 82-93.
  • Barone‐Adesi, G., Giannopoulos, K. and Vosper, L. (1999). VaR without correlations for portfolios of derivative securities. Journal of Futures Markets, 19(5), 583-602.
  • Barreto, H. (2015). Why Excel?. The Journal of Economic Education, 46(3), 300-309.
  • Bollerslev, T., Engle, R.F. and Wooldridge, J.M. (1988). A capital asset pricing model with time-varying covariances. Journal of political Economy, 96(1), 116-131.
  • Brown, R. and Klingenberg, B. (2015). Real estate risk: Heavy tail modelling using Excel. Journal of Property Investment & Finance, 33(4), 393- 407.
  • Cabedo, J.D. and Moya, I. (2003). Estimating oil price ‘Value at Risk’ using the historical simulation approach. Energy economics, 25(3), 239- 253.
  • Cárdenas, J.D., Fruchard, E., Picron, J.F., Reyes, C., Walters, K. and Yang, W. (2001). Monte Carlo within a day. In Quantitative Analysis In Financial Markets: Collected Papers of the New York University Mathematical Finance Seminar, Vol.II, 335-345.
  • Cheung, Y.H. and Powell, R.J. (2012). Anybody can do value at risk: a teaching study using parametric computation and Monte Carlo simulation. Australasian Accounting, Business and Finance Journal, 6(5), 101-118.
  • Čorkalo, Š. (2011). Comparison of value at risk approaches on a stock portfolio. Croatian Operational Research Review, 2(1), 81-90.
  • Das, N.M. and Rout, B.S. (2020). Impact of COVID-19 on Market Risk: Appraisal with Value-at-risk Models. The Indian Economic Journal, 68(3), 396-416.
  • Dionne, G. (2013). Risk management: History, definition, and critique. Risk management and insurance review, 16(2), 147-166.
  • Engle, R.F. and Kroner, K.F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(1), 122-150.
  • Estember, R.D. and Maraña, M.J.R. (2016). March. Forecasting of Stock Prices Using Brownian Motion–Monte Carlo Simulation.
  • In Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management. [online] Kuala Lumpur, Malaysia, p.2. Available at: [Accessed 19 September 2021].
  • Gaglianone, W.P., Lima, L.R., Linton, O. and Smith, D.R. (2011). Evaluating value-at-risk models via quantile regression. Journal of Business & Economic Statistics, 29(1), 150-160.
  • Hallerbach, W.G. (1999). Decomposing portfolio value-at-risk: A general analysis (No. 99-034/2). Tinbergen Institute Discussion Paper.
  • Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Economic policy review, 2(1), 1-32.
  • Ho, T.S., Chen, M.Z. and Eng, F.H. (1996). VAR analytics: Portfolio structure, key rate convexities, and VAR betas. Journal of portfolio management, 23(1), 89-101.
  • Homem-de-Mello, T. and Bayraksan, G. (2014). Monte Carlo sampling-based methods for stochastic optimization. Surveys in Operations Research and Management Science, 19(1), 56-85.
  • Hull, J. and White, A. (1998). Incorporating volatility updating into the historical simulation method for value-at-risk. Journal of Risk, 1(1), 5- 19.
  • Frame, W.S., Fuster, A., Tracy, J. and Vickery, J. (2015). The rescue of fannie mae and freddie mac. Journal of Economic Perspectives, 29(2), 25-52.
  • Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M.C. and Siering, M. (2014). Bitcoin-asset or currency? Revealing Users' Hidden Intentions.
  • In: Twenty Second European Conference on Information Systems. [online] pp.2-14. Available at: [Accessed 19 September 2021].
