Extreme Value at Risk and Expected Shortfall During Financial Crisis

Kourouma, L.; Dupré, Denis; Sanfilippo, Gilles; Taramasco, Ollivier

doi:10.2139/ssrn.1744091

Cited by 26 publications

(26 citation statements)

References 19 publications

(10 reference statements)

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“…For Expected Shortfall, our findings contrast with those of Kourouma, L. et al (2011) in the international landscape, whose tests suggest that estimates of ES t α based on a GARCH-GPD might overestimate the value of such risk measure.…”

Section: Risk Measures For the Innovations Seriescontrasting

confidence: 96%

“…On the other hand, our application also contrasts with previous work in the fact that it analyzes the efficiency in Mexican data of the GARCH-GPD method in periods both of crisis (unlike Fernández, V. P. (2003)) and relative calm (in contrast to Kourouma, L. et al (2011), López, E. (2013 and Aguirre, A. I. et al (2013)). …”

Section: Introductionmentioning

confidence: 82%

“…When exploring relevant literature on this type of applications we find that Kourouma, L. et al (2011) carried out an exercise similar to ours; however, such work focuses on US stock indices and asset prices such as oil and corn during a financial crisis. As far as we know, there has not been an application like ours in Mexican time series.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Market Risk Measures in Mexican Financial Time Series

Espinosa

2017

REMEF

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The objectives of this work are to investigate whether: i) a GARCH model with Generalized Pareto Distribution (GPD) innovations, complemented with an EWMA volatility forecast in order to consider practical problems that might arise in GARCH applications that comprise long periods of time, appropriately estimate risk measures (VaR and Expected Shortfall) for Mexican financial series, at high confidence levels; ii) the estimates yielded by such model are better than those given by a GARCH with Gaussian or Student-t innovations. Our quality assessment and comparison between models consist of backtests of the risk measures estimates yielded by each method used in this paper. Our results show that: i) the methodology used in this paper appropriately estimates our two risk measures; ii) the GARCH-GPD model yields better results than the GARCH-Gaussian and GARCH-t-Student models. Our results are limited to one-day risk measures estimates. As far as we know, our results on the Expected Shortfall are the first of its kind for Mexican series. We conclude that the study achieved its objectives and there are important areas of opportunity for further studies. Los objetivos de este trabajo son investigar si: i) un modelo GARCH con innovaciones modeladas mediante una Distribución Pareto Generalizada (DPG), complementado con un pronóstico EWMA de volatilidad para considerar problemas prácticos que pueden surgir en aplicaciones GARCH que comprenden largos periodos de tiempo, estima adecuadamente medidas de riesgo (VaR y Expected Shortfall) para series financieras mexicanas a altos niveles de confianza; ii) las estimaciones de dicho modelo son mejores que aquellas entregadas por un GARCH con innovaciones Gaussianas o t-Student. Nuestras evaluaciones de calidad y comparación entre modelos consisten de backtests de las medidas de riesgo de cada método * Circuito Exterior s/n, Coyoacán, Cd. Universitaria, C. P: 04510 Ciudad de México, CDMX. Tel. 01 55 5622 4992. E-mail: alberto.saavedra17@hotmail.comThe results presented in this paper are part of the authors bachelor thesis, which was supervised by Dr. María Asución Begoña Fernández Fernández. The author greatly thanks Dr. Begoña for her invaluable comments and support during the whole elaboration process of this work. The author also thanks two anonymous referees for helpful comments and observations on a previous version of this paper.366 NuevaÉpoca REMEF (The Mexican Journal of Economics and Finance) utilizado en el presente artículo. Nuestros resultados muestran que: i) la metodología utilizada estima apropiadamente nuestras dos medidas de riesgo; ii) el modelo GARCH-DPG entrega mejores resultados que los modelos GARCH-Normal y GARCH-t-Student. Nuestros resultados se limitan a estimaciones de medidas de riesgo a un día. Hasta donde sabemos, nuestros resultados sobre el Expected Shortfall son los primeros de su clase para series mexicanas. Concluimos que el estudio alcanzó sus objetivos y existen importanteś areas de oportunidad para estudios posteriores.

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Section: Risk Measures For the Innovations Seriescontrasting

confidence: 96%

Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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Estimation of Market Risk Measures in Mexican Financial Time Series

Espinosa

2017

REMEF

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“…We are indebted to an anonymous referee for the suggestion to conduct this study. 9 Embrechts (2000) and Kourouma et al (2011) provide a similar approach. Table 3 Estimates of intertemporal relationship between expected shortfall (as downside risk) and excess return This table reports estimates of the intertemporal relationship between expected shortfall and excess return.…”

Section: Preliminary Estimates Of Downside Riskmentioning

confidence: 99%

Empirical Analysis of the Intertemporal Relationship between Downside Risk and Expected Returns: Evidence from Time‐varying Transition Probability Models

Chen

Chiang

2015

Euro Fin Management

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This paper examines the intertemporal relationship between downside risks and expected stock returns for five advanced markets. Using Value-at-Risk (VaR) as a measure of downside risk, we find a positive and significant relationship between VaR and the expected return before the world financial crisis (September 2008). However, when we estimate the model using a sample after this date, the results show a negative risk-return relationship. Evidence from a two-state Markov regime-switching model indicates that as uncertainty rises, the sign of the riskreturn relationship turns negative. Evidence suggests that the Markov regimeswitching model helps to resolve the conflicting signs in the risk-return relationship.

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“…VaR is known to underestimate risk (Kourouma, Dupré, Sanfilippo & Taramasco, 2010), and thereby provides a lower boundary. A structural upper limit must also be determined: the inflator provides this upper limit as it increases in severity to ensure the avoidance of bubble formation during market stress.…”

Section: Inflatormentioning

confidence: 99%