Value-at-risk for long and short trading positions: Evidence from developed and emerging equity markets

Diamandis, Panayiotis F.; Drakos, Anastassios A.; Κουρέτας, Γεώργιος Π.; Zarangas, Leonidas

doi:10.1016/j.irfa.2011.02.009

Cited by 29 publications

(21 citation statements)

References 25 publications

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“…In particular, we are consistent with the recent evidence provided by Diamandis et al (2011), who investigated three groups of stock market indices: Developed, Southeast Asia and Latin America, and found that the skewed student distribution improves considerably the forecasts of one day ahead VaR for both long and short trading positions. We are also congruent with Giot and Laurent (2004), who found that the specification based on skewed Student-t distribution is superior for forecasting value at risk of the SP500 as well as the CAC40.…”

Section: Introductionsupporting

confidence: 91%

“…For instance, we are similar to Giot and Laurent (2004) and Diamandis et al (2011) Student-t distribution is more accurate, particularly for long trading positions. Similar to our evidence, they also found that the advantage is less when short trading positions are considered.…”

Section: Review Of Middle Eastsupporting

confidence: 61%

“…In the FIPARCH model, we follow Laurnet (2003, 2004), Tang and Shieh (2006), Wu and Shieh (2007), McMillan and Kambouridis (2009) and Diamandis et al (2011), and consider innovations that follow three different distributions: the normal, the Student-t, and the skewed Student-t distribution 10 . We then aim at evaluating the predictive accuracy of the model under the assumption of different distributions in forecasting VaR.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations

Maghyereh¹,

Awartani²

2012

Review of Middle East Economics and Finance

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In this paper, we investigate the adequacy of the fractionally integrated asymmetric power model to measure value at risk in the United Arab Emirates stock exchanges. Our empirical results show that the accuracy of the model is improved when value at risk is computed using innovations modeled as skewed Student-t distribution. Including a long memory in the conditional volatility process would also improve the results. We conclude that, the modeling of asymmetry, fat tails and long memory have potentially important implications for risk assessment, and hedging strategies in the UAE stock exchanges.

show abstract

Section: Introductionsupporting

confidence: 91%

Section: Review Of Middle Eastsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

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Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations

Maghyereh¹,

Awartani²

2012

Review of Middle East Economics and Finance

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“…The asymmetric power ARCH (APARCH) model developed by Ding, Granger, and Engle (1993) is considered as highly flexible, since it can nest a large variety of other GARCH-type models. Some authors (see Diamandis, Drakos, Kouretas, & Zarangas, 2011;Giot & Laurent, 2003;Nieto & Ruiz, 2016;Rodríguez & Ruiz, 2012) suggest measuring the VaR using asymmetric models, such as APARCH to cope with different responses of the volatility to negative and positive shocks. The fractionally integrated version of this model (FIAPARCH) is not recommended in this context, because incorporating a long-memory effect in the volatility while computing the VaR is against the Basel accords, which requires short-run forecasts (Nieto & Ruiz, 2016).…”

Section: Regime-switching Aparch Modelmentioning

confidence: 99%

Value‐at‐risk under market shifts through highly flexible models

BenSaïda

Boubaker

Nguyen

et al. 2018

Journal of Forecasting

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Managing market risk under unknown future shocks is a critical issue for policymakers, investors, and professional risk managers. Despite important developments in market risk modeling and forecasting over recent years, market participants are still skeptical about the ability of existing econometric designs to accurately predict potential losses, particularly in the presence of hidden structural changes. In this paper, we introduce Markov‐switching APARCH models under the skewed generalized t and the generalized hyperbolic distributions to fully capture the fuzzy dynamics and stylized features of financial market returns and to generate value‐at‐risk (VaR) forecasts. Our empirical analysis of six major stock market indexes shows the superiority of the proposed models in detecting and forecasting unobservable shocks on market volatility, and in calculating daily capital charges based on VaR forecasts.

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“…The relevance of VaR analysis is also supported by the fact that market agents use this measure not only as an instrument for market risk quantification (for example, for the assessment of premiums for the insurance sector by Wang & Huang, 2016) but also for investment decisions, as an optimization criterion for asset allocation (Diamandis et al, 2011;Zhu et al, 2013;Lwin et al, 2014). VaR is applied for the analysis of stock markets and indices (Nieto & Ruiz (2016) for the S&P500), and bond markets (Gunay (2016) apply VaR for the analysis of 10-year government bonds of South Korea, Japan, Malaysia and Singapore).…”

Section: Introductionmentioning

confidence: 99%

Design and Comparison VaR Specifications for the Russian Market Risk Modeling

Теплова

Ruzanov

2019

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Traditionally standard deviation has been considered as the main risk measure of an asset portfolio. The relevance of VaR analysis is widely recognized as an instrument for market risk quantification for investment decisions, asset allocation. Based on common practice VaR is estimated on 10-day basis and using 99 % confidence interval. More accurate VaR estimation requires identifying the optimal VaR parameters. Our paper documents that historical VaR has some limitation for high volatile stock markets. We conduct empirical analysis of statistical tests of VaR estimation with frequency tests, magnitude tests, independence and autocorrelation tests for the Russian stock market. We propose an original algorithm for optimal VaR specification in terms of accuracy of VaR estimates. We used historical and semi parametric VaR (EWMA VaR and volatility adjusted VaR). For each method we consider 16 VaR specifications (which are different combinations of time horizons-120, 250, 500 and 1000 trading days, and confidence intervals-90 %, 95 %, 99 %, 99,5 %). We consider the unstable Russian stock market with two main Russian indexes-MICEX and RTS. Backtesting different VaR specifications show that annual 99 % VaR prevails over other VaR specifications for the Russian stock indices. The significance level of confidence 1-5 % are optimal on various time horizons. VaR with our method of algorithmically defined parameters is more effective than commonly used estimation procedure.

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Value-at-risk for long and short trading positions: Evidence from developed and emerging equity markets

Cited by 29 publications

References 25 publications

Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations

Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations

Value‐at‐risk under market shifts through highly flexible models

Design and Comparison VaR Specifications for the Russian Market Risk Modeling

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