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

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.

2021 ◽  
pp. 1-29
Author(s):  
Yanhong Chen

ABSTRACT In this paper, we study the optimal reinsurance contracts that minimize the convex combination of the Conditional Value-at-Risk (CVaR) of the insurer’s loss and the reinsurer’s loss over the class of ceded loss functions such that the retained loss function is increasing and the ceded loss function satisfies Vajda condition. Among a general class of reinsurance premium principles that satisfy the properties of risk loading and convex order preserving, the optimal solutions are obtained. Our results show that the optimal ceded loss functions are in the form of five interconnected segments for general reinsurance premium principles, and they can be further simplified to four interconnected segments if more properties are added to reinsurance premium principles. Finally, we derive optimal parameters for the expected value premium principle and give a numerical study to analyze the impact of the weighting factor on the optimal reinsurance.


Author(s):  
Evangelos Vasileiou ◽  
Themistoclis Pantos

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.


2020 ◽  
Vol 21 (5) ◽  
pp. 543-557
Author(s):  
Modisane Bennett Seitshiro ◽  
Hopolang Phillip Mashele

Purpose The purpose of this paper is to propose the parametric bootstrap method for valuation of over-the-counter derivative (OTCD) initial margin (IM) in the financial market with low outstanding notional amounts. That is, an aggregate outstanding gross notional amount of OTC derivative instruments not exceeding R20bn. Design/methodology/approach The OTCD market is assumed to have a Gaussian probability distribution with the mean and standard deviation parameters. The bootstrap value at risk model is applied as a risk measure that generates bootstrap initial margins (BIM). Findings The proposed parametric bootstrap method is in favour of the BIM amounts for the simulated and real data sets. These BIM amounts are reasonably exceeding the IM amounts whenever the significance level increases. Research limitations/implications This paper only assumed that the OTCD returns only come from a normal probability distribution. Practical implications The OTCD IM requirement in respect to transactions done by counterparties may affect the entire financial market participants under uncleared OTCD, while reducing systemic risk. Thus, reducing spillover effects by ensuring that collateral (IM) is available to offset losses caused by the default of a OTCDs counterparty. Originality/value This paper contributes to the literature by presenting a valuation of IM for the financial market with low outstanding notional amounts by using the parametric bootstrap method.


Risks ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 222
Author(s):  
Danai Likitratcharoen ◽  
Nopadon Kronprasert ◽  
Karawan Wiwattanalamphong ◽  
Chakrin Pinmanee

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.


2017 ◽  
Vol 11 (22) ◽  
Author(s):  
Humberto Banda-Ortiz ◽  
Felipe Pérez-Sosa ◽  
Denise Gómez-Hernández

