Volatility Modeling and Value-at-Risk (VaR) Forecasting of Emerging Stock Markets in the Presence of Long Memory, Asymmetry, and Skewed Heavy Tails

Gencer, Hatice Gaye; Demiralay, Sercan

doi:10.1080/1540496x.2014.998557

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…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). These fractionally integrated models provide, however, a good fit compared to their normal versions (Gencer & Demiralay, 2016;Slim, Koubaa, & BenSaïda, 2017).…”

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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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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“…Given these studies, mixed results exist in the literature regarding the impact of oil prices on BRICS during crises periods, due to different methodologies and scopes and the inability to clarify the role of skewness (Raheem et al 2020). Gaye Gencer and Demiralay (2016) proceeded to model the daily and one-week market vagaries with the conditional volatilities obtained from the FIAPARCH models and revealed that the results of the Student-t skewed distribution are the best for predicting the one-day VaR for all stock markets. According to Gaye Gencer and Demiralay (2016), this paper used long memory models with the aim of analyzing the long-range dependence and volatility clustering in oil and stock markets in the BRICS countries during the global financial crisis.…”

Section: Literature Reviewmentioning

confidence: 99%

Interdependence between the BRICS Stock Markets and the Oil Price since the Onset of Financial and Economic Crises

Bouslama

2023

JRFM

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In this paper, we use a copula to examine the relationship and dynamic dependence structure between the crude oil market and the BRICS countries’ stock indices expressed through financial crises, from the 2008 global financial crisis to COVID-19, based on daily data. We characterize the long-term relationship as well as the short-term dynamics and represent the interdependence between them. We also study the short-run conditional links through the considered variables under the effects of long-run interactions and the asymmetric volatility spillover relationship. In addition, we establish that the volatility transmission is stubborn and that the impact of the crises and our empirical findings prove that there is fractional co-integration between crude oil and financial markets. We notice that there are lengthy correlations between the variables, as we detect significant bidirectional causal links. In particular, we see positive short-run links and use an optimal copula coefficient to measure the risk spillovers between oil markets and financial markets that represent the dependence structure. For robustness purposes, based on a sliding-window analysis, we complement our investigation with VaR analysis.

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“…However, kurtosis-based projection pursuit, aimed at removing excess kurtosis, suffers from a crucial drawback: Kurtosis may not be defined for relevant distributions (e.g., the Student-t distribution with 4 or less degrees of freedom; for non-normal multivariate distributions, the fourth cumulant may be a null matrix), so that kurtosis might not be an appropriate projection index 29 (for the situation in emerging stock markets, see Ref. 30 ). Our low-dimensional deterministic approach masters the computational challenge posed by the highly skewed time series with extreme kurtosis in a computationally cheap manner.…”

Section: Android’s Market Positionmentioning

confidence: 99%

Exploiting deterministic features in apparently stochastic data

Stoop

Orlando

Bufalo

et al. 2022

Sci Rep

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Many processes in nature are the result of many coupled individual subsystems (like population dynamics or neurosystems). Not always such systems exhibit simple stable behaviors that in the past science has mostly focused on. Often, these systems are characterized by bursts of seemingly stochastic activity, interrupted by quieter periods. The hypothesis is that the presence of a strong deterministic ingredient is often obscured by the stochastic features. We test this by modeling classically stochastic considered real-world data from both, the stochastic as well as the deterministic approaches to find that the deterministic approach’s results level with those from the stochastic side. Moreover, the deterministic approach is shown to reveal the full dynamical systems landscape, which can be exploited for steering the dynamics into a desired regime.

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Volatility Modeling and Value-at-Risk (VaR) Forecasting of Emerging Stock Markets in the Presence of Long Memory, Asymmetry, and Skewed Heavy Tails

Cited by 11 publications

References 29 publications

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

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

Interdependence between the BRICS Stock Markets and the Oil Price since the Onset of Financial and Economic Crises

Exploiting deterministic features in apparently stochastic data

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