scholarly journals The Accuracy of Risk Measurement Models on Bitcoin Market during COVID-19 Pandemic

Risks ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 222
Author(s):  
Danai Likitratcharoen ◽  
Nopadon Kronprasert ◽  
Karawan Wiwattanalamphong ◽  
Chakrin Pinmanee
Keyword(s):  
Value At Risk ◽  
Economic Recession ◽  
Statistical Testing ◽  
Var Model ◽  
Var Models ◽  
Independence Test ◽  
Historical Simulation ◽  
Confidence Levels ◽  
Measurement Models ◽  
A Value

Since late 2019, during one of the largest pandemics in history, COVID-19, global economic recession has continued. Therefore, investors seek an alternative investment that generates profits during this financially risky situation. Cryptocurrency, such as Bitcoin, has become a new currency tool for speculators and investors, and it is expected to be used in future exchanges. Therefore, this paper uses a Value at Risk (VaR) model to measure the risk of investment in Bitcoin. In this paper, we showed the results of the predicted daily loss of investment by using the historical simulation VaR model, the delta-normal VaR model, and the Monte Carlo simulation VaR model with the confidence levels of 99%, 95%, and 90%. This paper displayed backtesting methods to investigate the accuracy of VaR models, which consisted of the Kupiec’s POF and the Kupiec’s TUFF statistical testing results. Finally, Christoffersen’s independence test and Christoffersen’s interval forecasts evaluation showed effectiveness in the predictions for the robustness of VaR models for each confidence level.

scholarly journals EVALUATING MEASURES OF MARKET RISK IN CIRCUMSTANCES OF GLOBAL FINANCIAL CRISIS – EMPIRICAL EVIDENCE FROM FIVE COUNTRIES

2013 ◽  
Vol 1 ◽  
pp. 75-81
Author(s):  
Ivica Terzić ◽  
Marko Milojević
Keyword(s):  
Value At Risk ◽  
Global Financial Crisis ◽  
Market Risk ◽  
Var Models ◽  
Risk Models ◽  
Historical Simulation ◽  
Rolling Window ◽  
Independence Properties ◽  
Window Approach ◽  
Metrics Model

The purpose of this paper is to evaluate performance of value-at-risk (VaR) produced by two risk models: historical simulation and Risk Metrics. We perform three backtest: unconditional coverage, independence and conditional coverage. We present results on both VaR 1% and VaR 5% on a one-day horizon for the following indices: S&P 500, DAX, SAX, PX and Belex 15. Our results show that Historical simulation 500 days rolling window approach satisfies unconditional coverage for all tested indices, while Risk Metrics has many rejection cases. On the other hand Risk Metrics model satisfies independence backtest for three indices, while Historical simulation has rejected more times. Based on our strong criteria to accept accuracy of VaR models only if both unconditional coverage and independence properties are satisfied, results indicate that during the crisis period all tested VaR models underestimate the true level of market risk exposure.

Forecasting value at risk (VAR) for emerging and developed markets

2019 ◽  
Vol 37 (3) ◽  
pp. 133
Author(s):  
Viviane Naimy ◽  
Melissa Bou Zeidan
Keyword(s):  
Emerging Markets ◽  
Value At Risk ◽  
Predictive Ability ◽  
Historical Simulation ◽  
Confidence Levels ◽  
Developed Markets ◽  
Out Of Sample ◽  
Weighted Model ◽  
The One ◽  
Modelling And Forecasting

This paper explores different approaches to modelling and forecasting VaR, using both historical simulation and volatility-weighted bootstrap methods, where volatility is estimated using GARCH (1,1) and EGARCH (1,1). It examines the one day predictive ability of three historical simulation VaR models at the 90%, 95%, and 99% confidence levels for developed and emerging equity markets for the period 2011- 2017 that witnessed difficult and extreme market conditions. 870 scenarios of future returns are generated for each of the 500 days representing the out of sample period extending from March 2015 up to January 2017 in order to estimate the corresponding VaR for both markets. The GARCH (1,1) volatility-weighted model is accepted for both markets and is classified as the best performing model. The EGARCH (1,1) volatility-weighted model’s results were inconclusive; in fact, the back-test was accepted at all confidence levels for the developed markets while rejected at the 99% confidence level for the emerging markets. The basic historical simulation failed in estimating an accurate VaR for the emerging markets.

scholarly journals Return distribution and value at risk estimation for BELEX15

2011 ◽  
Vol 21 (1) ◽  
pp. 103-118 ◽  
Author(s):  
Dragan Djoric ◽  
Emilija Nikolic-Djoric
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Var Model ◽  
Ratio Test ◽  
Sample Period ◽  
Return Distribution ◽  
Model Accuracy ◽  
Confidence Levels

The aim of this paper is to find distributions that adequately describe returns of the Belgrade Stock Exchange index BELEX15. The sample period covers 1067 trading days from 4 October 2005 to 25 December 2009. The obtained models were considered in estimating Value at Risk ( VaR ) at various confidence levels. Evaluation of VaR model accuracy was based on Kupiec likelihood ratio test.

