Author: Carlos A. Abanto-VallePublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets
Author: Carlos A. Abanto-ValleStochastic Volatility in Mean
Author: Carlos A. Abanto-ValleModeling Stochastic Volatility with Application to Stock Returns
Author: Mr.Noureddine KrichenePublisher: International Monetary Fund
ISBN: 1451854846
Category : Business & Economics
Languages : en
Pages : 30
Book Description
A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.
Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing
Author: Achal AwasthiPublisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 150
Book Description
In this thesis, we propose a generalized Heston model as a tool to estimate volatility. We have used Approximate Bayesian Computing to estimate the parameters of the generalized Heston model. This model was used to examine the daily closing prices of the Shanghai Stock Exchange and the NIKKEI 225 indices. We found that this model was a good fit for shorter time periods around financial crisis. For longer time periods, this model failed to capture the volatility in detail.
An Empirical Application of Stochastic Volatility Models to Latin-American Stock Returns Using GH Skew Student's T-distribution
Author: Long Memory Stochastic Volatility Models of Latin American Stock Markets
Author: Alejandro Islas CamargoPublisher:
ISBN:
Category : Stock price indexes
Languages : en
Pages : 56
Book Description
Parameter Estimation in Stochastic Volatility Models and Hidden Markov Chains
Author: Julia TungBayesian Analysis of a Threshold Stochastic Volatility Model
Author: Tony S. WirjantoPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution in which the mean innovation switches between two regimes. In our model, the threshold is treated as an unknown parameter. We show that the proposed threshold SV model not only can capture the time-varying volatility of returns, but also can accommodate the asymmetric shape of conditional distribution of the returns. Parameter estimation is carried out by using Markov Chain Monte Carlo methods. For model selection and volatility forecast, an auxiliary particle filter technique is employed to approximate the filter and prediction distributions of the returns. Several experiments are conducted to assess the robustness of the proposed model and estimation methods. In the empirical study, we apply our threshold SV model to three return time series. The empirical analysis results show that the threshold parameter has a nonzero value and the mean innovations belong to two separately distinct regimes. We also find that the model with an unknown threshold parameter value consistently outperforms the model with a known threshold parameter value.
Empirical modelling of latin american stock markets returns and volatility using Markov - Switching garch models
Author: Miguel Ataurima ArellanoPublisher:
ISBN:
Category :
Languages : es
Pages :
Book Description
Bayesian Analysis of Moving Average Stochastic Volatility Models
Author: Stefanos DimitrakopoulosPublisher:
ISBN:
Category :
Languages : en
Pages : 28
Book Description
We propose a moving average stochastic volatility in mean model and a moving average stochastic volatility model with leverage. For parameter estimation, we develop efficient Markov chain Monte Carlo algorithms and illustrate our methods, using simulated data and a real data set. We compare the proposed specifications against several competing stochastic volatility models, using marginal likelihoods and the observed-data Deviance information criterion. We find that the moving average stochastic volatility model with leverage has better fit to our daily return series than various standard benchmarks.