Value-at-risk (VAR) analysis of the UK banking stocks

AbstractFor the purpose of Value-at-Risk (VaR) analysis, a model for the return distribution is important because it describes the potential behavior of a financial security in the future. What is primarily, is the behavior in the tail of the distribution since VaR analysis deals with extreme market situations. We analyze the extension of the normal distribution function to allow for fatter tails and for time-varying volatility. Equally important to the distribution function are the associated parameter values. We argue that parameter uncertainty leads to uncertainty in the reported VaR estimates. There is a tradeoff between more complex tail-behavior and this uncertainty. The “best estimate”-VaR should be adjusted to take account of the uncertainty in the VaR. Finally, we consider the VaR forecast for a portfolio of securities. We propose a method to treat the modeling in a univariate, rather than a multivariate, framework. Such a choice allows us to reduce parameter uncertainty and to model directly the relevant variable.

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An evaluation and comparison of Value at Risk and Expected Shortfall

Investment Management and Financial Innovations ◽

10.21511/imfi.15(4).2018.02 ◽

2018 ◽

Vol 15 (4) ◽

pp. 17-34 ◽

Cited By ~ 1

Author(s):

Tom Burdorf ◽

Gary van Vuuren

Keyword(s):

At Risk ◽

Monte Carlo ◽

Emerging Economies ◽

Value At Risk ◽

Risk Measures ◽

Risk Measure ◽

Expected Shortfall ◽

Significance Level ◽

Tail Conditional Expectation ◽

The Uk

As a risk measure, Value at Risk (VaR) is neither sub-additive nor coherent. These drawbacks have coerced regulatory authorities to introduce and mandate Expected Shortfall (ES) as a mainstream regulatory risk management metric. VaR is, however, still needed to estimate the tail conditional expectation (the ES): the average of losses that are greater than the VaR at a significance level These two risk measures behave quite differently during growth and recession periods in developed and emerging economies. Using equity portfolios assembled from securities of the banking and retail sectors in the UK and South Africa, historical, variance-covariance and Monte Carlo approaches are used to determine VaR (and hence ES). The results are back-tested and compared, and normality assumptions are tested. Key findings are that the results of the variance covariance and the Monte Carlo approach are more consistent in all environments in comparison to the historical outcomes regardless of the equity portfolio regarded. The industries and periods analysed influenced the accuracy of the risk measures; the different economies did not.

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ANALISIS RISIKO INVESTASI DEPOSITO MUDHARABAH (Studi pada Bank Syariah Mandiri Tahun 2013-2015)

JURNAL ILMIAH EKONOMI ISLAM ◽

10.29040/jiei.v2i02.45 ◽

2016 ◽

Vol 2 (02) ◽

Author(s):

Ulul Azmi Mustofa ◽

Iin Emy Prastiwi

Keyword(s):

At Risk ◽

Confidence Level ◽

Value At Risk ◽

Financial Risk ◽

Investment Risk ◽

Financial Report ◽

Net Return ◽

Var Analysis ◽

Maximum Loss ◽

The Mean

Value at Risk is defined as an estimate of the maximum loss of an investment over a given period and a given confidence level. The purposes of this research is to understand the size of financial risk and net return of mudharabah deposit on Islamic bank using Value at Risk (VaR) approach. Objects of this research is quarterly financial report of Bank Syariah Mandiri, for three years, 2013-2015. VaR analysis shows that investment mudaraba deposits at Bank Syariah Mandiri as measured by VaR approach to investment risk (VaRmean) in 2013 amounted to 0.30%, and a net return of 0.54%, in 2014 the mean VaR 0.18%, and the net return 0.62%, in 2015 the mean VaR of 0.25%, and a net return of 0.55%. Keywords : Value at Risk ( VaR ), risk, net return, mudharabah deposit

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Value at risk (VaR) analysis for fat tails and long memory in returns

Eurasian Economic Review ◽

10.1007/s40822-017-0067-z ◽

2017 ◽

Vol 7 (2) ◽

pp. 215-230 ◽

Cited By ~ 4

Author(s):

Samet Günay

Keyword(s):

At Risk ◽

Long Memory ◽

Value At Risk ◽

Fat Tails ◽

Var Analysis

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Economic evaluation of mechanical harvesting of lemons

Outlook on Agriculture ◽

10.1177/0030727018762657 ◽

2018 ◽

Vol 47 (1) ◽

pp. 44-50 ◽

Cited By ~ 3

Author(s):

JM Brotons-Martínez ◽

B Martin-Gorriz ◽

A Torregrosa ◽

I Porras

Keyword(s):

At Risk ◽

Monte Carlo ◽

Economic Evaluation ◽

Value At Risk ◽

Experimental Conditions ◽

Monte Carlo Approach ◽

Var Analysis ◽

Current Availability ◽

The Cost ◽

Farmer Income

Several hypotheses regarding hand and mechanical harvesting have been analysed, in order to estimate the economic possibilities for the mechanical harvesting of lemons taking into account the current availability of technology. We considered several detachment options under experimental conditions; only yellow detachment has been considered for mechanical harvesting, because the sensitivity to the impacts is lower and mechanical detachment was high (80%). Price changes throughout the season were also considered. Total harvest cost is an average of the cost of mechanical harvesting (80%) and the cost of manually harvesting remaining fruit (20%), plus the cost of handling the mechanical harvested fraction. This cost ranges between 0.031 € kg−1 and 0.058 € kg−1 for outputs between 20 t ha−1 and 60 t ha−1, respectively, which is always lower than harvesting by hand (0.065 € kg−1). A Monte Carlo approach was used to study the sensitivity of the results, and value at risk (VaR) was calculated. The analysis showed that the mechanical harvesting margin is 0.020 € kg−1 higher than the hand harvesting margin and the output dispersion is higher in March. The VaR analysis showed that at 10%, there was no risk that the hand margin is higher than the mechanical margin and at 5%, the risk is very low and only for March harvesting. Mechanical harvesting represents a good economic option compared to hand harvesting, since it can increase farmer income by between 400 € ha−1and 1200 € ha−1.

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Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis

Operations Research Perspectives ◽

10.1016/j.orp.2017.02.001 ◽

2017 ◽

Vol 4 ◽

pp. 21-28 ◽

Cited By ~ 7

Author(s):

Shokoofeh Banihashemi ◽

Sarah Navidi

Keyword(s):

At Risk ◽

Performance Evaluation ◽

Value At Risk ◽

Portfolio Performance ◽

Parametric Methods ◽

Var Analysis ◽

Non Parametric

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VALUE AT RISK OF A BANK'S BALANCE SHEET

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024999000042 ◽

1999 ◽

Vol 02 (01) ◽

pp. 43-58

Author(s):

THOMAS HO ◽

MARK ABBOTT ◽

ALLEN ABRAHAMSON

Keyword(s):

At Risk ◽

Value At Risk ◽

Risk Measures ◽

Balance Sheet ◽

Var Analysis ◽

Bank Managers

Through the application of a VaR analysis to the balance sheet of a hypothetical bank this paper will address several issues important to bank managers. We will establish which balance sheet accounts lend themselves to meaningful VaR measures and the kind of information needed for input to these measures. We explain how depositor and borrower behaviors are captured in the risk measures. We also address the accuracy of the measures, and how the bank can use the VaR information for actionable decisions.

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