Risky Risk Measures: A Note on Underestimating Financial Risk Under the Normal Assumption

Goodfellow, Christiane; Salm, Christian A.

doi:10.12775/cjfa.2016.017

Cited by 1 publication

(2 citation statements)

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“…It is also a supplement to the VaR LPM and applies specifically to the left tale of a distribution of the VaR. It can be the average across all negative deviations from the VaR or the variance of the negative deviations of a pre-defined VaR It is, therefore, a method that places heavier weight on larger deviations from the current VaR in comparison to the smaller deviations (Goodfellow and Salm 2016). An investor can select or set the risk-free rates, and by selecting the degree of the moment, one can specify the risk levels that suit the portfolio or risk needs.…”

Section: Suggested Alternatives For Non-normal Distributions-unconditional Distributionsmentioning

confidence: 99%

“…An investor can select or set the risk-free rates, and by selecting the degree of the moment, one can specify the risk levels that suit the portfolio or risk needs. Goodfellow and Salm (2016) compared three different risk measures based on the same stock return data, the portfolio variance as in the seminal works of Markowitz (1952) and the VaR based on t copula. They concluded that normal assumption substantially underestimates the risk faced by an organization, and therefore, they discredited the use of risk measures based on normal assumption.…”

Section: Suggested Alternatives For Non-normal Distributions-unconditional Distributionsmentioning

confidence: 99%

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Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Samunderu

Murahwa²

2021

JRFM

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Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

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Section: Suggested Alternatives For Non-normal Distributions-unconditional Distributionsmentioning

confidence: 99%

Section: Suggested Alternatives For Non-normal Distributions-unconditional Distributionsmentioning

confidence: 99%

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Samunderu

Murahwa²

2021

JRFM

View full text Add to dashboard Cite

show abstract

Risky Risk Measures: A Note on Underestimating Financial Risk Under the Normal Assumption

Cited by 1 publication

References 12 publications

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

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