Comparison of Value-at-Risk models using the MCS approach

Bernardi, Mauro; Catania, Leopoldo

doi:10.1007/s00180-016-0646-6

Cited by 43 publications

(13 citation statements)

References 76 publications

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“…We surmise from the results that the ease/difficulty with which to model tail risks does not depend on the actual conditions of the financial markets for an equity except for China and South Africa. Bernardi & Catania [8] made similar remarks in using the MCS algorithm to compare VaR models of four major global stock indices from Asia, North America and Europe. It follows that risk managers would prefer a SSM with a size closer to M 0 to those that are smaller.…”

Section: Descriptive Statisticsmentioning

confidence: 85%

On the Elicitability and Risk Model Comparison of Emerging Markets Equities

Owusu

Alagidede

Tiwari³

2021

MCA

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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.

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Section: Descriptive Statisticsmentioning

confidence: 85%

On the Elicitability and Risk Model Comparison of Emerging Markets Equities

Owusu

Alagidede

Tiwari³

2021

MCA

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“…The existing risk measurement methods are mainly based on different properties of the international crude oil market returns. On the one hand, the market risk is measured from the perspective of heteroscedasticity of asset returns, such as in literature which uses static and dynamic VaR based on GARCH models to predict risks in financial markets like stock markets, international crude oil markets and virtual money markets [20][21][22]. On the other hand, from the perspective of asset returns agglomeration to measure market risks.…”

Section: Risk Measurement Methods Of International Crude Oil Marketmentioning

confidence: 99%

“…Cabedo and Moya [19] assumed heteroscedasticity of crude oil returns and measured the risk of the crude oil market based on the GARCH model. Thereafter, most of the literature adopts static and dynamic VaR based on the GARCH model to predict financial market risks in stock markets, international crude oil markets and virtual currency markets [20][21][22][23]. For example, based on the heteroscedasticity of virtual currency returns, Li et al [24] measured virtual currency market risks with different types of GARCH models.…”

Section: Introductionmentioning

confidence: 99%

Asymmetry of Risk Evolution in Crude Oil Market: From the Perspective of Dual Attributes of Oil

Liu

Yao

et al. 2021

Energies

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Investor emotional heterogeneity and oil dual attributes are the key factors that cause the asymmetry of risks in the international crude oil market. This paper uses the monthly data from April 2003 to October 2020 to identify the dynamic characteristics of oil’s commodity attribute and financial attribute, and this paper also analyzes the asymmetric characteristics of risk evolution and risk degree in the international crude oil market under the condition of oil returns heterogeneity. The empirical results show that: first, there is heterogeneity in the influence of oil attributes on the risk evolution and risk degree of the international crude oil market; second, the alternation of oil dual attributes has a significant asymmetric impact on the risk evolution of international crude oil market; third, the sudden change of international crude oil market risk caused by oil attributes is asymmetric under different oil returns trends. Based on the empirical conclusion, this paper puts forward the corresponding policy recommendations.

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“…The model class is closely related to Generalized Autoregressive Score models of Creal et al (2013), for a predictive application see e.g. Bernardi & Catania (2016). In the paper we follow the formulation by Harvey.…”

Section: Methodsmentioning

confidence: 99%

Probabilistic predictive analysis of business cycle fluctuations in Polish economy

Mazur

2017

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Research background: The probabilistic setup and focus on evaluation of uncertainties and risks has become more widespread in modern empirical macroeconomics, including the analysis of business cycle fluctuations. Therefore, forecast-based indicators of future economic conditions should be constructed using density forecasts rather than point forecasts, as the former provide description of forecast uncertainty. Purpose of the article: We discuss model-based probabilistic inference on business cycle fluctuations in Poland. In particular, we consider model comparison for probabilistic prediction of growth rates of the Polish industrial production. We also develop a class of indicators of future economic conditions constructed using probabilistic information on the rates (that make use of joint predictive distribution over several forecast horizons). Methods: We use Bayesian methods (in order to capture the estimation uncertainty) and consider two groups of models. The first group consists of Dynamic Conditional Score models with the generalized t conditional distribution (with conditional heteroskedasticity and heavy tails, being important for modelling of extreme observations). Another group of models relies on deterministic cycle modelling using Flexible Fourier Form. Ex-post density forecasting performance of the models is compared using the criteria for probabilistic pre-diction: Log-Predictive Score (LPS) and Continuous Ranked Probability Score (CRPS). Findings & value added: The pre-2013 data support the deterministic cycle models whereas more recent observations can be explained by a simple mean-reverting Gaussian AR(4) process. The results indicate a structural change affecting Polish business cycle fluctuations after 2013. Hence, forecast pooling strategies are recommended as a tool for further research. We find rather limited support in favor of the first group of models. The probabilistic indicator of future economic conditions considered here leads actual phases of the growth cycle quite well, though the effect is less obvious after 2013.

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Comparison of Value-at-Risk models using the MCS approach

Cited by 43 publications

References 76 publications

On the Elicitability and Risk Model Comparison of Emerging Markets Equities

On the Elicitability and Risk Model Comparison of Emerging Markets Equities

Asymmetry of Risk Evolution in Crude Oil Market: From the Perspective of Dual Attributes of Oil

Probabilistic predictive analysis of business cycle fluctuations in Polish economy

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