The Leverage Effect In Stochastic Volatility PDF Download
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Author | : Ionut Florescu |
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Total Pages | : 25 |
Release | : 2018 |
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Download A Study About the Existence of the Leverage Effect in Stochastic Volatility Models Book in PDF, ePub and Kindle
The empirical relationship between the return of an asset and the volatility of the asset has been well documented in the financial literature. Named the leverage e ffect or sometimes risk-premium effect, it is observed in real data that, when the return of the asset decreases, the volatility increases and vice-versa.Consequently, it is important to demonstrate that any formulated model for the asset price is capable to generate this eff ect observed in practice. Furthermore, we need to understand the conditions on the parameters present in the model that guarantee the apparition of the leverage effect. In this paper we analyze two general speci cations of stochastic volatility models and their capability of generating the perceived leverage effect. We derive conditions for the apparition of leverage e ffect in both of these stochastic volatility models. We exemplify using stochastic volatility models used in practice and we explicitly state the conditions for the existence of the leverage effect in these examples.
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Release | : 1998 |
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Download Research Report Book in PDF, ePub and Kindle
Author | : Hong Jiang |
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Total Pages | : 125 |
Release | : 2014 |
Genre | : Asset-liability management |
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Download A Stochastic Volatility Model with Leverage Effect and Regime Switching Book in PDF, ePub and Kindle
Author | : Amaan Mehrabian |
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Total Pages | : |
Release | : 2012 |
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Download The Leverage Effect in Stochastic Volatility Book in PDF, ePub and Kindle
A striking empirical feature of many financial time series is that when the price drops, the future volatility increases. This negative correlation between the financial return and future volatility processes was initially addressed in Black 76 and explained based on financial leverage, or a firm's debt-to-equity ratio: when the price drops, financial leverage increases, the firm becomes riskier, and hence, the future expected volatility increases. The phenomenon is, therefore, traditionally been named the leverage effect. In a discrete time Stochastic Volatility (SV) model framework, the leverage effect is often modelled by a negative correlation between the innovation processes of return and volatility equations. These models can be represented as state space models in which the returns and the volatilities are considered as the observed and the latent state variables respectively. Including the leverage effect in the SV model not only results in a better fit ...
Author | : Dinghai Xu |
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Total Pages | : 26 |
Release | : 2010 |
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Download Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model Book in PDF, ePub and Kindle
Author | : Ole Eiler Barndorff-Nielsen |
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Total Pages | : 18 |
Release | : 1998 |
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Download Incorporation of a Leverage Effect in a Stochastic Volatility Model Book in PDF, ePub and Kindle
Author | : Jun Yu |
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Total Pages | : 18 |
Release | : 2004 |
Genre | : Bayesian statistical decision theory |
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Download On Leverage in a Stochastic Volatility Model Book in PDF, ePub and Kindle
This paper is concerned with specification for modelling finanical leverage effect in the context of stochastic volatility models.
Author | : Bent Jesper Christensen |
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Total Pages | : |
Release | : 2010 |
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Download The Risk-return Tradeoff and Leverage Effect in a Stochastic Volatility-in-mean Model Book in PDF, ePub and Kindle
Author | : Agnès Grimaud |
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Total Pages | : 16 |
Release | : 2006 |
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Download Estimation for Mean-reverting Stochastic Volatility Models with a Leverage Effect Book in PDF, ePub and Kindle
Author | : Philippe J. Deschamps |
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Total Pages | : 41 |
Release | : 2016 |
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Download Alternative Formulations of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors Book in PDF, ePub and Kindle
This paper investigates three formulations of the leverage effect in a stochastic volatility model with a skewed and heavy-tailed observation distribution. The first formulation is the conventional one, where the observation and evolution errors are correlated. The second is a hierarchical one, where log-volatility depends on the past log-return multiplied by a time-varying latent coefficient. In the third formulation, this coefficient is replaced by a constant. The three models are compared with each other and with a GARCH formulation, using Bayes factors. MCMC estimation relies on a parametric proposal density estimated from the output of a particle smoother. The results, obtained with recent S&P500 and Swiss Market Index data, suggest that the last two leverage formulations strongly dominate the conventional one. The performance of the MCMC method is consistent across models and sample sizes, and its implementation only requires a very modest (and constant) number of filter and smoother particles.