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Author: Jun Yu
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 18
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Book Description
This paper is concerned with specification for modelling finanical leverage effect in the context of stochastic volatility models.
Author: Jun Yu
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 18
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Book Description
This paper is concerned with specification for modelling finanical leverage effect in the context of stochastic volatility models.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Hong Jiang
Publisher:
ISBN:
Category : Asset-liability management
Languages : en
Pages : 125
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Author: Ole Eiler Barndorff-Nielsen
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ISBN:
Category :
Languages : en
Pages : 18
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Book Description
Author: Ionut Florescu
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ISBN:
Category :
Languages : en
Pages : 25
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Book Description
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.
Author: Josep Perelló
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ISBN:
Category :
Languages : en
Pages : 19
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Book Description
Financial time series exhibit two different type of non linear correlations: (i) volatility autocorrelations that have a very long range memory, on the order of years, and (ii) asymmetric return-volatility (or 'leverage') correlations that are much shorter ranged. Different stochastic volatility models have been proposed in the past to account for both these correlations. However, in these models, the decay of the correlations is exponential, with a single time scale for both the volatility and the leverage correlations, at variance with observations. We extend the linear Ornstein-Uhlenbeck stochastic volatility model by assuming that the mean reverting level is itself random. We find that the resulting three-dimensional diffusion process can account for different correlation time scales. We show that the results are in good agreement with a century of the Dow Jones index daily returns (1900-2000), with the exception of crash days.
Author: Hoang Nguyen
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Category :
Languages : en
Pages :
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Author: Philippe J. Deschamps
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ISBN:
Category :
Languages : en
Pages : 41
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Book Description
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.
Author: Dinghai Xu
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ISBN:
Category :
Languages : en
Pages : 26
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Author: Daniel R. Smith
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Category :
Languages : en
Pages : 24
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Book Description
We develop a new stochastic volatility model that captures the three most important features of stock index returns: negative correlation between returns and future volatility, excess kurtosis and negative skewness. We estimate the model parameters by maximum likelihood using a numerical integration-based filter to deal with the latent nature of volatility. In this approach different models are defined by varying the joint density of returns and future volatility conditional on current volatility. Our innovation is to construct the joint conditional density using a copula. This approach is tremendously flexible and allows the econometrician to choose the marginal distribution of both returns and volatility independently and then stitch them together using a copula, which is also chosen independently, to form the joint density. We also develop conditional moment-based model specification tests for the extent to which the various stochastic volatility models are able to capture the skewness and excess kurtosis we observe in practice. The parameter estimates and conditional moment tests indicate that leverage effects, excess kurtosis and skewness are all crucial for modeling stock returns.
