Value-at-Risk and Expected Shortfall When There Is Long Range Dependence

Härdle, Wolfgang Karl; Mungo, Julius

doi:10.2139/ssrn.2894310

Cited by 8 publications

(4 citation statements)

References 44 publications

(40 reference statements)

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“…Cardamone and Folkinshteyn (2007) study the sensitivity of U.S. interest rates and exchange rates. Härdle and Mungo (2008) focus on the value-at-risk of some stock indexes. McMillan and Kambouroudis (2009) compare the predictions of the HYGARCH model and other models for 31 stock indexes.…”

Section: Presentation Of the Semifarma-hygarch Modelmentioning

confidence: 99%

SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence

2012

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This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated for Dow Jones returns studied over a long period.

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Section: Presentation Of the Semifarma-hygarch Modelmentioning

confidence: 99%

SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence

2012

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“…Nevertheless, empirical studies have revealed that long memory GARCH (LM-GARCH) models are very successful in accurately forecasting the conditional volatility of asset returns and often outperform short memory GARCH type models (see, among others, Giot andLaurent, 2003, Degiannakis* (2004), Tang andShieh, 2006, Grané andVeiga (2008), Härdle and Mungo (2008), Morana, 2009, Demiralay andUlusoy, 2014 and Aloui and Ben Hamida, 2015).…”

Section: Introductionmentioning

confidence: 99%

Semiparametric GARCH models with long memory applied to value-at-risk and expected shortfall

Letmathe¹,

Feng²,

Uhde³

2022

JOR

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In this paper new semiparametric GARCH models with long memory are introduced. The estimation of the nonparametric scale function is carried out by an adapted version of the SEMIFAR algorithm (Beran and Feng, 2002a). Recurring on the revised recommendations by the Basel Committee to measure market risk in the banks' trading books (Basel Committee on Banking Supervision, 2013), these new semiparametric GARCH models are applied to obtain rolling one-step ahead forecasts for the Value at Risk (VaR) and Expected Shortfall (ES) for market risk assets.In addition, standard regulatory traffic light tests (Basel Committee on Banking Supervision, 1996) and a newly introduced traffic light test for the ES are carried out for all models. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long memory GARCH models are an attractive alternative to their conventional, parametric counterparts.

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“…So far there is no consensus which model or method is the most suitable for forecasting VaR and ES. Nevertheless, empirical studies have revealed that long memory GARCH (LM-GARCH) models are very successful in accurately forecasting the conditional volatility of asset returns and often outperform short memory GARCH type models (see, among others, Giot andLaurent, 2003, Degiannakis* (2004), Tang andShieh, 2006, Grané andVeiga (2008), Härdle and Mungo (2008), Baillie and Morana, 2009, Demiralay and Ulusoy, 2014and Aloui and Ben Hamida, 2015.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall

2021

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In this paper new semiparametric GARCH models with long memory are introduced. The estimation of the nonparametric scale function is carried out by an adapted version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised recommendations by the Basel Committee to measure market risk in the banks' trading books (Basel Committee on Banking Supervision, 2013), the semiparametric GARCH models are applied to obtain rolling one-step ahead forecasts for the Value at Risk (VaR) and Expected Shortfall (ES) for market risk assets.In addition, standard regulatory traffic light tests (Basel Committee on Banking Supervision, 1996) and a newly introduced traffic light test for the ES are carried out for all models. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long memory GARCH models are an attractive alternative to their conventional, parametric counterparts.

show abstract

Value-at-Risk and Expected Shortfall When There Is Long Range Dependence

Cited by 8 publications

References 44 publications

SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence

SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence

Semiparametric GARCH models with long memory applied to value-at-risk and expected shortfall

Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall

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