What do the value-at-risk measure and the respective legislative framework really offer to financial stability? Critical views and pro-cyclicality

2020 ◽  
Vol 17 (1) ◽  
pp. 39-60
Author(s):  
Evangelos Vasileiou ◽  
Themistoclis Pantos
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Financial Risk ◽  
Risk Measure ◽  
Counterparty Risk ◽  
Financial Industry ◽  
Historical Simulation ◽  
Statistical Measures ◽  
Legislative Framework

In this paper, we examine how value at risk (VaR) contributes to the financial market's stability. We apply the Guidelines on Risk Measurement and the Calculation of Global Exposure and Counterparty Risk for UCITS of the Committee of European Securities Regulators (CESR 2010) to the main indices of the 12 stock markets of the countries that have used the euro as their official currency since its initial circulation. We show that gaps in the legislative framework give incentives to investment funds to adopt conventional models for the VaR estimation in order to avoid the increased costs that the advanced models involve. For this reason, we apply the commonly used historical simulation VaR (HVaR) model, which is: (i) taught at most finance classes; (ii) widely applied in the financial industry; and (iii) accepted by CESR (2010). The empirical evidence shows the HVaR does not really contribute to financial stability, and the legislative framework does not offer the appropriate guidance. The HVaR model is not representative of the real financial risk, and does not give any signal for trends in the near future. The HVaR is absolutely backward-looking and this increases the stock market's overreaction. The fact that the suggested confidence level in CESR (2010) is set at 99 percent leads to hidden pro-cyclicality. Scholars and researchers should focus on issues such as the abovementioned, otherwise the VaR estimations will become, sooner or later, just a formality, and such conventional statistical measures rarely contribute to financial stability.

What do the value-at-risk measure and the respective legislative framework really offer to financial stability? Critical views and pro-cyclicality

2020 ◽  
Vol 17 (1) ◽  
pp. 39-60
Author(s):  
Evangelos Vasileiou ◽  
Themistoclis Pantos
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Financial Risk ◽  
Risk Measure ◽  
Counterparty Risk ◽  
Financial Industry ◽  
Historical Simulation ◽  
Statistical Measures ◽  
Legislative Framework

In this paper, we examine how value at risk (VaR) contributes to the financial market's stability. We apply the Guidelines on Risk Measurement and the Calculation of Global Exposure and Counterparty Risk for UCITS of the Committee of European Securities Regulators (CESR 2010) to the main indices of the 12 stock markets of the countries that have used the euro as their official currency since its initial circulation. We show that gaps in the legislative framework give incentives to investment funds to adopt conventional models for the VaR estimation in order to avoid the increased costs that the advanced models involve. For this reason, we apply the commonly used historical simulation VaR (HVaR) model, which is: (i) taught at most finance classes; (ii) widely applied in the financial industry; and (iii) accepted by CESR (2010). The empirical evidence shows the HVaR does not really contribute to financial stability, and the legislative framework does not offer the appropriate guidance. The HVaR model is not representative of the real financial risk, and does not give any signal for trends in the near future. The HVaR is absolutely backward-looking and this increases the stock market's overreaction. The fact that the suggested confidence level in CESR (2010) is set at 99 percent leads to hidden pro-cyclicality. Scholars and researchers should focus on issues such as the abovementioned, otherwise the VaR estimations will become, sooner or later, just a formality, and such conventional statistical measures rarely contribute to financial stability.

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

2021 ◽  
Vol 14 (11) ◽  
pp. 540
Author(s):  
Eyden Samunderu ◽  
Yvonne T. Murahwa
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Hedge Funds ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Extensive Literature ◽  
Data Set ◽  
Normally Distributed

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.

scholarly journals Research on the Effect of Chinese Margin Trading on Market Risk: Based on GJR Model and Filtered Historical Simulation

2017 ◽  
Vol 4 (4) ◽  
pp. 84 ◽  
Author(s):  
Lan-Ya Ma ◽  
Zi-Yu Li
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Risk ◽  
Parametric Method ◽  
Leverage Effect ◽  
Financial Risk Management ◽  
Historical Simulation ◽  
The Difference ◽  
Gjr Model ◽  
Sample Time