scholarly journals Transformational Approach to Analytical Value-at-Risk for near Normal Distributions

2021 ◽  
Vol 14 (2) ◽  
pp. 51
Author(s):  
Puneet Prakash ◽  
Vikas Sangwan ◽  
Kewal Singh
Keyword(s):  
Economic History ◽  
Value At Risk ◽  
Estimation Methods ◽  
Var Models ◽  
Risk Capital ◽  
Normal Distributions ◽  
Historical Simulation ◽  
Return Distributions ◽  
Transformation Methods ◽  
Economic Domain

In this paper, we extend the parametric approach of VaR estimation that is based upon the application of two transforms, one for handling skewness and other for kurtosis. These transformations restore normality to data when applied in succession. The transforms are well defined and offer an alternative to VaR models based on the variance–covariance approach. We demonstrate the application of the technique using three pairs of uncorrelated but negatively skewed and fat-tailed stock return distributions, one pair each from recent periods in US and international market, and one from the stressed period of US economic history. Furthermore, we extend the analysis to economic domain by calculating expected shortfalls and risk capital under different estimation methods. For the sake of completion, we compare the estimation results of normal and transformation methods to non-parametric historical simulation.

scholarly journals Are range based models good enough? Evidence from seven stock markets

2018 ◽  
Vol 8 (2) ◽  
pp. 7-40
Author(s):  
Everton Dockery ◽  
Miltiadis Efentakis Miltiadis Efentakis ◽  
Mamdouh Abdulaziz Saleh Al-Faryan
Keyword(s):  
Financial Market ◽  
Coverage Probability ◽  
Value At Risk ◽  
Risk Measure ◽  
Likelihood Ratio Tests ◽  
Loss Functions ◽  
Model Accuracy ◽  
Historical Simulation ◽  
Measurement Models ◽  
Empirical Estimates

We study the performance of range-based models over varying market conditions and compare their performance against a set of alterative risk measurement models, including the more widely used techniques in practice for measuring the Value-at-Risk (VaR) of seven financial market indices. In particular, we focus on model accuracy in estimated VaRs over quiet and volatile moments utilizing loss functions and likelihood ratio tests for coverage probability. The empirical estimates based on these two criteria find that the range based-model of Yang and Zhang (2000) shows some success in estimated VaR risk measure, especially during quiet periods, than is the case for the other range based models considered. Also, we find that the EWMA and RiskMetrics models have an inconsistent marginal edge over the widely used GARCH and historical simulation specifications and that there is validity in the use of the EWMA and RiskMetrics models over range-based approaches as both capture and thus provide more accurate estimated VaR risk measure of market risk.

scholarly journals Value at Risk, Legislative Framework, Crises, and Procyclicality: what goes wrong?

2020 ◽  
Vol 12 (3) ◽  
pp. 345-369
Author(s):  
Evangelos Vasileiou ◽  
Aristeidis Samitas
Keyword(s):  
At Risk ◽  
Financial Management ◽  
Financial Crises ◽  
Value At Risk ◽  
Var Model ◽  
Var Models ◽  
Investment Environment ◽  
Basel Accords ◽  
Legislative Framework ◽  
Sustainable Investment

This study highlights some deficiencies of the stock markets’ risk legislation framework, and particularly the CESR (2010) guidelines. We show that the current legislative framework fails to offer incentives to financial management companies to invest in advanced models for more representative Value at Risk (VaR) estimations, and for this reason, in many cases conventional VaR models are applied. We use data from the DAX, CAC 40, FTSE, FTSEMIB and IBEX indices, and then we apply them to the widely accepted Delta Normal VaR model. The empirical findings show that the conventional VaR models not only fail to provide information for the upcoming financial crises, but also contribute to such phenomena as procyclicality and overreaction in the stock market. We suggest additional tests and we empirically show how these tests could reduce the procyclicality issue and promote a more sustainable investment environment. Even though this study is mainly focused on CESR (2010) guidelines, it could be useful for any similar legislative framework, such as the Basel Accords.

A VaR-based Downside Risk Analysis of Indian Equity Mutual Funds in the Pre- and Post-global Financial Crisis Periods

2019 ◽  
Vol 18 (2) ◽  
pp. 210-236 ◽  
Author(s):  
Soumya Guha Deb
Keyword(s):  
Mutual Funds ◽  
Value At Risk ◽  
Global Financial Crisis ◽  
Moving Average ◽  
Downside Risk ◽  
Parametric Models ◽  
Historical Simulation ◽  
A Value ◽  
Equity Mutual Funds ◽  
Non Parametric