Keywords: portfolio, uncertainty analysis, Value at RiskAbstract: VaR is the most accepted risk measure worldwide and the leading reference in any risk management assessment. However, its methodology has important limitations which makes it unreliable in contexts of crisis or high uncertainty. For this reason, the aim of this work is to test the VaR accuracy when is employed in contexts of volatility, for which we compare the VaR outcomes in scenarios of both stability and uncertainty, using the parametric method and a historical simulation based on data generated with the Black & Scholes model. VaR main objective is the prediction of the highest expected loss for any given portfolio, but even when it is considered a useful tool for risk management under conditions of markets stability, we found that it is substantially inaccurate in contexts of crisis or high uncertainty. In addition, we found that the Black & Scholes simulations lead to underestimate the expected losses, in comparison with the parametric method and we also found that those disparities increase substantially in times of crisis. In the first section of this work we present a brief context of risk management in finance. In section II we present the existent literature relative to the VaR concept, its methods and applications. In section III we describe the methodology and assumptions used in this work. Section IV is dedicated to expose the findings. And finally, in Section V we present our conclusions.Palabras clave: análisis de incertidumbre, portafolio, Valor en RiesgoResumen: El VaR es la medida de riesgo más aceptada a nivel mundial y la principal referencia en cualquier valuación de riesgo. Sin embargo, su metodología tiene importantes limitantes que la hace poco fiable en contextos de crisis o de alta incertidumbre. Por esta razón, el objetivo de este trabajo es poner a prueba la precisión del VaR cuando se emplea en contextos de volatilidad, por lo que se comparan los resultados del VaR en los escenarios de estabilidad e incertidumbre, utilizando el método paramétrico y una simulación histórica basada en datos generados con el modelo Black & Scholes. El objetivo principal del VaR es la predicción de la pérdida esperada más alta para cualquier cartera determinada, pero incluso cuando se considera una herramienta útil para la gestión de riesgos en condiciones de mercados estables, encontramos que es sustancialmente inexacta en contextos de crisis o de alta incertidumbre. Además, se encontró que las simulaciones de Black & Scholes conducen a subestimar las pérdidas esperadas, en comparación con el método paramétrico y también encontramos que esas disparidades aumentan sustancialmente en tiempos de crisis. En la primera sección de este trabajo se presenta un breve contexto de la gestión de riesgos en las finanzas. En la sección II se presenta la literatura existente en relación con el concepto del VaR, sus métodos y aplicaciones. En la sección III se describe la metodología y los supuestos utilizados en este trabajo. Sección IV está dedicado a exponer los hallazgos. Y, por último, en la Sección V se presentan las conclusiones.


2013 ◽  
Vol 43 (3) ◽  
pp. 271-299 ◽  
Author(s):  
Jianfa Cong ◽  
Ken Seng Tan ◽  
Chengguo Weng

AbstractHedging is one of the most important topics in finance. When a financial market is complete, every contingent claim can be hedged perfectly to eliminate any potential future obligations. When the financial market is incomplete, the investor may eliminate his risk exposure by superhedging. In practice, both hedging strategies are not satisfactory due to their high implementation costs, which erode the chance of making any profit. A more practical and desirable strategy is to resort to the partial hedging, which hedges the future obligation only partially. The quantile hedging of Föllmer and Leukert (Finance and Stochastics, vol. 3, 1999, pp. 251–273), which maximizes the probability of a successful hedge for a given budget constraint, is an example of the partial hedging. Inspired by the principle underlying the partial hedging, this paper proposes a general partial hedging model by minimizing any desirable risk measure of the total risk exposure of an investor. By confining to the value-at-risk (VaR) measure, analytic optimal partial hedging strategies are derived. The optimal partial hedging strategy is either a knock-out call strategy or a bull call spread strategy, depending on the admissible classes of hedging strategies. Our proposed VaR-based partial hedging model has the advantage of its simplicity and robustness. The optimal hedging strategy is easy to determine. Furthermore, the structure of the optimal hedging strategy is independent of the assumed market model. This is in contrast to the quantile hedging, which is sensitive to the assumed model as well as the parameter values. Extensive numerical examples are provided to compare and contrast our proposed partial hedging to the quantile hedging.


Author(s):  
Evangelos Vasileiou ◽  
Themistoclis Pantos

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.


2019 ◽  
Vol 22 (1) ◽  
pp. 38-52 ◽  
Author(s):  
Umut Uyar ◽  
Ibrahim Korkmaz Kahraman

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.


2018 ◽  
Vol 5 (331) ◽  
pp. 185-203
Author(s):  
Dominik Krężołek

 Risk analysis in the financial market requires the correct evaluation of volatility in terms of both prices and asset returns. Disturbances in quality of information, the economic and political situation and investment speculations cause incredible difficulties in accurate forecasting. From the investor’s point of view, the key issue is to minimise the risk of huge losses. This article presents the results of using some selected GARCH‑type models, ARMA‑GARCH and ARMA‑APARCH, in evaluating volatility of asset returns in the metals market. To assess the level of risk, the Value‑at‑Risk measure is used. The comparison between real and estimated losses (in terms of VaR) is made using the backtesting procedure. 


2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Khreshna Syuhada

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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