Testing the Gaussian and Student's t copulas in a risk management framework

Lourme, Alexandre; Maurer, Frantz

doi:10.1016/j.econmod.2016.12.014

Cited by 22 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We analyze the time-varying dependence framework between oil and agricultural commodities using a dependence-switching CoVaR copula approach. Prior analyses have argued that a time invariant copula cannot model the real relationships, hence, they have allowed the parameters to vary in a copula function (Lourme and Maurer, 2017) or allowed the copula function to vary with time (Okimoto, 2008). Given the former framework is not indicative of the dependence switching between positive and negative correlation regimes, the latter framework is preferred.…”

Section: Introductionmentioning

confidence: 99%

Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach

et al. 2021

View full text Add to dashboard Cite

We examine the energy-food nexus using the dependence-switching copula model. Specifically, we look at the dependence for four distinct market states, such as, increasing oil-increasing commodity, declining oil-declining commodity, increasing oil-declining commodity, as well as declining oil-increasing commodity markets. Our results support the argument that the crash of oil markets and agricultural commodities happen at the same time, especially during crisis period. However, the same is not true during times of normal economic conditions, implying that investors cannot make excess profits in both agricultural and oil markets at once. Furthermore, our analysis suggests that the return chasing effect dominates for all commodities on maximum occasions. The CoVaR and ∆CoVaR results indicate important risk spillover from oil to agricultural markets, especially around the financial crisis.

show abstract

Section: Introductionmentioning

confidence: 99%

Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Many studies have used copula models to understand movements across markets (e.g. Ning, 2010;Meng and Liang, 2013;Reboredo & Ugolini, 2015;Kleinow & Moreira, 2016;Lourme & Maurer, 2017;Pircalabu & Benth, 2017;Ji et al, 2018). A copula is a multivariate cumulative distribution function with uniform marginal distributions on the interval (0, 1).…”

Section: Introductionmentioning

confidence: 99%

Is oil price risk systemic to sectoral equity markets of an oil importing country? Evidence from a dependence-switching copula delta CoVaR approach

et al. 2021

View full text Add to dashboard Cite

In this paper, a dependence-switching copula model is used for the first time to analyse the dependence structure between sectoral equity markets and crude oil prices for India, one of the largest oil importing countries. Specifically, we investigate the dependence and tail dependence for four distinctive states of the market, i.e. rising oil prices-rising equity markets, declining oil prices-declining equity markets, rising oil prices-declining equity markets, and declining oil prices-rising equity markets. Our results reveal that the tail dependence is symmetric (asymmetric) in positive (negative) correlation regimes. Based on the copula results, we estimate the systemic crude oil price risk to different sectors using CoVaR and delta CoVaR. A fleeting positive sectoral CoVaR and delta CoVaR across all sectors implies a time-varying oil price systemic risk. Yet, little difference between CoVaR and VaR across the sectors reveals that a bearish oil market does not add additional systemic risk to a bearish sectoral equity market. The carbon sector is found to be the safe haven investment when both the equity and the oil markets are in a downward phase.

show abstract

“…Various studies show that copulas can capture linear correlation as well as asymmetric dependence and upper and lower tail dependence, and thus provide a useful tool to obtain precise VaR estimations. In the latest studies, the authors compare the performances of different copula structures applied in risk analysis (Lourme and Maurer, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Time-varying copula models were introduced in the 2000s to describe the dependence structures between asset returns, which technically measures “the joint probability of events as a function of the marginal probabilities of each event” (Lourme and Maurer, 2017, p. 204). Various studies show that copulas can capture linear correlation as well as asymmetric dependence and upper and lower tail dependence, and thus provide a useful tool to obtain precise VaR estimations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Looking Beyond Wine Risk-Adjusted Performance

2020

Self Cite

View full text Add to dashboard Cite

In 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)

show abstract

Testing the Gaussian and Student's t copulas in a risk management framework

Cited by 22 publications

References 25 publications

Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach

Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach

Is oil price risk systemic to sectoral equity markets of an oil importing country? Evidence from a dependence-switching copula delta CoVaR approach

Looking Beyond Wine Risk-Adjusted Performance

Contact Info

Product

Resources

About