Managing extreme risks in tranquil and volatile markets using conditional extreme value theory

Byström, Hans

doi:10.1016/j.irfa.2004.02.003

Cited by 79 publications

(49 citation statements)

References 14 publications

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“…13 This result is consistent with that of Byström (2004), in which it is stated that for the lower tail of the DJI index (December 14, 1983to September 8, 1999 and within the out-of-sample estimation the EVTbased models do a better job for confidence levels equal or higher than 99%. …”

Section: Panel A: Djisupporting

confidence: 82%

“…This theory has been increasingly playing a role in many research areas such as hydrology and climatology where extreme events are not infrequent and can involve important negative (or positive) consequences and, more recently, there has been a number of extreme value studies in the finance literature. Some examples include Embrechts et al (1999), who present a broad basis for understanding the extreme value theory with applications to finance and insurance; Liow (2008), who compares the extreme behaviour of securitized real state and equity market indices representing Asian, European and North American markets; Danielsson and de Vries (1997), who test the predictive performance of various VaR 2 methods for simulated portfolios of seven US stocks concluding that EVT is particularly accurate as tails become more extreme whereas the conventional variance-covariance and the historical simulation methods under-and over-predict losses, respectively; similar results are found in Longin (2000) 3 , Assaf (2009) 4 and Bekiros and Georgoutsos (2005) 5 ; Danielsson and Morimoto (2000) apply EVT to Japanese financial data to confirm the accuracy and stability of this methodology over the GARCH-type techniques; Byström (2004) focuses on the negative distribution tails of the Swedish AFF and the U.S. DOW indices to compare EVT with generalized ARCH approaches and finds EVT to be a generally superior approach both for standard and more extreme VaR quantiles. Nevertheless,…”

Section: Introductionmentioning

confidence: 81%

“…6 For instance, Byström (2004) The remainder of this paper is organized as follows. Section 2 describes and carries out a preliminary analysis of the data set.…”

Section: Fernández (2005) Uses a Sample Comprised Of Several Financiamentioning

confidence: 99%

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Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation

Furió

Diranzo

2013

Quantitative Finance

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Although stock prices fluctuate, the variations are relatively small and are frequently assumed to be normal distributed on a large time scale. But sometimes these fluctuations can become determinant, especially when unforeseen large drops in asset prices are observed that could result in huge losses or even in market crashes. The evidence shows that these events happen far more often than would be expected under the generalized assumption of normal distributed financial returns. Thus it is crucial to properly model the distribution tails so as to be able to predict the frequency and magnitude of extreme stock price returns. In this paper we follow the approach suggested by McNeil and Frey (2000) and combine the GARCH-type models with the Extreme Value Theory (EVT) to estimate the tails of three financial index returns DJI, FTSE 100 and NIKKEI 225 representing three important financial areas in the world.Our results indicate that EVT-based conditional quantile estimates are much more accurate than those from conventional AR-GARCH models assuming normal or Student's t-distribution innovations when doing out-of-sample estimation (within the insample estimation, this is so for the right tail of the distribution of returns).

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Section: Panel A: Djisupporting

confidence: 82%

Section: Introductionmentioning

confidence: 81%

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Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation

Furió

Diranzo

2013

Quantitative Finance

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“…GARCH), standardise the returns by the estimated conditional volatility and proceed in EVT analysis. This approach has received attention by McNeil and Frey (2000) and Byström (2004). However, additional parameters have to be estimated which make this approach subject to increased estimation error and model risk.…”

Section: Applying Extreme Value Theory To Estimate Value-at-riskmentioning

confidence: 99%

Value-at-risk and extreme value distributions for financial returns

Tolikas¹

2008

JOR

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The ability of the Generalised Extreme Value (GEV) and Generalised Logistic (GL) distributions to fit extreme financial returns in the stock, commodities and bond markets is assessed. The empirical results indicate that the too much celebrated GEV is not the most appropriate model for the data since the fatter tailed GL is found to provide better descriptions of the extreme returns. Extreme Value Theory (EVT) based VaR estimates are then derived and compared to those generated by traditional methods. The results show that when the focus is on the really ruinous events which are located deep into the tails of the returns distribution, the EVT methods used in this study can be particularly useful since they produce VaR estimates that outperform those derived by the traditional methods at high confidence levels. However, these estimates were found to be considerably higher than those derived by traditional VaR models; consequently leading to higher capital reserves for financial institutions.

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“…For a set of European stock markets, Poon et al (2004) find that extreme dependence among these countries is much stronger in bear markets than in bull markets, and that some of this dependence is related to volatility co-clustering. Byström (2004) shows that the 50 most extreme losses for the Swedish index AFF (Affärvärlden's Generalindex) and the Dow Jones Industrial Average index during the period from 1980 to 1999 occur within the same month for half of the extremes, while two-thirds occur within the same quarter.…”

Section: Clustering Of Extreme Events Across Timementioning

confidence: 99%