Fat-Tailed Models for Risk Estimation

Stoyanov, Stoyan V.; Rachev, Svetlozar T.; Racheva-Iotova, Boryana; Fabozzi, Frank J.

doi:10.3905/jpm.2011.37.2.107

Cited by 49 publications

(28 citation statements)

References 24 publications

(25 reference statements)

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“…Predlagani su različiti ekonometrijski modeli koji bi pojasnili volatilnost koja varira kroz vreme a oni koji su najčešće korišćeni su modeli GARCH tipa (Stoyanov et al, 2011). U našoj studiji smo koristili modele vremenskih serija GARCH (1,1) i EGARCH (1,1) kako bi smo raščistili grupisanje efekta volatilnosti u klastere pretpostavljajući normalnu, klasičnu studentov-t raspodelu i GED raspodelu inovacija.…”

Section: Data and Empirical Resultsunclassified

“…Different econometric models have been suggested to explain the time varying volatility and the most widely used ones are the GARCH-type models (Stoyanov et al, 2011). In our study we fitted time series models GARCH(1,1) and EGARCH(1,1) to clean the clustering of volatility effect assuming the normal, classical Student-t model and generalized error distribution on the innovations.…”

Section: Data and Empirical Resultsmentioning

confidence: 99%

“…Therefore, any probabilistic model for risk estimation should include a GARCH-type component (Stoyanov et al, 2011). The most recent and advanced VaR methods make use of GARCH models to calculate the conditional standard deviation (Köksal et al, 2013).…”

Section: Model Formula Opismentioning

confidence: 99%

See 2 more Smart Citations

Value at risk estimation and validation in the Serbian capital market in the period 2005-2015

Jeremić¹,

Terzić²,

Milojević³

2016

Bankarstvo

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Prevod obezbedili autori RezimeGlobalna finansijska kriza ukazala je na značajne slabosti postojećih modela merenja rizika na finansijskim tržištima. Posebno je izražena potreba za unapređivanjem i daljim razvojem modela finansijskog rizika na tržištima kapitala u zemljama u tranziciji. U ovom radu bavimo se mogućnošću predviđanja i ponašanjem raznih klasa VaR modela usredsređujući se na tržište kapitala u Srbiji od 2005. do kraja 2015. godine. U postupku procene tržišnog rizika implementirali smo različite VaR modele, a za validaciju VaR modela, koristili smo dvofazni postupak "backtestinga". Rezultati ukazuju da se VaR model, baziran na simetričnom GARCH modelu sa GED raspodelom inovacija, ponaša opravdano dobro u periodu testiranja izvan uzroka. Najbolje rezultate daje filtrirana istorijska simulacija za interval poverenja od 99%. Ovi rezultati istraživanja ukazuju da standardni VaR modeli koji se najčešće koriste u finansijskim institucijama potcenjuju predviđanja rizika na tržištu kapitala u Srbiji u svim tržišnim okolnostima. Autori stoga sugerišu regulatornim telima i investitorima da za tržište kapitala u Srbiji uvedu robusnije i kompleksnije mere rizika.Ključne reči: Tržišni rizik, tržišta kapitala u nastajanju, GARCH, Istorijska simulacija, Risk Metrics, VaR, Backtest JEL: G17, G32, C53 SummaryThe global financial crisis pointed at the significant weaknesses of the existing models for measuring risk in financial markets. In particular, there is a need for improvement and further development of the financial risk models in the capital markets in transition countries. Within this paper we deal with the possibility of predicting and behavior of various classes of VaR models focusing on the capital market in Serbia from 2005 to the end of 2015.We implemented various VaR models in assessing market risk and used a two-stage backtesting process for VaR models validation. The results indicate that VaR based on symmetric GARCH model with generalized error distribution of innovations behaves reasonably well in the post-of-sample testing period. Filtered historical simulation at 99% confidence level gives the best results. Standard VaR models mostly used by financial institutions underestimate the risk forecast in the Serbian capital market in all market circumstances. Thus, the authors suggest to the regulators and investors to introduce more robust and complex risk measures in the Serbian capital market. Uvod Poslednja svetska finansijska kriza ukazala je na potrebu fundamentalnih promena u upravljanju rizikom. Sve veći obim i složenost hartija od vrednosti kojima sa trguje, uz povećanu volatilnost tržišta tokom protekle decenije, primorali su finansijske institucije i regulatorna tela da usvoje veliki broj modela merenja rizika (Milojević i Terzić, 2014.). U jeku je debata učesnika na finansijskim tržištima, kao i akademske javnosti, o distribuciji prinosa i rizika na finansijskim tržištima u cilju poboljšanja njihovog modelovanja kako bi se povećala upotrebna vrednost i preciznost ovih modela. KeywordsModel ...