In this paper, we address the issue that the financial institutes need to identify the risk of margin trading, and we analyze the volatility and value at risk of China’s Shanghai-Shenzhen 300 Index before and since the inception of margin trading policy. We first analyze the statistical attributes of the logarithmic return series. Then we build the GJR-GARCH to model the difference of volatility and leverage effect of the two sample time series. After that, we calculate the dynamic value at risk based on the parametric method. Moreover, we apply the filtered historical simulation with the help of Bootstrap technique to obtain the pathway of return and finally calculate the value at risk under the two circumstances. In the end, we propose some reasonable policies to financial risk management department.

An Improved Historical Simulation Method to Estimate the Amount of Refined Oil Retail Value at Risk VaR

2013 ◽  
Vol 734-737 ◽  
pp. 1711-1718
Author(s):  
Yong Tao Wan ◽  
Zhi Gang Zhang ◽  
Lu Tao Zhao
Keyword(s):  
At Risk ◽  
Crude Oil ◽  
Value At Risk ◽  
Financial Risk ◽  
Rapid Development ◽  
Simulation Method ◽  
Support Vector ◽  
Refined Oil ◽  
Historical Simulation ◽  
Oil Market

The international crude oil market is complicated in itself and with the rapid development of China in recent years, the dramatic changes of the international crude oil market have brought some risk to the security of Chinas oil market and the economic development of China. Value at risk (VaR), an effective measurement of financial risk, can be used to assess the risk of refined oil retail sales as well. However, VaR, as a model that can be applied to complicated nonlinear data, has not yet been widely researched. Therefore, an improved Historical Simulation Approach, historical stimulation of genetic algorithm to parameters selection of support vector machine, HSGA-SVMF, in this paper, is proposed, which is based on an approach the historical simulation with ARMA forecasts, HSAF. By comparing it with the HSAF and HSGA-SVMF approach, this paper gives evidence to show that HSGA-SVMF has a more effective forecasting power in the field of amount of refined oil.

scholarly journals METODE HISTORIS UNTUK PERHITUNGAN VALUE AT RISK PADA MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDACITY IN MEAN

2016 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Alfi Reny Kusumaningtyas ◽  
Abdul Aziz
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Parametric Method ◽  
Simulation Method ◽  
Financial Industry ◽  
Main Method ◽  
Historical Simulation ◽  
Historical Method ◽  
Implementation Model ◽  
Main Motive

Investment is a commitment of the placement of the data on an object or a few investments with expectations will benefit in the future. The main motive is to seek investment gain or profit in a certain amount, but behind the good side there is one side that can harm or the risk of, for it required a measurement of risk where methods of value at risk (VaR) is very popular is widely used by the financial industry worldwide. Three main method on calculation of VaR historical method, parametric method and Monte Carlo method. So, the selected calculation of VaR GARCH-M model with historical simulation method on Bank Mandiri Tbk closing stock in 2005-2010. This research aims to know the calculation of VaR model GARCH-M through the historical method and implementation model GARCH-M on the computation of VaR via simulation on closing stock Bank Mandiri Tbk. Historical method approach is a model calculation of VaR is determined by the value of the past (historical) or return generated by simulation (repetition) of data used. The measures undertaken that explains the historical simulation method VaR models in the estimation of GARCH-M with a normal distribution, then apply GARCH-M in case of loss obtained by investors after investing with the help of Minitab software, E-views software and Matlab software.

