scholarly journals Modeling Extreme Events: Time-Varying Extreme Tail Shape

2020 ◽  
Author(s):  
Bernd Schwaab ◽  
Xin (Kelvin) Zhang ◽  
Andre Lucas
Keyword(s):  
Extreme Events ◽  
Time Varying ◽  
Tail Shape

scholarly journals Modeling Extreme Events: Time-Varying Extreme Tail Shape

2021 ◽  
Author(s):  
Bernd Schwaab ◽  
Andre Lucas ◽  
Xin Zhang
Keyword(s):  
Extreme Events ◽  
Time Varying ◽  
Tail Shape

Looking Beyond Wine Risk-Adjusted Performance

2020 ◽  
Vol 15 (2) ◽  
pp. 229-259 ◽  
Author(s):  
Frantz Maurer ◽  
Jean-Marie Cardebat ◽  
Linda Jiao
Keyword(s):  
Extreme Events ◽  
Stock Markets ◽  
Secondary Market ◽  
Garch Models ◽  
Time Varying ◽  
Daily Data ◽  
Volatility Asymmetry ◽  
Stock Indexes ◽  
Jel Classifications

AbstractIn this paper, we use copula-GARCH models applied to daily data from March 2010 to March 2018 to test the time-varying dependence of the Liv-ex 50, a secondary market fine wine index comprised of the ten most recent vintages of the five Bordeaux First Growths, with a portfolio composed of the six main stock markets (S&P 500, CAC 40, DAX 30, FTSE 100, and Hang Seng). Our results suggest that the Liv-ex 50 underperforms the six stock indexes, but provides diversification benefits in terms of volatility, asymmetry, and extreme events. (JEL Classifications: G110, G120, Q14)

scholarly journals EXTREME EVENTS AND THE OIL MARKET: CONDITIONAL JUMP PROCESS

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
MAX C. RESENDE ◽  
EVANDRO C. PEDRO
Keyword(s):  
Extreme Events ◽  
Design Methodology ◽  
Price Level ◽  
Jump Process ◽  
Efficient Market Hypothesis ◽  
Policy Formation ◽  
Time Varying ◽  
Short Term ◽  
Oil Market ◽  
Light Oil

ABSTRACT Purpose: This research aims to analyse price movements in the oil market stimulated by extreme events such as oil platform explosions, geopolitical events, and financial crises and to understand the reaction and the persistence of these effects on the commodity’s price. Originality/value: The prominent position of oil raises the concerns of investors, producers, and policymakers because of the unstable behaviour of its price level and pattern of volatility. This justifies the need to investigate the dynamics of this behaviour for the purposes of economic policy formation, strategies around trade and costs, and revenue calculations for companies of this sector, as well as investment decisions for other sources of energy. Design/methodology/approach: In order to model the occurrence of volatility jumps caused by extreme events, four specifications were used for the ARJI-GARCH conditional jumping methodology developed by Chan and Maheu (2002). The data consist of 2008 daily records of the closing price of light oil (WTI) from January 2010 to December 2017 obtained from NYMEX. Findings: Among several results it was verified that the occurrence of extreme events causes significant changes in the oil price, which goes against the efficient market hypothesis, and that a time-varying conditional jump process can be specified, but it has little sensibility to past shocks and very short-term persistence.

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