Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures

Gong, Xiao-Li; Liu, Xihua; Xiong, Xiong

doi:10.1016/j.pacfin.2019.03.010

Cited by 27 publications

(10 citation statements)

References 41 publications

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“…Let

r_{t}

be the return at time

t

. The AR(

m

)‐GJR(

p, q

) model is specified as

r_{t} = c_{0} + \sum_{i = 1}^{m} c_{i} r_{t - i} + e_{t},

h_{t} = w + \sum_{i = 1}^{p} α_{i} e_{t - i}^{2} + \sum_{j = 1}^{q} β_{j} h_{t - j} + \sum_{i = 1}^{p} γ_{i} e_{t - i}^{2} I (e_{t - i} < 0),

z_{t} = e_{t} ∕ \sqrt{h_{t}},

where

e_{t}

is the residual of the return

r_{t}

;

h_{t}

is the conditional variance;

I (e_{t - i} < 0)

is an indicator function with a value of 1 if

e_{t - i} < 0

and 0 otherwise, and

z_{t}

is the standardized residual following the skewed Student‐

t

distribution (Gong et al, 2019).…”

Section: Dynamic Mrs‐copula‐evt Modelmentioning

confidence: 99%

The dynamics of cross‐boundary fire—Financial contagion between the oil and stock markets

Wang

2021

Journal of Futures Markets

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Motivated by the complex dynamics between the oil and stock markets, this study develops a dynamic Markov regime switching‐copula‐extreme value theory model to quantitatively investigate financial contagion and its characteristics between these two markets. The proposed model is applied to daily returns on crude oil prices and the stock markets in the United States and China over six major extreme downside risk events. We find that financial contagion is shorter, stronger, and more susceptible to extreme downside shocks in the United States than in China. In addition, the COVID‐19 crisis shows the largest financial contagion compared with previous crises.

show abstract

“…Let

r_{t}

be the return at time

t

. The AR(

m

)‐GJR(

p, q

) model is specified as

r_{t} = c_{0} + \sum_{i = 1}^{m} c_{i} r_{t - i} + e_{t},

h_{t} = w + \sum_{i = 1}^{p} α_{i} e_{t - i}^{2} + \sum_{j = 1}^{q} β_{j} h_{t - j} + \sum_{i = 1}^{p} γ_{i} e_{t - i}^{2} I (e_{t - i} < 0),

z_{t} = e_{t} ∕ \sqrt{h_{t}},

where

e_{t}

is the residual of the return

r_{t}

;

h_{t}

is the conditional variance;

I (e_{t - i} < 0)

is an indicator function with a value of 1 if

e_{t - i} < 0

and 0 otherwise, and

z_{t}

is the standardized residual following the skewed Student‐

t

distribution (Gong et al, 2019).…”

Section: Dynamic Mrs‐copula‐evt Modelmentioning

confidence: 99%

The dynamics of cross‐boundary fire—Financial contagion between the oil and stock markets

Wang

2021

Journal of Futures Markets

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show abstract

“…e characteristics of exchange rate volatility have always been a hot topic for scholars, gradually showing new characteristics with different financial and economic environments. Based on the empirical analysis of linear and nonlinear GARCH models, foreign scholars have obtained four nonlinear characteristics: thick tail and clustering, asymmetry, and negative correlation [1][2][3]. ick tail and clustering refer to the high probability and clustering of extreme values in financial time series.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on RMB Exchange Rate Volatility Risk Based on MSGARCH-VaR Model

Zhu

Zhou

2020

Discrete Dynamics in Nature and Society

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This paper captures the RMB exchange rate volatility using the Markov-switching GARCH (MSGARCH) models and traditional single-regime GARCH models. Through the Markov Chain Monte Carlo (MCMC) method, the model parameters are estimated to study the volatility dynamics of the RMB exchange rate. Furthermore, we compare the MSGARCH models to the single-regime GARCH specifications in terms of Value-at-Risk (VaR) prediction accuracy. According to the Deviance information criterion method, the research shows that MSGARCH models outperform the single-regime specifications in capturing the complexity of RMB exchange rate volatility. After the RMB exchange rate reform in 2015, the volatility is more asymmetric and persistent, and the probability of being in the turbulent volatility regime is significantly increased. The continuous escalation of Sino-US trade friction has increased the VaR of RMB exchange rate log-returns. From the evaluation results of the actual over expected exceedance ratio (AE), the conditional coverage (CC) test, and the dynamic quantile (DQ) test, we find strong evidence that two-regime MSGARCH models could forecast VaR more accurately, which provides practical value for China’s foreign exchange management authorities to manage the financial risk.

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“…It is also commonly used in the extended ARMA models. Gong et al [ 8 ] modified the conventional two‐stage maximum likelihood estimation method by non‐Gaussian QML estimator for the ARMA‐GJR‐GARCH process. Lennon and Yuan [ 13 ] estimated the parameters for their proposed multivariable ARMA model by a log‐likelihood function and partial derivatives.…”

Section: Introductionmentioning

confidence: 99%

A study of the impact of COVID‐19 on the Chinese stock market based on a new textual multiple ARMA model

et al. 2022

Statistical Analysis

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Coronavirus 2019 (COVID‐19) has caused violent fluctuation in stock markets, and led to heated discussion in stock forums. The rise and fall of any specific stock is influenced by many other stocks and emotions expressed in forum discussions. Considering the transmission effect of emotions, we propose a new Textual Multiple Auto Regressive Moving Average (TM‐ARMA) model to study the impact of COVID‐19 on the Chinese stock market. The TM‐ARMA model contains a new cross‐textual term and a new cross‐auto regressive (AR) term that measure the cross impacts of textual emotions and price fluctuations, respectively, and the adjacent matrix which measures the relationships among stocks is updated dynamically. We compute the textual sentiment scores by an emotion dictionary‐based method, and estimate the parameter matrices by a maximum likelihood method. Our dataset includes the textual posts from the Eastmoney Stock Forum and the price data for the constituent stocks of the FTSE China A50 Index. We conduct a sliding‐window online forecast approach to simulate the real‐trading situations. The results show that TM‐ARMA performs very well even after the attack of COVID‐19.

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Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures

Cited by 27 publications

References 41 publications

The dynamics of cross‐boundary fire—Financial contagion between the oil and stock markets

The dynamics of cross‐boundary fire—Financial contagion between the oil and stock markets

Research on RMB Exchange Rate Volatility Risk Based on MSGARCH-VaR Model

A study of the impact of COVID‐19 on the Chinese stock market based on a new textual multiple ARMA model

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