Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Degiannakis, Stavros; Floros, Christos; Dent, Pamela

doi:10.1016/j.irfa.2012.06.001

Cited by 42 publications

(39 citation statements)

References 49 publications

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“…We select the parameters of the data generation process to coincide with the parameters obtained for the daily returns of the North American market index Standard & Poor's 500 (S&P500) before and during the subprime crisis. We attribute this selection to the representativeness of this index, which is also employed in the example with real data and in various simulation studies for risk assessment in finance, such as Christoffersen and Gonçalves (2005) and Degiannakis et al (2013), among others.…”

Section: Illustrationsmentioning

confidence: 99%

Shortfall deviation risk: an alternative for risk measurement

Righi¹,

Ceretta²

2016

JOR

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We present the Shortfall Deviation Risk (SDR), a risk measure that represents the expected loss that occurs with certain probability penalized by the dispersion of results that are worse than such an expectation. SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating two fundamental pillars of the risk concept -the probability of adverse events and the variability of an expectation -and considers extreme results. We demonstrate that SD is a generalized deviation measure, whereas SDR is a coherent risk measure. We achieve the dual representation of SDR, and we discuss issues such as its representation by a weighted ES, acceptance sets, convexity, continuity and the relationship with stochastic dominance. Illustrations with real and simulated data allow us to conclude that SDR offers greater protection in risk measurement compared with VaR and ES, especially in times of significant turbulence in riskier scenarios.

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Section: Illustrationsmentioning

confidence: 99%

Shortfall deviation risk: an alternative for risk measurement

Righi¹,

Ceretta²

2016

JOR

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“…Para computar as previsões de VaR para múltiplos passos à frente, utilizouse o algoritmo de simulação de Monte Carlo apresentado por Degiannakis et al(2013) -6,211298 -6,222938 -6,219385 -6,225228 -6,210960 -6,221784 -6,218873 -6,223913 SBC -6,201843 -6,211908 -6,208354 -6,212621 -6,203081 -6,212329 -6,209418 - proposto por Engle e Ng (1993) para detectar a presença do efeito de alavancagem nos retornos, isto é, a estatística SBT testa se os impactos na volatilidade causados por retornos negativos são maiores que os impactos causados por retornos positivos. A hipótese nula do teste é de que não há diferença entre os impactos causados por retornos positivos e negativos.…”

Section: Cálculo De Var Para Múltiplos Passos à Frenteunclassified

“…Esses resultados diferem dos encontrados por Degiannakis et al(2013), cuja conclusão foi a de que os modelos FIGARCH não superavam os modelos GARCH com o aumento do horizonte de previsão para a modelagem dos retornos dos mercados dos países desenvolvidos.…”

Section: Estimativa Dos Parâmetros Utilizando Rolagens Diárias Para Aunclassified

“…Com relação aos modelos FIGARCH, destaca-se que os modelos FIGARCH-n e FIGARCH-ged apresentaram desempenho semelhante aos modelos GARCH-n e GARCH-ged para o horizonte de 1 dia à frente, mas para os horizontes de 10 e 20 dias à frente, esses modelos GARCH mostraram desempenho inferior, pois foram considerados inadequados mais vezes que os FIGARCH, pelo critério do teste de Christoffersen. Degiannakis et al(2013) uma parte dessa instabilidade pode ser devida ao próprio processo de modelagem do FIGARCH.…”

Section: Estimativa Dos Parâmetros Utilizando Rolagens Diárias Para Aunclassified

“…De acordo com as contribuições de Degiannakis et al(2013), Xekalaki e Degiannakis (2010), e Christoffersen (2012) …”

Section: Apêndiceunclassified

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Ensaios Em Gerenciamento De Riscos Financeiros De Países Emergentes

