Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Approximate Bayesian Estimation Of Stochastic Volatility In Mean Models Using Hidden Markov Models Empirical Evidence From Stock Latin American Markets
Download Approximate Bayesian Estimation Of Stochastic Volatility In Mean Models Using Hidden Markov Models Empirical Evidence From Stock Latin American Markets full books in PDF, epub, and Kindle. Read online Approximate Bayesian Estimation Of Stochastic Volatility In Mean Models Using Hidden Markov Models Empirical Evidence From Stock Latin American Markets ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets by : Carlos A. Abanto-Valle
Download or read book Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets written by Carlos A. Abanto-Valle and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Volatility in Mean by : Carlos A. Abanto-Valle
Download or read book Stochastic Volatility in Mean written by Carlos A. Abanto-Valle and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modeling Stochastic Volatility with Application to Stock Returns by : Mr.Noureddine Krichene
Download or read book Modeling Stochastic Volatility with Application to Stock Returns written by Mr.Noureddine Krichene and published by International Monetary Fund. This book was released on 2003-06-01 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing by : Achal Awasthi
Download or read book Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing written by Achal Awasthi and published by . This book was released on 2018 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis An Empirical Application of Stochastic Volatility Models to Latin-American Stock Returns Using GH Skew Student's T-distribution by :
Download or read book An Empirical Application of Stochastic Volatility Models to Latin-American Stock Returns Using GH Skew Student's T-distribution written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Long Memory Stochastic Volatility Models of Latin American Stock Markets by : Alejandro Islas Camargo
Download or read book Long Memory Stochastic Volatility Models of Latin American Stock Markets written by Alejandro Islas Camargo and published by . This book was released on 2000 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation in Stochastic Volatility Models and Hidden Markov Chains by : Julia Tung
Download or read book Parameter Estimation in Stochastic Volatility Models and Hidden Markov Chains written by Julia Tung and published by . This book was released on 2000 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Analysis of a Threshold Stochastic Volatility Model by : Tony S. Wirjanto
Download or read book Bayesian Analysis of a Threshold Stochastic Volatility Model written by Tony S. Wirjanto and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Empirical modelling of latin american stock markets returns and volatility using Markov - Switching garch models by : Miguel Ataurima Arellano
Download or read book Empirical modelling of latin american stock markets returns and volatility using Markov - Switching garch models written by Miguel Ataurima Arellano and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Analysis of Moving Average Stochastic Volatility Models by : Stefanos Dimitrakopoulos
Download or read book Bayesian Analysis of Moving Average Stochastic Volatility Models written by Stefanos Dimitrakopoulos and published by . This book was released on 2017 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Volatility Estimation and Price Prediction Using a Hidden Markov Model with Empirical Study by : Pei Yin
Download or read book Volatility Estimation and Price Prediction Using a Hidden Markov Model with Empirical Study written by Pei Yin and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides a solid development of a hidden Markov model (HMM) from the economic insight to the mathematic formulation. In this model, we assume both drift and volatility of the security return process are driven by certain underlying economic forces which evolve together as a finite-state, time-invariant Markov chain. Unfortunately, this chain is unobservable. Through stochastic filtering techniques and EM algorithm with modified iteration steps, we estimate the state space and transition matrix of the Markov chain, as well as the state spaces of the drift and volatility. With these estimates we can smooth and predict the drift and volatility processes and apply them to the security price prediction. On an empirical level, we first use Monte Carlo simulation to show the robustness of our estimates, and then implement HMM on various data sets of historical prices including: major indices, bonds, mutual funds, common stocks, and ETFs to back test the predicability of the model. Moreover, we compare the applicability of HMM with the well established GARCH(1,1) model, as far as the prediction performance is concerned, our results indicate HMM outperforms GARCH(1,1).
Download or read book Haḥlāṭōt Haw-Wa'ad hap-pō'ēl haṣ-ṣijjōnī, še-nitqabbelū be-mōšebō b-Irūšālajim baj-jāmīm 27 be-Sīwān, 4-8 be-Tammūz [5]731 (20 be-Jūnī, 27 be-Jūnī-1 be-Jūlī 1971) written by and published by . This book was released on 1971 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Volatility Models with Heavy-tailed Distributions by : Toshiaki Watanabe
Download or read book Stochastic Volatility Models with Heavy-tailed Distributions written by Toshiaki Watanabe and published by . This book was released on 2001 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Empirical Modeling of Latin American Stock and Forex Markets Returns and Volatility Using Markov-Switching Garch Models by : Miguel Ataurima Arellano
Download or read book Empirical Modeling of Latin American Stock and Forex Markets Returns and Volatility Using Markov-Switching Garch Models written by Miguel Ataurima Arellano and published by . This book was released on 2017 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis EGARCH and Stochastic Volatility by : Jouchi Nakajima
Download or read book EGARCH and Stochastic Volatility written by Jouchi Nakajima and published by . This book was released on 2008 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This paper proposes the EGARCH [Exponential Generalized Autoregressive Conditional Heteroskedasticity] model with jumps and heavy-tailed errors, and studies the empirical performance of different models including the stochastic volatility models with leverage, jumps and heavy-tailed errors for daily stock returns. In the framework of a Bayesian inference, the Markov chain Monte Carlo estimation methods for these models are illustrated with a simulation study. The model comparison based on the marginal likelihood estimation is provided with data on the U.S. stock index."--Author's abstract.
Book Synopsis A Stochastic Volatility Model with GH Skew Student's T-distribution by :
Download or read book A Stochastic Volatility Model with GH Skew Student's T-distribution written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Analysis of a Stochastic Volatility Model with Leverage Effect and Fat Tails by : Eric Jacquier
Download or read book Bayesian Analysis of a Stochastic Volatility Model with Leverage Effect and Fat Tails written by Eric Jacquier and published by . This book was released on 2001 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: The basic univariate stochastic volatility model specifies that conditional volatility follows a log-normal auto-regressive model with innovations assumed to be independent of the innovations in the conditional mean equation. Since the introduction of practical methods for inference in the basic volatility model (JPR-(1994)), it has been observed that the basic model is too restrictive for many financial series. We extend the basic SVOL to allow for a so-called quot;Leverage effectquot; via correlation between the volatility and mean innovations, and for fat-tails in the mean equation innovation. A Bayesian Markov Chain Monte Carlo algorithm is developed for the extended volatility model. Thus far, likelihood-based inference for the correlated SVOL model has not appeared in the literature. We develop Bayes Factors to assess the importance of the leverage and fat-tail extensions. Sampling experiments reveal little loss in precision from adding the model extensions but a large loss from using the basic model in the presence of mis-specification. For both equity and exchange rate data, there is overwhelming evidence in favor of models with fat-tailed volatility innovations, and for a leverage effect in the case of equity indices. We also find that volatility estimates from the extended model are markedly different from those produced by the basic SVOL.
![Download Haḥlāṭōt Haw-Wa'ad hap-pō'ēl haṣ-ṣijjōnī, še-nitqabbelū be-mōšebō b-Irūšālajim baj-jāmīm 27 be-Sīwān, 4-8 be-Tammūz [5]731 (20 be-Jūnī, 27 be-Jūnī-1 be-Jūlī 1971) PDF Online Free](https://books.google.com/books/content/images/frontcover/eFHQjwEACAAJ?fife=w200-h370)
