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A Study About the Existence of the Leverage Effect in Stochastic Volatility Models

A Study About the Existence of the Leverage Effect in Stochastic Volatility Models
Author: Ionut Florescu
Publisher:
Total Pages: 25
Release: 2018
Genre:
ISBN:

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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.


Research Report

Research Report
Author:
Publisher:
Total Pages:
Release: 1998
Genre:
ISBN:

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The Leverage Effect in Stochastic Volatility

The Leverage Effect in Stochastic Volatility
Author: Amaan Mehrabian
Publisher:
Total Pages:
Release: 2012
Genre:
ISBN:

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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 ...


On Leverage in a Stochastic Volatility Model

On Leverage in a Stochastic Volatility Model
Author: Jun Yu
Publisher:
Total Pages: 18
Release: 2004
Genre: Bayesian statistical decision theory
ISBN:

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This paper is concerned with specification for modelling finanical leverage effect in the context of stochastic volatility models.


Alternative Formulations of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors

Alternative Formulations of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors
Author: Philippe J. Deschamps
Publisher:
Total Pages: 41
Release: 2016
Genre:
ISBN:

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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.