scholarly journals Market Risk Measurement

2020 ◽  
Author(s):  
Emese Lazar ◽  
Ning Zhang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Preliminary Analysis ◽  
Risk Measures ◽  
Market Risk ◽  
Capital Requirements ◽  
Risk Measurement ◽  
Regulatory Framework ◽  
Bank Capital ◽  
Bank Capital Requirements

This chapter presents a preliminary analysis on how some market risk measures dramatically increased during the COVID-19 pandemic, with measures computed over longer horizons experiencing more pronounced effects. We provide examples when regulatory market risk measurement proved to be suboptimal, overestimating risk. A further issue was the large number of Value-at-Risk ‘exceptions’ during the first few months of the crisis, which normally leads to overinflated bank capital requirements. The current regulatory framework should address these problems by suggesting improvements to the calculation of risk measures and/or by modifying the rules which determine capital requirements to make them appropriate and realistic in crisis situations.

Value-at-risk and related measures for the Bitcoin

2018 ◽  
Vol 19 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Stavros Stavroyiannis
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Capital Allocation ◽  
Capital Requirements ◽  
Spot Price ◽  
Content Type ◽  
Type Iv ◽  
Pearson Type ◽  
Standard And Poor's

Purpose The purpose of this paper is to examine the value-at-risk and related measures for the Bitcoin and to compare the findings with Standard and Poor’s SP500 Index, and the gold spot price time series. Design/methodology/approach A GJR-GARCH model has been implemented, in which the residuals follow the standardized Pearson type-IV distribution. A large variety of value-at-risk measures and backtesting criteria are implemented. Findings Bitcoin is a highly volatile currency violating the value-at-risk measures more than the other assets. With respect to the Basel Committee on Banking Supervision Accords, a Bitcoin investor is subjected to higher capital requirements and capital allocation ratio. Practical implications The risk of an investor holding Bitcoins is measured and quantified via the regulatory framework practices. Originality/value This paper is the first comprehensive approach to the risk properties of Bitcoin.

Comparison of market risk models with respect to suggested changes of Basel Accord

2014 ◽  
Vol 64 (Supplement-2) ◽  
pp. 257-274
Author(s):  
Eliška Stiborová ◽  
Barbora Sznapková ◽  
Tomáš Tichý
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Market Risk ◽  
Capital Requirements ◽  
Internal Models ◽  
Conditional Value At Risk ◽  
Risk Capital ◽  
Basel Accord ◽  
Capital Charge

The market risk capital charge of financial institutions has been mostly calculated by internal models based on integrated Value at Risk (VaR) approach, since the introduction of the Amendment to Basel Accord in 1996. The internal models should fulfil several quantitative and qualitative criteria. Besides others, it is the so called backtesting procedure, which was one of the main reasons why the alternative approach to market risk estimation — conditional Value at Risk or Expected Shortfall (ES) — were not applicable for the purpose of capital charge calculation. However, it is supposed that this approach will be incorporated into Basel III. In this paper we provide an extensive simulation study using various sets of market data to show potential impact of ES on capital requirements.

scholarly journals Pomiar ryzyka rynkowego miarą wartoś​ci zagrożonej. Metoda kombinowania prognoz ​

2018 ◽  
pp. 183-198
Author(s):  
Piotr Mazur
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Value At Risk ◽  
Risk Measure ◽  
Market Risk ◽  
Risk Measurement ◽  
Conditional Value At Risk ◽  
Parametric Methods ◽  
Combining Forecasts

The article discusses the measurement of market risk by Value at Risk method. Value at Risk measure is an important element of risk measurement mainly for financial institutions but can also be used by other companies. The Value at Risk is presented together with its alternative Conditional Value at Risk. The main methods of VaR estimation were divided into nonparametric, parametric and semi-parametric methods. The next part of the article presents a method of combining forecasts, which can be used in the context of forecasting Value at Risk.

scholarly journals Investment Risk Measurement Based on Quantiles and Expectiles

2018 ◽  
Vol 5 (338) ◽  
pp. 213-227
Author(s):  
Grażyna Trzpiot
Keyword(s):  
At Risk ◽  
Quantile Regression ◽  
Value At Risk ◽  
Risk Measures ◽  
Quantile Function ◽  
Risk Measurement ◽  
Conditional Value At Risk ◽  
Investment Risk ◽  
Response Distribution ◽  
Expectile Regression

In the presented research, we attempt to examine special investment risk measurement. We use quantile regression as a model by describing more general properties of the response distribution. In quantile regression, we assume regression effects on the conditional quantile function of the response. In regression modelling, the focus is on extending linear regression (OLS), and in this paper we seek to apply expectile regression. The purpose of using both approaches is investment risk measurement. Both regression models are a version of least weighted squares model. The families of risk measures most commonly used in practice are the Value‑at‑Risk (VaR) and the Conditional Value‑at‑Risk (CVaR), which can be estimated by quantiles or expectiles in the tail of the response distribution.

