Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets

Cifter, Atilla

doi:10.1016/j.physa.2011.02.033

Cited by 33 publications

(14 citation statements)

References 28 publications

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“…The GPD distribution estimators obtained a better performance since, in both cases, either in the traditional way or corrected by the extremal index , the VaR could be better adjusted. Consequently, the results corroborated the findings of Geyçay et al (2003), Silva and Mendes (2003), Geyçay andSelçuk (2004), andCifter (2011), which pointed out the EVT-GPD as the finest VaR estimators.…”

Section: Resultssupporting

confidence: 90%

Value-at-risk in times of crisis: An analysis in the Brazilian market

Gaio¹,

Pimenta²,

Lima³

et al. 2015

Afr. J. Bus. Manage.

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Section: Resultssupporting

confidence: 90%

Value-at-risk in times of crisis: An analysis in the Brazilian market

Gaio¹,

Pimenta²,

Lima³

et al. 2015

Afr. J. Bus. Manage.

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“…This technique has already been applied for several purposes in finance and resource economics; for instance, Lo Cascio (2007) decomposes UK real gross domestic product via wavelet filter to investigate the long-run structure of the data apart from external shocks. Cifter (2011) makes use of the wavelet decomposition to determine the parameters of the generalized Pareto distribution (GPD). Reboredo and Riveira-Castro (2014) analyze dependency between oil prices and stock returns; Jammazi and Aloui (2012) combine wavelets with neural networks to investigate crude oil prices; and Tiwari et al (2013) assess oil price dependence via wavelet analysis.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis

Berger

2015

Journal of Forecasting

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We transform financial return series into its frequency and time domain via wavelet decomposition to separate shortrun noise from long-run trends and assess the relevance of each frequency to value-at-risk (VaR) forecast. Furthermore, we analyze financial assets in calm and turmoil market times and show that daily 95% VaR forecasts are mainly driven by the volatility that is captured by the first scales comprising the short-run information, whereas more timescales are needed to adequately forecast 99% VaR. As a result, individual timescales linked via copulas outperform classical parametric VaR approaches that incorporate all information available.

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“…McNeil and Frey (2000) [11], Karmakar and Shukla (2015) [19], Bee et al (2016) [20], Totić and Božović (2016) [21], Li (2017) [22], Fernandez (2003) [12], Jadhav and Ramanathan (2009) [13] and Huang et al (2017) [23] chose the 90th quantile of the loss distribution as a threshold. In contrast, a less conservative, but fixed threshold was used by Gençay and Selçuk (2004) [14], Cifter (2011) [24], Soltane et al (2012) [25].…”

Section: Introductionmentioning

confidence: 99%

Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection

Echaust

Just

2020

Mathematics

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A conditional Extreme Value Theory (GARCH-EVT) approach is a two-stage hybrid method that combines a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) filter with the Extreme Value Theory (EVT). The approach requires pre-specification of a threshold separating distribution tails from its middle part. The appropriate choice of a threshold level is a demanding task. In this paper we use four different optimal tail selection algorithms, i.e., the path stability method, the automated Eye-Ball method, the minimization of asymptotic mean squared error method and the distance metric method with a mean absolute penalty function, to estimate out-of-sample Value at Risk (VaR) forecasts and compare them to the fixed threshold approach. Unlike other studies, we update the optimal fraction of the tail for each rolling window of the returns. The research objective is to verify to what extent optimization procedures can improve VaR estimates compared to the fixed threshold approach. Results are presented for a long and a short position applying 10 world stock indices in the period from 2000 to June 2019. Although each approach generates different threshold levels, the GARCH-EVT model produces similar Value at Risk estimates. Therefore, no improvement of VaR accuracy may be observed relative to the conservative approach taking the 95th quantile of returns as a threshold.

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Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets

Cited by 33 publications

References 28 publications

Value-at-risk in times of crisis: An analysis in the Brazilian market

Value-at-risk in times of crisis: An analysis in the Brazilian market

Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis

Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection

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