Linear Filtering for Asymmetric Stochastic Volatility Models PDF Download
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Author: Chris Kirby
Publisher:
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Languages : en
Pages :
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Book Description
Linear filtering techniques are used to develop a quasi maximum likelihood estimator for asymmetric stochastic volatility models. The estimator is straightforward to implement and performs well in Monte Carlo experiments.
Author: Chris Kirby
Publisher:
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Category :
Languages : en
Pages :
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Book Description
Linear filtering techniques are used to develop a quasi maximum likelihood estimator for asymmetric stochastic volatility models. The estimator is straightforward to implement and performs well in Monte Carlo experiments.
Author: Adam Clements
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 20
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Author: Francisco Blasques
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Category :
Languages : en
Pages : 0
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Book Description
This paper considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation-based methods to address key challenges in parameter estimation, the filtering of time-varying volatility, and volatility forecasting. Specifically, we make use of the indirect inference method to estimate the static parameters, and the extremum Monte Carlo method to extract latent volatility. Both methods can be easily adapted to modifications of the model, such as having other distributions for the errors and other dynamic specifications for the volatility process. Illustrations are presented for a simulated dataset and for an empirical application to a time series of Bitcoin returns.
Author: Xiuping Mao
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
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Book Description
In this paper, we derive the statistical properties of a general family of Stochastic Volatility (SV) models with leverage effect which capture the dynamic evolution of asymmetric volatility in financial returns. We provide analytical expressions of moments and autocorrelations of power-transformed absolute returns. Moreover, we use an Approximate Bayesian Computation (ABC) filter-based Maximum Likelihood (ML) method to estimate the parameters of the SV models. In Monte Carlo simulations we show that the ABC filter-based ML accurately estimates the parameters of a very general specification of the log-volatility with standardized returns following the Generalized Error Distribution (GED). The results are illustrated by analyzing series of daily S&P 500 and MSCI World returns.
Author: Kai Ming Lee
Publisher: Rozenberg Publishers
ISBN: 9036101697
Category :
Languages : en
Pages : 150
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Author: Adam Clements
Publisher:
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Category : Economics
Languages : en
Pages : 22
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Author: Hyoung Il Lee
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Category :
Languages : en
Pages :
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We consider the stochastic volatility model with smooth transition and persistent latent factors. We argue that this model has advantages over the conventional stochastic model for the persistent volatility factor. Though the linear filtering is widely used in the state space model, the simulation result, as well as theory, shows that it does not work in our model. So we apply the density-based filtering method; in particular, we develop two methods to get solutions. One is the conventional approach using the Maximum Likelihood estimation and the other is the Bayesian approach using Gibbs sampling. We do a simulation study to explore their characteristics, and we apply both methods to actual macroeconomic data to extract the volatility generating process and to compare macro fundamentals with them. Next we extend our model into multivariate model extracting common and id- iosyncratic volatility for multivariate processes. We think it is interesting to apply this multivariate model into measuring time-varying uncertainty of macroeconomic variables and studying the links to market returns via a consumption-based asset pric- ing model. Motivated by Bansal and Yaron (2004), we extract a common volatility factor using consumption and dividend growth, and we find that this factor predicts post-war business cycle recessions quite well. Then, we estimate a long-run risk model of asset prices incorporating this macroeconomic uncertainty. We find that both risk aversion and the intertemporal elasticity of substitution are estimated to be around two, and our simulation results show that the model can match the first and second moments of market return and risk-free rate, hence the equity premium.
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Category :
Languages : da
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Author: Manabu Asai
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Category : Foreign exchange rates
Languages : en
Pages : 25
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Author: Daniel R. Smith
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
We compare the ability of correlation and threshold effects in a stochastic volatility model to capture the asymmetric relationship between stock returns and volatility. The parameters are estimated using Maximum Likelihood based on the extended Kalman filter and uses numerical integration over the latent volatility process. The stochastic volatility model with only correlation does a better job of capturing asymmetry than a threshold stochastic volatility model even though it has fewer parameters. We develop a stochastic volatility model that includes both threshold effects and correlated innovations. We find that the general model with both threshold effects and correlated innovations dominates purely threshold and correlated models. In this augmented model volatility and returns are negatively correlated, and volatility is more persistent, less volatile and higher following negative returns even after accounting for the negative correlation.