The Influence of Specific Criteria of Emerging Markets on SME Financing in MENA Markets

2017 ◽  
pp. 46-68
Author(s):  
Wafa Snoussi ◽  
Azza Béjaoui
Keyword(s):  
At Risk ◽  
Emerging Markets ◽  
Value At Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Basel Ii ◽  
Risk Measurement ◽  
Measurement Tool ◽  
Back Testing ◽  
High Volatility

In this chapter we are interested in the impact of specific microstructure criteria of emerging markets in the financing of SMEs especially in risk measures. The main risk measurement tool is the Value-at-Risk (VaR) which is recommanded by the Basel II Committee on Banking Supervision (BCBS). The recommendations of the Basel II committee give financial institutions the freedom to develop their own Value-at-Risk model of risk measurement in order to calculate their capital requirements for financial risk. The Basel II committee recommends the use of back testing in order to validate the choice of the best method. In order to finance SMEs enterprises in emerging market we must consider the specific microstructure criteria of these emerging markets such as low liquidity, very pronounced asymmetric information, over predictability and high volatility how affects the risk estimation.

scholarly journals Evaluasi Kualitas Pengungkapan Value at Risk Perbankan Indonesia

2019 ◽  
pp. 276
Author(s):  
Buddi Wibowo ◽  
Hasna Fadhila
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Simulation Method ◽  
Market Risk ◽  
Risk Measurement ◽  
Risk Level ◽  
Risk Disclosure ◽  
Historical Simulation ◽  
Risk Disclosures

Market risk measurement of bank investment portfolios is a still problem not only among practitioners, but  also among academicians. The accuracy and quality of market risk disclosures are important issues because  transparency of the bank risk level encourages market control in the form of market discipline and it also  improve the quality of risk management carried out internally by the bank. This research measures the quality of Value at Risk disclosures carried out by Indonesian banks. The accuracy of Value at Risk in this research is measured from the Value at Risk component which contains information of yield volatility of bank trading treasury activities. To measure Value at Risk disclosure, this research runs various methods of Value at Risk measurement. This research shows Historical Simulation is a Value at Risk method that is most widely used by Indonesia banks. The empirical test results show that the Value at Risk parametric method using asymmetric volatility have better quality than the Value at Risk Historical Simulation method. This research shows that Value at Risk as measured by Historical Simulation method contains the least information of bank trading treasury yields. Keywords: value at risk; disclosure; market risk; volatility

Portfolio Market Risk Assessment Using Value-at-Risk (VaR) Method – Historical Stimulation

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sergei Radukanov ◽  
◽  
◽  
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Value At Risk ◽  
Theoretical Aspect ◽  
Market Risk ◽  
Risk Measurement ◽  
Gamble Company ◽  
Historical Simulation ◽  
Toyota Motor Corporation

One of the main VaR methods on theoretical aspect – Historical Simulation to Portfolio is explained in this article. Portfolio market risk measurement is carried out towards the shares of the particular companies – The Procter & Gamble Company (PG), Toyota Motor Corporation (TM) and Nokia Corporation (NOK).

The Influence of Specific Criteria of Emerging Markets on SME Financing in MENA Markets

2018 ◽  
pp. 398-420
Author(s):  
Wafa Snoussi ◽  
Azza Béjaoui
Keyword(s):  
At Risk ◽  
Emerging Markets ◽  
Value At Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Basel Ii ◽  
Risk Measurement ◽  
Measurement Tool ◽  
Back Testing ◽  
High Volatility

In this chapter we are interested in the impact of specific microstructure criteria of emerging markets in the financing of SMEs especially in risk measures. The main risk measurement tool is the Value-at-Risk (VaR) which is recommanded by the Basel II Committee on Banking Supervision (BCBS). The recommendations of the Basel II committee give financial institutions the freedom to develop their own Value-at-Risk model of risk measurement in order to calculate their capital requirements for financial risk. The Basel II committee recommends the use of back testing in order to validate the choice of the best method. In order to finance SMEs enterprises in emerging market we must consider the specific microstructure criteria of these emerging markets such as low liquidity, very pronounced asymmetric information, over predictability and high volatility how affects the risk estimation.

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