Full versus quasi MLE for ARMA-GARCH models with infinitely divisible innovations

Goode, Jimmie; Kim, Young S.; Fabozzi, Frank J.

doi:10.1080/00036846.2015.1042203

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…This step is performed through the garchFit function of the package fGarch of R. While for the multivariate models we consider a normal distributional assumption to extract the innovations, for the copula model we directly consider the skew-t distributional assumption. The former approach can be viewed as a quasi maximum-likelihood-estimation (QMLE) approach (see Goode et al (2015)). Additionally, at each estimation step we verify if the autoregressive component is statistically significant: if it is not, we estimate the model without the autoregressive component.…”

Section: Resultsmentioning

confidence: 99%

CoVaR with volatility clustering, heavy tails and non-linear dependence

Bianchi,

De Luca,

Rivieccio

2020

Preprint

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In this paper we estimate the conditional value-at-risk by fitting different multivariate parametric models capturing some stylized facts about multivariate financial time series of equity returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is got by AR-GARCH dynamics of the GJR type, the other stylized facts are captured through non-Gaussian multivariate models and copula functions. The CoVaR ≤ is computed on the basis on the multivariate normal model, the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic model (MGH) and four possible copula functions. These risk measure estimates are compared to the CoVaR = based on the multivariate normal GARCH model. The comparison is conducted by backtesting the competitor models over the time span from January 2007 to March 2020. In the empirical study we consider a sample of listed banks of the euro area belonging to the main or to the additional global systemically important banks (GSIBs) assessment sample.

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Section: Resultsmentioning

confidence: 99%

CoVaR with volatility clustering, heavy tails and non-linear dependence

Bianchi,

De Luca,

Rivieccio

2020

Preprint

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“…Furthermore, the methodology of this work follows the footsteps of the discussions of [24], but somewhat different since neither no closed forms for VaR and CVaR under EVD is given nor a direct application on the GARCH process is given in Reference [24] for forecasting the risk on the tickers considered in this paper. For more, an interested reader may refer to Reference [25].…”

Section: Motivation and Article's Planmentioning

confidence: 99%

On the Statistical GARCH Model for Managing the Risk by Employing a Fat-Tailed Distribution in Finance

et al. 2020

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The Conditional Value-at-Risk (CVaR) is a coherent measure that evaluates the risk for different investing scenarios. On the other hand, since the extreme value distribution has been revealed to furnish better financial and economical data adjustment in contrast to the well-known normal distribution, we here employ this distribution in investigating explicit formulas for the two common risk measures, i.e., VaR and CVaR, to have better tools in risk management. The formulas are then employed under the generalized autoregressive conditional heteroskedasticity (GARCH) model for risk management as our main contribution. To confirm the theoretical discussions of this work, the daily returns of several stocks are considered and worked out. The simulation results uphold the superiority of our findings.

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“…Financial risk measurement and portfolio reversion closely rely on the modeling of underlying asset dynamics (Kim et al, 2011;Goode et al, 2015). Abundant evidence has contradicted the normal distribution assumption in returns distribution which exhibits leptokurtosis, asymmetry and volatility clustering phenomena, leading to stochastic jumps in stock prices.…”

Section: Introductionmentioning

confidence: 99%

Measuring financial risk and portfolio reversion with time changed tempered stable Lévy processes

Gong

Xin-tian

2017

The North American Journal of Economics and Finance

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Given that underlying assets in financial markets exhibit stylized facts such as leptokurtosis, asymmetry, clustering properties and heteroskedasticity effect, this paper applies the stochastic volatility models driven by tempered stable Lévy processes to construct time changed tempered stable Lévy processes (TSSV) for financial risk measurement and portfolio reversion. The TSSV model framework permits infinite activity jump behaviors of returns dynamics and time varying volatility consistently observed in financial markets by introducing time changing volatility into tempered stable processes which specially refer to normal tempered stable (NTS) distribution as well as classical tempered stable (CTS) distribution, capturing leptokurtosis, fat tailedness and asymmetry features of returns in addition to volatility clustering effect in stochastic volatility. Through employing the analytical characteristic function and fast Fourier transform (FFT) technique, the closed form formulas for probability density function (PDF) of returns, value at risk (VaR) and conditional value at risk (CVaR) can be derived. Finally, in order to forecast extreme events and volatile market, we perform empirical researches on Hangseng index to measure risks and construct portfolio based on risk adjusted reward risk stock selection criteria employing TSSV models, with the stochastic volatility normal tempered stable (NTSSV) model producing superior performances relative to others.

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Full versus quasi MLE for ARMA-GARCH models with infinitely divisible innovations

Cited by 11 publications

References 25 publications

CoVaR with volatility clustering, heavy tails and non-linear dependence

CoVaR with volatility clustering, heavy tails and non-linear dependence

On the Statistical GARCH Model for Managing the Risk by Employing a Fat-Tailed Distribution in Finance

Measuring financial risk and portfolio reversion with time changed tempered stable Lévy processes

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