  • Kroner, K.F. and Ng, V.K. (1998). Modeling asymmetric comovements of asset returns. The review of financial studies, 11(4), 817-844.
  • Jorion, P. (1996). Risk2: Measuring the risk in value at risk. Financial analysts journal, 52(6), 47-56.
  • Linsmeier, T.J. and Pearson, N.D. (2000). Value at risk. Financial Analysts Journal, 56(2), 47-67.
  • Löeffler, G. and Posch, P.N. (2011). Credit risk modeling using Excel and VBA. John Wiley & Sons.
  • Lopez, J.A. and Walter, C.A. (2000). Evaluating covariance matrix forecasts in a value-at-risk framework. FRB of San Francisco Working Paper, (2000-21).
  • Lucas, A. (2000). A note on optimal estimation from a risk-management perspective under possibly misspecified tail behavior. Journal of Business & Economic Statistics, 18(1), 31-39.
  • Mason, A.J. (2013). SolverStudio: A new tool for better optimisation and simulation modelling in Excel. INFORMS Transactions on Education, 14(1), 45-52.
  • ONS. (2020). Early assessment of the impact of the coronavirus pandemic on the UK’s financial accounts - Office for National Statistics. [online] Ons.gov.uk. Available at: [Accessed 19 September 2021].
  • Pérignon, C. and Smith, D.R. (2010). Diversification and value-at-risk. Journal of Banking & Finance, 34(1), 55-66.
  • Pritsker, M. (1997). Evaluating value at risk methodologies: accuracy versus computational time. Journal of Financial Services Research, 12(2), 201-242.
  • Rockafellar, R.T. and Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of risk, 2, 21-42.
  • Rudd, K. (2009). The global financial crisis. Monthly, The, Feb., 20-29.
  • Sarma, M., Thomas, S. and Shah, A. (2003). Selection of Value‐at‐Risk models. Journal of Forecasting, 22(4), 337-358.
  • Saunders, A. and Allen, L. (2010). Credit risk management in and out of the financial crisis: new approaches to value at risk and other paradigms (Vol. 528). John Wiley & Sons.
  • Stambaugh, F. (1996). Risk and value at risk. European Management Journal, 14(6), 612-621.
  • Strong, R.A., Steiger, N.M. and Wilson, J.R. (2009). December. Introduction to financial risk assessment using Monte Carlo simulation. In Proceedings of the 2009 Winter Simulation Conference (WSC), 99-11, IEEE.
  • Swamy, V. (2014). Testing the interrelatedness of banking stability measures. Journal of Financial Economic Policy, 6(1), 25-45
  • Tracey, M. (2007). A VAR analysis of the effects of macroeconomic shocks on banking sector loan quality in Jamaica. Available from internet: http://boj. org. jm/uploads/pdf/papers_pamphlets/papers_pamphlets_A_VAR_Analysis_of_ the_Effects_of_Macroeconomic_Shocks_on_Banking_Sector_Loan_Quality. pdf.
  • Woods, M., Dowd, K. and Humphrey, C. (2008). The value of risk reporting: a critical analysis of value-at-risk disclosures in the banking sector. International Journal of Financial Services Management, 3(1), 45-64.
Journal of Economics Finance and Accounting-Cover
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2014
  • Yayıncı: PressAcademia
Arşiv
Sayıdaki Diğer Makaleler

FİNANSAL PERFORMANS TAHMİNİNDE PROPHET MODELİ: İMALAT SEKTÖRÜ UYGULAMASI

Ali YURTTABİR, İlker KIYMETLİ ŞEN

Nour ALSHAMALI, Khuloud M. ALAWADHI, Mansour ALSHAMALI, Fatemah M. BEHBEHANI

Muhanned OBEIDAT, Ahmad TARAWNEH, Mohammad KHATAİBEH, Ghassan OMET

Melike Betul TAVSANLI, Tarkan HAMLACI

GERÇEĞE UYGUN DEĞER İLE KAZANÇ YÖNETİMİ ARASINDAKİ İLİŞKİNİN LİTERATÜR ANALİZİ İLE İNCELENMESİ

Deniz OZBAY