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Section: Data and Empirical Resultsunclassified

Section: Data and Empirical Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Value at risk estimation and validation in the Serbian capital market in the period 2005-2015

Jeremić¹,

Terzić²,

Milojević³

2016

Bankarstvo

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“…The conclusions are the same for 2013. While fat-tailness is not a well known characteristic of the grid frequency, it is a common topic in the financial industry [12], [13], e.g. when dealing with asset returns, and it is a practical concern for the modeller.…”

Section: Fat-tailness Of the Grid Frequencymentioning

confidence: 99%

Stochastic programming for valuing energy storage providing primary frequency control

Mercier

Jomaux

Jaeger

2016

2016 IEEE International Energy Conference (ENERGYCON)

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This paper introduces a novel modelling of the economics of energy storages providing frequency containment reserves. The focus lies both on the practical operation of day-ahead markets and the stochastic and autocorrelation characteristics of the grid frequency as they are practical concerns for any energy storage operator. Based on yearly series of grid-frequency measurements we put in light seasonal trends in the grid frequency. After having removed these trends we build an ARMA-GARCH model with fat-tail feature to explain the remaining autocorrelation. The overall model can be used to generate scenarios of 15-minute time-step frequency deviations and we explain how these scenarios can be used along with stochastic programming to dispatch and compute the revenues of a storage providing frequency containment reserves. By considering two different cases, battery storage and variable-speed pumped-storage, the paper emphasizes the importance of the technical characteristics of energy ... Abstract-This paper introduces a novel modelling of the economics of energy storages providing frequency containment reserves. The focus lies both on the practical operation of dayahead markets and the stochastic and autocorrelation characteristics of the grid frequency as they are practical concerns for any energy storage operator. Based on yearly series of gridfrequency measurements we put in light seasonal trends in the grid frequency. After having removed these trends we build an ARMA-GARCH model with fat-tail feature to explain the remaining autocorrelation. The overall model can be used to generate scenarios of 15-minute time-step frequency deviations and we explain how these scenarios can be used along with stochastic programming to dispatch and compute the revenues of a storage providing frequency containment reserves. By considering two different cases, battery storage and variable-speed pumped-storage, the paper emphasizes the importance of the technical characteristics of energy storages providing frequency containment reserves.

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“…The general issue with downside risk modeling is that the tails of the distribution are important but the sample usually contains only a few data points from the tails which implies that the model needs to be based on some parametric hypothesis or a statistical theory about the behavior of extreme losses such as extreme value theory (EVT). See Stoyanov et al (2011) for a discussion on fat-tailed models for risk estimation and Embrechts, Klüppelberg, and Mikosch (2004) for applications of EVT in insurance and finance.…”

Section: Introductionmentioning

confidence: 99%

Computational aspects of portfolio risk estimation in volatile markets: a survey

Fabozzi

Stoyanov

Rachev³

2013

Studies in Nonlinear Dynamics and Econometrics

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Portfolio risk estimation requires appropriate modeling of fat-tails and asymmetries in dependence in combination with a true downside risk measure. In this survey, we discuss computational aspects of a Monte Carlo based framework for risk estimation and risk capital allocation. We review different probabilistic approaches focusing on practical aspects of statistical estimation and scenario generation. We discuss value-at-risk and conditional value-at-risk and comment on the implications of using a fat-tailed Monte Carlo framework for the reliability of risk estimates including model risk and Monte Carlo variability.

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Fat-Tailed Models for Risk Estimation

Cited by 49 publications

References 24 publications

Value at risk estimation and validation in the Serbian capital market in the period 2005-2015

Value at risk estimation and validation in the Serbian capital market in the period 2005-2015

Stochastic programming for valuing energy storage providing primary frequency control

Computational aspects of portfolio risk estimation in volatile markets: a survey

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