Egarch And Stochastic Volatility
DOWNLOAD
Download Egarch And Stochastic Volatility PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Egarch And Stochastic Volatility book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Volatility Prediction
DOWNLOAD
Author : Harry M. Kat
language : en
Publisher:
Release Date : 2003
Volatility Prediction written by Harry M. Kat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
Future volatility is a key input for pricing and hedging derivatives and for quantitative investment strategies in general. There are many different approaches. This article investigates whether random walk, GARCH (1,1), EGARCH (1,1) and stochastic volatility models of return volatility behavior differ in their ability to predict the volatility of stock index and currency returns over horizons ranging from 2 to 100 trading days. We use close-to-close return data for 7 indices and 5 currencies over the period 1980-1992. The results show that the forecast performance of the different models depends on the specific asset class in question. For stock indices the best volatility predictions are generated by the stochastic volatility model. For currencies on the other hand, the best forecasts come from the GARCH (1,1) model.
Egarch And Stochastic Volatility
DOWNLOAD
Author : Jouchi Nakajima
language : en
Publisher:
Release Date : 2008
Egarch And Stochastic Volatility written by Jouchi Nakajima and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Stochastic processes categories.
"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.
Aggregations And Marginalization Of Garch And Stochastic Volatility Models
DOWNLOAD
Author : Nour Meddahi
language : fr
Publisher:
Release Date : 1998
Aggregations And Marginalization Of Garch And Stochastic Volatility Models written by Nour Meddahi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Deciding Between Garch And Stochastic Volatility Via Strong Decision Rules
DOWNLOAD
Author : Arie Preminger
language : en
Publisher:
Release Date : 2008
Deciding Between Garch And Stochastic Volatility Via Strong Decision Rules written by Arie Preminger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
The GARCH and stochastic volatility (SV) models are two competing, well-known and often used models to explain the volatility of financial series. In this paper, we consider a closed form estimator for a stochastic volatility model and derive its asymptotic properties. We confirm our theoretical results by a simulation study. In addition, we propose a set of simple, strongly consistent decision rules to compare the ability of the GARCH and the SV model to fit the characteristic features observed in high frequency financial data such as high kurtosis and slowly decaying autocorrelation function of the squared observations. These rules are based on a number of moment conditions that is allowed to increase with sample size. We show that our selection procedure leads to choosing the best and simple model with probability one as the sample size increases. The finite sample size behaviour of our procedure is analyzed via simulations. Finally, we provide an application to stocks in the Dow Jones industrial average index.
Modeling Stock Volatility With Stochastic Arch Garch And Stochastic Volatility Model
DOWNLOAD
Author : Chang Sun (M.S. in Statistics)
language : en
Publisher:
Release Date : 2016
Modeling Stock Volatility With Stochastic Arch Garch And Stochastic Volatility Model written by Chang Sun (M.S. in Statistics) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Modeling volatility within the log stock return is key to the stock price prediction. Despite numerous researches that modeled the volatility with conditional heavy-tailed error distributions, the unconditional distribution remains unknown. In this report, we use and follow the method introduced by Pitt and Walker (2005) by assigning a Student-t distribution for the marginal density of log return and constructing three models respectively, with similar structures to Autoregressive Conditional Heteroskedasticity (ARCH), Generalized ARCH (GARCH) and Stochastic Volatility model in a Bayesian way. We demonstrate the capability of the three models for stock price prediction with S&P 500 index and show that all our models outperform the standard GARCH model (Bollerslev, 1986).
Handbook Of Volatility Models And Their Applications
DOWNLOAD
Author : Luc Bauwens
language : en
Publisher: John Wiley & Sons
Release Date : 2012-03-22
Handbook Of Volatility Models And Their Applications written by Luc Bauwens and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-22 with Business & Economics categories.
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
A Simple Test For Garch Against A Stochastic Volatility Model
DOWNLOAD
Author : Philip Hans Franses
language : en
Publisher:
Release Date : 2010
A Simple Test For Garch Against A Stochastic Volatility Model written by Philip Hans Franses and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
GARCH models and Stochastic Volatility (SV) models can both be used to describe unobserved volatility in asset returns. We consider the issue of testing a GARCH model against an SV model. For that purpose, we propose a new and parsimonious GARCH-t model with an additional restricted moving average term, which can capture SV model properties. We discuss model representation, parameter estimation, and our simple test for model selection. Furthermore, we derive the theoretical moments and the autocorrelation function of our new model. We illustrate our model and test for nine daily stock-return series.
Aggregations And Marginalization Of Garch And Stochastic Volatility Models
DOWNLOAD
Author : Meddahi, Nour
language : en
Publisher: Montréal : Université de Montréal, Centre de recherche et développement en économique
Release Date : 1997
Aggregations And Marginalization Of Garch And Stochastic Volatility Models written by Meddahi, Nour and has been published by Montréal : Université de Montréal, Centre de recherche et développement en économique this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.
Forecasting Variance Using Stochastic Volatility And Garch
DOWNLOAD
Author : Björn Hansson
language : en
Publisher:
Release Date : 1998
Forecasting Variance Using Stochastic Volatility And Garch written by Björn Hansson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
A Closer Look At The Relation Between Garch And Stochastic Autoregressive Volatility
DOWNLOAD
Author : Jeff Fleming
language : en
Publisher:
Release Date : 2010
A Closer Look At The Relation Between Garch And Stochastic Autoregressive Volatility written by Jeff Fleming and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
We show that, for three common SARV models, fitting a minimum mean square linear filter is equivalent to fitting a GARCH model. This suggests that GARCH models may be useful for filtering, forecasting, and parameter estimation in stochastic volatility settings. To investigate, we use simulations to evaluate how the three SARV models and their associated GARCH filters perform under controlled conditions and then we use daily currency and equity index returns to evaluate how the models perform in a risk management application. Although the GARCH models produce less precise forecasts than the SARV models in the simulations, it is not clear that the performance differences are large enough to be economically meaningful. Consistent with this view, we find that the GARCH and SARV models perform comparably in tests of conditional value-at-risk estimates using the actual data.