Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study

Sun, Yiguo; Wu, Ximing

doi:10.3390/jrfm11020029

Cited by 4 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these models tail dependence in the upper left corner λ U L M is stronger than the one in the lower right corner λ LR M . This means that joint extreme comovements, where the stock index decreases and the volatility index increases are more likely to occur than vice versa, which agrees with the statement that the market reacts more extreme in bad market situations (Sun and Wu (2018)). The time varying estimates for Kendall's τ and the tail dependence coefficients are shown in Figure 3.…”

Section: Two Step Estimationsupporting

confidence: 88%

Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation

Kreuzer

Czado

2020

Journal of Computational and Graphical Statistics

View full text Add to dashboard Cite

Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as nonlinear state space models for which inference is not straightforward due to the high number of parameters. Therefore maximum likelihood methods are often infeasible and researchers rely on alternative techniques, such as Gibbs sampling. But conventional Gibbs samplers are often tailored to specific situations and suffer from high autocorrelation among repeated draws. We present a Gibbs sampler for general nonlinear state space models with an univariate autoregressive state equation. For this we employ an interweaving strategy and elliptical slice sampling to exploit the dependence implied by the autoregressive process. Within a simulation study we demonstrate the efficiency of the proposed sampler for bivariate dynamic copula models. Further we are interested in modeling the volatility return relationship. Therefore we use the proposed sampler to estimate the parameters of stochastic volatility models with skew Student t errors and the parameters of a novel bivariate dynamic mixture copula model. This model allows for dynamic asymmetric tail dependence. Comparison to relevant benchmark models, such as the DCC-GARCH or a Student t copula model, with respect to predictive accuracy shows the superior performance of the proposed approach.

show abstract

Section: Two Step Estimationsupporting

confidence: 88%

Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation

Kreuzer

Czado

2020

Journal of Computational and Graphical Statistics

View full text Add to dashboard Cite

show abstract

“…There is a long-standing academic interest in studying the relation between underlying equity returns and implied volatility (indicatively, Fleming et al 1995;Whaley 2000;Allen et al 2013;Sun and Wu 2018). Comparatively fewer studies focus on the relation between implied volatility and future returns of the underlying assets.…”

Section: Introductionmentioning

confidence: 99%

VIX Futures as a Market Timing Indicator

Fassas

Hourvouliades

2019

JRFM

View full text Add to dashboard Cite

Our work relates to the literature supporting that the VIX also mirrors investor sentiment and, thus, contains useful information regarding future S&P500 returns. The objective of this empirical analysis is to verify if the shape of the volatility futures term structure has signaling effects regarding future equity price movements, as several investors believe. Our findings generally support the hypothesis that the VIX term structure can be employed as a contrarian market timing indicator. The empirical analysis of this study has important practical implications for financial market practitioners, as it shows that they can use the VIX futures term structure not only as a proxy of market expectations on forward volatility, but also as a stock market timing tool.

show abstract

“…The papers in the collection cover a number of different topics. Sun and Wu (2018) study the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method, where they propose a conditional dependence index to investigate how the dependence between the two series varies across different segments of the market return distribution. Kalaitzidakis et al (2018) use a smooth coefficient semi-parametric model to examine the effect of emissions, as measured by carbon dioxide (CO 2 ), on economic growth among a set of OECD countries during the period 1981-1998 and they estimate directly the output elasticity of emissions.…”

mentioning

confidence: 99%

Nonparametric Econometric Methods and Applications

Stengos

2019

JRFM

View full text Add to dashboard Cite

An area of very active research in econometrics over the last 30 years has been that of nonand semi-parametric methods. These methods have provided ways to complement more-traditional parametric approaches in terms of robust alternatives, as well as preliminary data analysis. The present Special Issue collects a number of new contributions, both theoretical and empirical that cover a wide spectrum of areas such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth as well as statistical theory and methodology.

show abstract

Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study

Cited by 4 publications

References 43 publications

Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation

Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation

VIX Futures as a Market Timing Indicator

Nonparametric Econometric Methods and Applications

Contact Info

Product

Resources

About