Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si86.gif" display="inline" overflow="scroll"><mml:mi>t</mml:mi></mml:math>-distribution

Nakajima, Jouchi; Omori, Yasuhiro

doi:10.1016/j.csda.2010.07.012

Cited by 86 publications

(86 citation statements)

References 41 publications

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“…In the case of stock returns this correlation is often negative and is known as the leverage effect (Nakajima and Omori, 2012). This assumption offers increased flexibility as it allows for the possibility of having skewness in the errors of our VAR equation in (1).…”

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confidence: 99%

Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

Geraci¹,

Gnabo²

2018

J. Financ. Quant. Anal.

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We propose a market-based framework that exploits time-varying parameter vector autoregressions to estimate the dynamic network of financial spillover effects. We apply it to financials in the Standard & Poor's 500 index and estimate interconnectedness at the sector and institution level. At the sector level, we uncover two main events in terms of interconnectedness: the Long Term Capital Management crisis and the 2008 financial crisis. After these crisis events, we find a gradual decrease in interconnectedness, not observable using the classical rolling window approach. At the institution level, our framework delivers more stable interconnectedness rankings over time than other market-based measures of systemic risk. Keywords

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confidence: 99%

Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

Geraci¹,

Gnabo²

2018

J. Financ. Quant. Anal.

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“…In all cases, we set (ψ 0 , ψ 1 , μ, φ, τ 2 ) = (0, −0.05, −10, 0.95, 0.15). The values of the hyperparameters were chosen following Nakajima and Omori (2012) to reflect the empirical results from the literature:…”

Section: Simulated Datamentioning

confidence: 99%

“…Tsiotas (2012) proposed the SV models with the asymmetric non-central t and normal distributions. Based on the generalised hyperbolic skew t distribution of Aas and Haff (2006), Nakajima and Omori (2012) extended the model with the t distribution such that the skewness is taken into account. Their model was further extended by Nakajima (2013) such that the skewness is allowed vary over time through the Markov switching process.…”

Section: Introductionmentioning

confidence: 99%

Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles

Kobayashi

2015

Comput Stat

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This paper proposes a unified framework to analyse the skewness, tail heaviness, quantiles and expectiles of the return distribution based on a stochastic volatility model using a new parametrisation of the skew exponential power (SEP) distribution. The SEP distribution can express a wide range of distribution shapes through two shape parameters and one skewness parameter. Since the asymmetric Laplace and skew normal distributions are included as special cases, the proposed model is related to quantile regression and expectile regression. The efficient and simple Markov chain Monte Carlo estimation methods are also described. The proposed model is demonstrated using the simulated data and real data on daily return of foreign exchange rate.Keywords Bayesian expectile regression · Bayesian quantile regression · Double two-piece family · Foreign exchange return · Markov chain Monte Carlo

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“…The previous studies (e.g. Nakajima and Omori, 2012) often assume that A c c e p t e d M a n u s c r i p t…”

Section: Gh Skew T-distributionmentioning

confidence: 99%

“…In contrast, the current paper proposes MSV models with leverage effect, where structural errors follow the GH skew t-distribution. This is a natural extension of standard univariate stochastic volatility processes with skew distributions (e.g., Durham, 2007, Nakajima and Omori, 2012, Silva et al, 2006 to multivariate analysis; time-varying covariance components are incorporated based on the Cholesky decomposition of volatility matrices, which is increasingly used in time series analysis (e.g., Lopes et al, 2012, Pinheiro and Bates, 1996, Smith and Kohn, 2002.…”

Section: Introductionmentioning

confidence: 99%

Bayesian analysis of multivariate stochastic volatility with skew return distribution

Nakajima

2014

Econometric Reviews

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Multivariate stochastic volatility models with skew distributions are proposed. Exploiting Cholesky stochastic volatility modeling, univariate stochastic volatility processes with leverage effect and generalized hyperbolic skew t-distributions are embedded to multivariate analysis with time-varying correlations. Bayesian modeling allows this approach to provide parsimonious skew structure and to easily scale up for high-dimensional problem.Analyses of daily stock returns are illustrated. Empirical results show that the time-varying correlations and the sparse skew structure contribute to improved prediction performance and Value-at-Risk forecasts.

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Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’st-distribution

Cited by 86 publications

References 41 publications

Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles

Bayesian analysis of multivariate stochastic volatility with skew return distribution

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