MORAES¹

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Para a minha esposa Priscila e meu filho Leonardo, meus maiores tesouros, pelo amor infinito em compreender o meu longo período de ausência motivado pela dedicação a esta Tese de Doutorado. Essa vitória não é só minha. É de toda a nossa família. Para os meus pais, Yêda e Carlos, e irmãos, Alessandra e Alan, pela torcida sincera e incondicional que acompanham todas as minhas realizações. PUC-Rio -Certificação Digital Nº 1212917/CA AgradecimentosA Deus pelas infinitas bençãos que tem derramado sobre a minha vida.À Diretoria de Finanças da Marinha, representada pelos Vice-Almirante (IM) Edesio Teixeira Lima Junior, Contra-Almirante (IM) Samy Moustapha e ContraAlmirante (IM) Hugo Cavalcante Nogueira, pelo apoio e confiança em mim depositada.Ao meu orientador, Prof. Dr. Antonio Carlos Figueiredo Pinto, pela simplicidade, praticidade e precisão com que me orientou para a realização deste trabalho.Ao Prof. Marcelo pela gentileza em compartilhar comigo seu "arsenal" de livros, artigos e softwares, todos relevantes para o bom andamento de minhas pesquisas.Ao meus amigos da Diretoria de Finanças da Marinha pela torcida e apoio.Aos meus colegas da PUC-Rio em especial aos amigos Flávio Val e Macelly Morais pelas longas conversas de desabafo e motivação.Aos professores que participaram da comissão examinadora pelas contribuições que enriqueceram este trabalho.A todos os professores do IAG pelos ensinamentos e ajuda.À Teresa Campos e Fabio Etienne, secretários do IAG, pela gentileza e cordialidade nos atendimentos de minhas demandas ao longo do curso.A todos os amigos e familiares que, de uma forma ou de outra, me incentivaram ou me ajudaram a concluir este trabalho. In this thesis we develop three essays on risk management in some emerging countries. On the first one, using models of the GARCH family, we verified that the increase in relative weights assigned to the earlier observations due to the increase of the forecast horizon results in better estimates of volatility.Through the use of seven forecasting models of volatility and return series of financial markets assets (shares of Petrobras and Vale, Bovespa index, exchange rate Real/Dollar, 1-year and 3 years interest rates of Brazilian Government bonds issued in Reais) the estimates obtained in the sample (in-sample) were compared with observations outside the sample (out-of-sample). Based on this comparison, it was found that the best estimates of expected volatility were obtained predominantly by two models that allow its parameters to vary depending on the forecast horizon: the modified EGARCH model (exponential generalized autoregressive conditional heteroskedastic) and the ARLS model proposed by Ederington and Guan (2005). We conclude that the use of traditional forecasting models of volatility, which keeps unchanged relative weights assigned to both old and new observations, regardless of the forecast horizon, was inappropriate. On the second essay we compared the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting value-at-...

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Forecasting volatility by integrating financial risk with environmental, social, and governance risk

Capelli¹,

Ielasi

Russo

2021

Corp Soc Responsibility Env

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The study aims to verify whether the consideration of a risk measure based on environmental, social, and governance (ESG) factors can reduce the difference between the ex‐ante financial risk and ex‐post volatility of financial assets. The statistical models are run on 17,996 firm‐year observations (3332 active firms from 55 countries and 10 industries, listed on the ECPI Global Ethical Equity index) in 2007–2015. According to our main results, the forecasting effectiveness of traditional financial risk measures can be improved by integrating financial risk with an ESG risk measure that considers the ESG entropy. We found that the dispersion of ESG scores within a country, sector and year is a risk factor that would be helpful in predicting the volatility of financial assets. Other similar long‐run risk measures, such as issuers' credit ratings, do not reveal the same forecasting power. By reducing unexpected volatility, especially in the medium term, the ESG risk measure provides investors and fund managers with a useful metric for decision making.

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Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Cited by 42 publications

References 49 publications

Shortfall deviation risk: an alternative for risk measurement

Shortfall deviation risk: an alternative for risk measurement

Ensaios Em Gerenciamento De Riscos Financeiros De Países Emergentes

Forecasting volatility by integrating financial risk with environmental, social, and governance risk

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