The risk analysis of Bitcoin and major currencies: value at risk approach

2019 ◽  
Vol 22 (1) ◽  
pp. 38-52 ◽  
Author(s):  
Umut Uyar ◽  
Ibrahim Korkmaz Kahraman
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Value At Risk ◽  
Risk Measure ◽  
Risk Level ◽  
Portfolio Risk ◽  
Content Type ◽  
Historical Simulation ◽  
Digital Currency ◽  
Risk Approach

Purpose This study aims to compare investors of major conventional currencies and Bitcoin (BTC) investors by using the value at risk (VaR) method common risk measure. Design/methodology/approach The paper used a risk analysis named as VaR. The analysis has various computations that Historical Simulation and Monte Carlo Simulation methods were used for this paper. Findings Findings of the analysis are assessed in two different aspects of singular currency risk and portfolios built. First, BTC is found to be significantly risky with respect to the major currencies; and it is six times riskier than the singular most risky currency. Second, in terms of inclusion of BTC into a portfolio, which equally weights all currencies, it elevates overall portfolio risk by 98 per cent. Practical implications In spite of the remarkable risk level, it could be considered that investors are desirous of making an investment on BTC could mitigate their overall exposed risk relatively by building a portfolio. Originality/value The paper questions the risk level of Bitcoin, which is a digital currency. BTC, a matter of debate in the contemporary period, is seen as a digital currency free from control or supervision of a regulatory board. With the comparison of major currencies and BTC shows that how could be risky of a financial instrument without regulations. However, there is some advice for investors who would like to invest digital currencies despite the risk level in this study.

Extended Robust Support Vector Machine Based on Financial Risk Minimization

2014 ◽  
Vol 26 (11) ◽  
pp. 2541-2569 ◽  
Author(s):  
Akiko Takeda ◽  
Shuhei Fujiwara ◽  
Takafumi Kanamori
Keyword(s):  
At Risk ◽  
Support Vector Machine ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Support Vector ◽  
Risk Minimization ◽  
Hinge Loss ◽  
Margin Distribution

Financial risk measures have been used recently in machine learning. For example, [Formula: see text]-support vector machine ([Formula: see text]-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than [Formula: see text]-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM’s possibility of achieving a better prediction performance with proper parameter setting.

scholarly journals Financial Risk Quantification of Indian Agro-Commodities using Value At Risk

2019 ◽  
Vol 8 (6) ◽  
pp. 5138-5144
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Risk ◽  
Commodity Prices ◽  
Simulation Method ◽  
Historical Trends ◽  
Spot Prices ◽  
Historical Simulation ◽  
Secondary Sources ◽  
Guar Seed

Indian commodity traders are exposed to various risks like price risk, market risk, financial risk, credit risk, etc. To understand the risk resulting in the financial impact, this paper attempts to assess the historical trends of commodity prices and probability of loss occurrence in the commodity invested. The present study analyses five Indian agro commodities namely, Almond, Cardamom, Cotton, Guar Seed and Wheat using the data collected from secondary sources like Multi Commodity Exchange (MCX), Securities Exchange Board of India (SEBI) etc. This paper uses the Historical Simulation method for the calculation of Value at Risk (VaR) by considering spot prices of the commodities on MCX for a five year period (2013-2018). It is established that Value at Risk (VaR) is a relevant measure to arrive at risk which is useful for the commodity traders to estimate the financial risk and thus control the risk exposure

scholarly journals The Improved Value-at-Risk for Heteroscedastic Processes and Their Coverage Probability

2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Khreshna Syuhada
Keyword(s):  
At Risk ◽  
Coverage Probability ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measure ◽  
Type Model ◽  
Arch Model ◽  
Svar Model ◽  
Volatility Model ◽  
Do So

A risk measure commonly used in financial risk management, namely, Value-at-Risk (VaR), is studied. In particular, we find a VaR forecast for heteroscedastic processes such that its (conditional) coverage probability is close to the nominal. To do so, we pay attention to the effect of estimator variability such as asymptotic bias and mean square error. Numerical analysis is carried out to illustrate this calculation for the Autoregressive Conditional Heteroscedastic (ARCH) model, an observable volatility type model. In comparison, we find VaR for the latent volatility model i.e., the Stochastic Volatility Autoregressive (SVAR) model. It is found that the effect of estimator variability is significant to obtain VaR forecast with better coverage. In addition, we may only be able to assess unconditional coverage probability for VaR forecast of the SVAR model. This is due to the fact that the volatility process of the model is unobservable.

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