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.

scholarly journals Applying three VaR (value at risk) approaches in measuring market risk of stock portfolio: The case study of VN-30 stocks basket in HOSE

2017 ◽  
Vol 24 (02) ◽  
pp. 90-113
Author(s):  
Thinh Nguyen Quang ◽  
Quy Vo Thi
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Stock Price ◽  
Stock Exchange ◽  
Market Risk ◽  
Risk Models ◽  
Historical Simulation ◽  
Back Testing

This study examines and applies the three statistical value at risk models including variance-covariance, historical simulation, and Monte Carlo simulation in measuring market risk of VN-30 portfolio of Ho Chi Minh stock exchange (HOSE) in Vietnam stock market and some back-testing techniques in assessing the validity of the VaR performance in the timeframe of January 30, 2012–February 26, 2016. The models are constructed from two volatility methods of stock price: SMA and EWMA throughout the five chosen confi-dence level: 90%, 93%, 95%, 97.5%, and 99%. The findings of the study show that the differences among the results of three models are not significant. Additionally, three VaR (Value at Risk) models have generally the similar accepted range assessed in both types of back-tests at all confidence levels considered and at the 97.5% con-fidence level. They can work best to achieve the highest validity level of results in satisfying both conditional and unconditional back-tests. The Monte Carlo Simulation (MCS) has been considered the most appropriate method to apply in the context of VN-30 port-folio due to its flexibility in distribution simulation. Recommenda-tions for further research and investigations are provided according-ly.

scholarly journals Measuring Market Risk of Commercial Banks Implementing VaR with Historical Simulation Approach

2021 ◽  
pp. 79-99
Author(s):  
Minhaz-Ul Haq
Keyword(s):  
Stock Prices ◽  
Commercial Banks ◽  
Value At Risk ◽  
First Generation ◽  
Market Risk ◽  
Jel Classification ◽  
Simulation Approach ◽  
Share Prices ◽  
Historical Simulation ◽  
The Impact

This paper attempts to picture the impact of the market risk of ten commercial banks located in Bangladesh with the help of a non-parametric model known as the Historical Simulation Approach over the course of eight years. These banks' daily stock prices were used as inputs and analyzed in Microsoft Excel by means of Percentile and LN function. The study revealed market risk exposure as third, second-and first-generation banks from the least to the highest. It also pointed out the ups and downs of these banks' share prices in the selected period. Further analysis showed the portfolio VaR estimation for different time intervals. JEL classification numbers: G32. Keywords: Value-at-risk, Historical Simulation, Market Risk, Confidence Interval.

scholarly journals On the Elicitability and Risk Model Comparison of Emerging Markets Equities

2021 ◽  
Vol 26 (3) ◽  
pp. 63
Author(s):  
Peterson Owusu Junior ◽  
Imhotep Paul Alagidede ◽  
Aviral Kumar Tiwari
Keyword(s):  
South Africa ◽  
Emerging Markets ◽  
Value At Risk ◽  
Risk Model ◽  
Global Financial Crisis ◽  
Risk Models ◽  
Market Conditions ◽  
Tail Risk ◽  
Risk Modelling ◽  
Model Confidence Set

The need for comparative backtesting in the Basel III framework presents the challenge for ranking of internal value-at-risk (VaR) and expected shortfall (ES) models. We use a joint loss function to score the elicitable joint VaR and ES models to select competing tail risk models for the top 9 emerging markets equities and the emerging markets composite index. We achieve this with the model confidence set (MCS) procedure. Our analysis span two sub-sample periods representing turbulent (Eurozone and Global Financial crises periods) and tranquil (post-Global Financial crisis period) market conditions. We find that many of the markets risk models are time-invariant and independent of market conditions. But for China and South Africa this is not true because their risk models are time-varying, market conditions-dependent, percentile-dependent and heterogeneous. Tail risk modelling may be difficult compared to other markets. The resemblance between China and South Africa can stem from the closeness between their equities composition. However, generally, there is evidence of more homogeneity than heterogeneity in risk models. This is indicated by a minimum of three models (out of six) per equity in most of the countries. This may ease the burden for risk managers to find the optimal set of models. Our study is important for internal risk modelling, regulatory oversight, reduce regulatory arbitrage and may bolster confidence in international investors with respect to emerging markets equities.

