Author: Adrian R. Pagan Publisher: ISBN: Category : Rate of return Languages : en Pages : 92
Book Description
This paper compares several statistical models for monthly stock return volatility. The focus is on U.S. data from 1834-19:5 because the post-1926 data have been analyzed in more detail by others. Also, the Great Depression had levels of stock volatility that are inconsistent with stationary models for conditional heteroskedasticity, We show the importance of nonlinearities in stock return behavior that are not captured by conventional ARCH or GARCH models. We also show the nonstationariry of stock volatility, even over the 1834-1925 period.
Author: Adrian Pagan Publisher: ISBN: Category : Languages : en Pages : 40
Book Description
This paper compares several statistical models for monthly stock return volatility. The focus is on U.S. data from 1834-19:5 because the post-1926 data have been analyzed in more detail by others. Also, the Great Depression had levels of stock volatility that are inconsistent with stationary models for conditional heteroskedasticity, We show the importance of nonlinearities in stock return behavior that are not captured by conventional ARCH or GARCH models. We also show the nonstationariry of stock volatility, even over the 1834-1925 period.
Author: Per Frennberg Publisher: ISBN: Category : Languages : en Pages :
Book Description
We examine the forecasting power of three recently proposed models for conditional stock volatility - an ARCH(9), a GARCH(1,2) and an AR(12)-model - with and without a seasonal component on a sample of monthly Swedish stock returns for the period 1977-1990. Our main results are the following: 1) the seasonal component adds forecasting power to all models, 2) the AR-model performs significantly better than both the ARCH- and the GARCH-model and 3) the AR-model performs at least as well as two benchmark forecasts - the implied volatility from stock index options and lagged actual volatility - despite the fact that these benchmark forecasts use a larger information set.
Author: Luc Bauwens Publisher: John Wiley & Sons ISBN: 1118272056 Category : Business & Economics Languages : en Pages : 566
Book Description
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.
Author: Turgut Kisinbay Publisher: International Monetary Fund ISBN: 1451855303 Category : Business & Economics Languages : en Pages : 40
Book Description
Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.
Author: Adrian R. Pagan
Author: A. R. Pagan
Author: Adrian Pagan
Author: 
Author: R. Glen Donaldson
Author: Anya Khanthavit
Author: Per Frennberg
Author: Luc Bauwens
Author: Dongcheol Kim
Author: Turgut Kisinbay