This article analyses downside risk of Indian equity mutual funds from 1999 to 2014 using a value at risk (VaR)-based approach. We use weekly return data of a sample of 349 equity mutual funds during the said period to estimate their weekly VaRs on a rolling basis using some parametric and non-parametric models. Moving average (MA), exponentially weighted MA and GARCH (1, 1) are the parametric models and historical simulation (HS) is the non-parametric model. We also carry out backtesting of the models using three popular approaches—two under the unconditional coverage approach, namely Jorion’s ‘Failure Rate’ approach and Kupiec’s proportion of ‘failures’ (POF) test, and one under the conditional coverage approach, namely the Christoffersen’s Independence test—to test the robustness of the VaR models. Our results show that Indian equity mutual funds exhibit considerable downside risk during the chosen period, in terms of the magnitude of the projected VaRs. Moreover, significant proportions of the funds ‘fail’ the predicted VaRs, particularly during times of crisis for some of the models, raising questions about their robustness in an investment setting in India. On the whole, both from failure proportion as well as backtesting perspective, the GARCH (1,1) seems to be the most robust of the models. JEL codes: G32, G15, G23

scholarly journals MODELING MARKET RISK IN FRONTIER EQUITY MARKETS—EVIDENCE FROM SERBIA

2014 ◽  
Vol 2 ◽  
pp. 126-133
Author(s):  
Marko Milojević ◽  
Ivica Terzić
Keyword(s):  
Value At Risk ◽  
Financial Risk ◽  
Statistical Tests ◽  
Moving Average ◽  
Predictive Ability ◽  
Equity Market ◽  
Evaluation Procedure ◽  
Asymmetric Distribution ◽  
Var Models ◽  
Historical Simulation

The need for understanding financial risk management and unique models for measuring risk in transitional capital markets increasingly gains in importance and becomes a very current issue. This article studies predictive ability of various classes of Value-at-Risk (VaR) models focusing on Serbian equity market in both stressed and normal market conditions. The five VaR models adopted in our evaluation procedure include: historical simulation with rolling window of 500 days, Risk Metrics, exponentially-weighted moving average (EWMA) with optimized decay factor, VaR based on models from GARCH family under three distributional assumptions (normal, generalized error, and Student-t), and Filtered historical simulation. In order to verify the forecasting performance of different VaR models, we employ a backtesting procedure, which consists of statistical tests. The results indicate that VaR based on conditional volatility models with asymmetric distribution of innovations behave reasonably well in both tranquil and crisis period.  Standard VaR models developed for liquid and efficient markets seriously underestimate risk forecast in Serbian equity market under all circumstances.

scholarly journals METODE SIMULASI HISTORIS UNTUK PERHITUNGAN NILAI VALUE AT RISK PADA PORTOFOLIO DENGAN MODEL MARKOWITZ

2021 ◽  
Vol 9 (2) ◽  
pp. 94-102
Author(s):  
I Wayan Eka Sultra ◽  
Muhammad Rifai Katili ◽  
Muhammad Rezky Friesta Payu
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Calculation Model ◽  
Expected Returns ◽  
Historical Simulation ◽  
Markowitz Model ◽  
Time Value ◽  
The Past ◽  
A Value ◽  
Multiple Assets

A portfolio concerns the formation of the composition of multiple assets to obtain optimum results. At the same time, Value at Risk is a technique in risk management to measure and assess parametrically (variant and co-variant), Monte-Carlo, and historical simulation. This research employed historic simulation because normal distribution is not required from returns and is a Value at Risk calculation model that is determined by the past value on produced return asset, in which this research aimed to determine the Markowitz model positive shares and Value at Risk in the portfolio by using historical simulation. The Markowitz model found eight shares with positive expected returns, which are as follows: BBCA, BBRI, BRPT, EXCL, ICBP, INDF, MNCN, and TPIA. The BBCA has the most significant exposure of all the shares with the amount of Rp 2.287.200.440.000, while the TPIA has the smallest exposure of all the shares with the amount of Rp 58.899.375.000. Further, the EXCL has the largest VaR with the amount of Rp 236.189.538.497, while the TPIA and ICBP had no VaR losses because the VaR of TPIA and ICBP is Rp 0 and Rp -1.407.719.893, respectively, along with the INDF as the share with the smallest VaR of Rp 18.513.213.620. The most significant exposure average is Rp 719.246.318.375, while the largest VaR average is Rp 76.827.608.341,3. As long as the VaR did not exceed the exposure value, the investors will be safe and have no loss.

scholarly journals Market Risk VaR Historical Simulation Model with Autocorrelation Effect: A Note

2009 ◽  
Author(s):  
Wantanee Surapaitoolkorn
Keyword(s):  
Brownian Motion ◽  
Simulation Model ◽  
Stock Returns ◽  
Autocorrelation Function ◽  
Value At Risk ◽  
Risk Model ◽  
Market Risk ◽  
Geometric Brownian Motion ◽  
Var Model ◽  
Historical Simulation

The modern market risk model using Value at Risk (VaR) method in the banking area under the BASEL II Accord can take different forms of simulation. In this paper, historical simulation will be applied to the VaR model comparing the two different approaches of Geometric Brownian Motion (GBM) process and Bootstrapping methods. The analysis will use correlation plots and examine the effects of the autocorrelation function for stock returns.  

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