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 Symmetric and Asymmetric Causal Relationship between Oil Prices and G7 Stock Markets: A Bootstrap Rolling-Window Granger Causality Test

2021 ◽  
Vol 36 (4) ◽  
pp. 718-744
Author(s):  
Khaled Mokni ◽  
Mohamed Sahbi Nakhli ◽  
Othman Mnari ◽  
Khemaies Bougatef
Keyword(s):  
Stock Markets ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Causal Link ◽  
Stock Index ◽  
Causality Test ◽  
Hedging Strategies ◽  
Rolling Window ◽  
Window Approach ◽  
Asymmetric Causality

This study examines the causal relationships between oil prices and the MSCI stock index of G7 countries between September 2004 and October 2020. This study is novel in implementing symmetric and asymmetric time-varying causality tests based on the bootstrap rolling-window approach. The results reveal that the causal link between oil prices and G7 stock markets is time-dependent. The periods of bidirectional causality roughly coincide with the global financial crisis and the ongoing COVID-19 pandemic. When asymmetry is accounted for, the results suggest an asymmetric causality between the two markets expressed by different patterns regarding positive and negative oil shocks. The results also indicate symmetric causality during the COVID-19 pandemic. These findings have implications for portfolio design and hedging strategies that are important to both policymakers and investors.

Part III Investment Banking, 13 Trading Book—Standardized Approaches

2018 ◽  
Author(s):  
Gleeson Simon
Keyword(s):  
Default Risk ◽  
Investment Banking ◽  
Market Risk ◽  
Expected Shortfall ◽  
Capital Adequacy ◽  
Third Party ◽  
Regulatory Capital ◽  
Var Models ◽  
Risk Models ◽  
Basel 3

This chapter discusses trading book models. Risk models come in a variety of types. However, for market risk purposes there have been a number of types which may be used within the framework. The simplest is the ‘CAD 1’ model — named after the first Capital Adequacy Directive, which permitted such models to be used in the calculation of regulatory capital. VaR models, permitted by Basel 2, were more complex, and this complexity was increased by Basel 2.5, which required the use of ‘stressed VAR’. In due course all of this will be replaced by the Basel 3 FRTB calculation, which rejects VAR and is based on the calculation of an expected shortfall (ES) market risk charge, a VaR based default risk charge (DRC) (for those exposures where the bank is exposed to the default of a third party), and a stressed ES-based capital add-on.

Selection of Value-at-Risk models for MENA Islamic indices

2020 ◽  
Vol 11 (9) ◽  
pp. 1689-1708
Author(s):  
Wassim Ben Ayed ◽  
Ibrahim Fatnassi ◽  
Abderrazak Ben Maatoug
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Prediction Models ◽  
Market Risk ◽  
Parametric Models ◽  
Arch Model ◽  
Var Models ◽  
Content Type ◽  
Cent Level

Purpose The purpose of this study is to investigate the performance of Value-at-Risk (VaR) models for nine Middle East and North Africa Islamic indices using RiskMetrics and VaR parametric models. Design/methodology/approach The authors test the performance of several VaR models using Kupiec and Engle and Manganelli tests at 95 and 99 per cent levels for long and short trading positions, respectively, for the period from August 10, 2006 to December 14, 2014. Findings The authors’ findings show that the VaR under Student and skewed Student distribution are preferred at a 99 per cent level VaR. However, at 95 per cent level, the VaR forecasts obtained under normal distribution are more accurate than those generated using models with fat-tailed distributions. These results suggest that VaR is a good tool for measuring market risk. The authors support the use of RiskMetrics during calm periods and the asymmetric models (Generalized Autoregressive Conditional Heteroskedastic and the Asymmetric Power ARCH model) during stressed periods. Practical implications These results will be useful to investors and risk managers operating in Islamic markets, because their success depends on the ability to forecast stock price movements. Therefore, because a few Islamic financial institutions use internal models for their capital calculations, the regulatory committee should enhance market risk disclosure. Originality/value This study contributes to the knowledge in this area by improving our understanding of market risk management for Islamic assets during the stress periods. Then, it highlights important implications regarding financial risk management. Finally, this study fills a gap in the literature, as most empirical studies dealing with evaluating VaR prediction models have focused on quantifying the model risk in the conventional market.

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