A Symbolic Dynamics Approach To Volatility Prediction
DOWNLOAD
Download A Symbolic Dynamics Approach To Volatility Prediction PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get A Symbolic Dynamics Approach To Volatility Prediction 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
A Symbolic Dynamics Approach To Volatility Prediction
DOWNLOAD
Author : Peter Tino
language : en
Publisher:
Release Date : 1998
A Symbolic Dynamics Approach To Volatility Prediction written by Peter Tino 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.
Forecasting Volatility In The Financial Markets
DOWNLOAD
Author : John L. Knight
language : en
Publisher: Butterworth-Heinemann
Release Date : 2002
Forecasting Volatility In The Financial Markets written by John L. Knight and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Business & Economics categories.
This text assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modeling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.
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.
Multifractal Volatility
DOWNLOAD
Author : Laurent E. Calvet
language : en
Publisher: Academic Press
Release Date : 2008-10-13
Multifractal Volatility written by Laurent E. Calvet and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-13 with Business & Economics categories.
Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and mathematics and provides a unified treatment of the use of multifractal techniques in finance. A large existing literature (e.g., Engle, 1982; Rossi, 1995) models volatility as an average of past shocks, possibly with a noise component. This approach often has difficulty capturing sharp discontinuities and large changes in financial volatility. Their research has shown the advantages of modelling volatility as subject to abrupt regime changes of heterogeneous durations. Using the intuition that some economic phenomena are long-lasting while others are more transient, they permit regimes to have varying degrees of persistence. By drawing on insights from the use of multifractals in the natural sciences and mathematics, they show how to construct high-dimensional regime-switching models that are easy to estimate, and substantially outperform some of the best traditional forecasting models such as GARCH. The goal of Multifractal Volatility is to popularize the approach by presenting these exciting new developments to a wider audience. They emphasize both theoretical and empirical applications, beginning with a style that is easily accessible and intuitive in early chapters, and extending to the most rigorous continuous-time and equilibrium pricing formulations in final chapters. Presents a powerful new technique for forecasting volatility Leads the reader intuitively from existing volatility techniques to the frontier of research in this field by top scholars at major universities The first comprehensive book on multifractal techniques in finance, a cutting-edge field of research
A Machine Learning Approach To Volatility Forecasting
DOWNLOAD
Author : Kim Christensen
language : en
Publisher:
Release Date : 2021
A Machine Learning Approach To Volatility Forecasting written by Kim Christensen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
The Empirical Similarity Approach For Volatility Prediction
DOWNLOAD
Author : Vasyl Golosnoy
language : en
Publisher:
Release Date : 2014
The Empirical Similarity Approach For Volatility Prediction written by Vasyl Golosnoy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
In this paper we adapt the empirical similarity (ES) concept for the purpose of combining forecasts originating from different models. Our ES approach is suitable for situations where a decision maker refrains from evaluating success probabilities of forecasting models but prefers to think by analogy. It allows to determine weights of the forecasting combination by quantifying distances between model predictions and corresponding realizations of the process of interest as they are perceived by decision makers. The proposed ES approach is applied for combining models in order to forecast daily volatility of the major stock market indices.
Computational Finance 1999
DOWNLOAD
Author : Yaser S. Abu-Mostafa
language : en
Publisher: MIT Press
Release Date : 2000
Computational Finance 1999 written by Yaser S. Abu-Mostafa and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Business & Economics categories.
This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and hedging, forecasting, and option pricing. The book is based on the sixth annual international conference Computational Finance 1999, held at New York University's Stern School of Business.
Enhancing Volatility Forecasting
DOWNLOAD
Author : Yi Liu
language : en
Publisher:
Release Date : 2023
Enhancing Volatility Forecasting written by Yi Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
The paper aims to enhance the accuracy of realized volatility prediction by introducing a novel Dual Empirical Mode Decomposition (DEMD) method that allows for the extraction of incremental information related to volatility prediction in raw financial data. The empirical results show that using the DEMD method to decompose and reconstruct trading volume leads to a demonstration of superior in-sample explanatory power. Furthermore, in terms of out-of-sample volatility forecasting, the DEMD method exhibits significant advantages over the direct addition of trading volume and the use of similar methods employed in popular volatility and linear regularization models.
Modelling Volatility In Financial Markets
DOWNLOAD
Author : Chun Liu
language : en
Publisher:
Release Date : 2007
Modelling Volatility In Financial Markets written by Chun Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
In this thesis, I study the dynamics of the volatility process and focus on estimation and forecasting. Recent research uses high frequency intraday data to construct ex post measures of daily volatility including realized volatility (RV). Chapter 1 is the introduction. In Chapter 2, I use a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. I focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations I demonstrate that the estimation approach is effective in identifying and dating structural breaks. Applied to daily S & P 500 data, I find strong evidence of a single structural break in log(RV). The main effect of the break is on the long-run mean and variance of log-volatility. Chapter 3 uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include HAR specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and leverage term. The Bayesian model averaging provides very competitive density forecasts and consistent but modest improvements in point forecasts over the benchmarks. Applied to equity and exchange rate volatility over several forecast horizons, the Bayesian model averaging provides the best performance compared to the benchmarks including HAR, AR and simple model averaging models. I discuss the reasons for this, including the importance of using realized power variation as a predictor. In the last chapter, I propose a new joint model of volatility and duration in high frequency framework using tick-by-tick data. This model decomposes the conditional variance into different volatility components associated with different transaction horizons. Using stock market data, I demonstrate its superiority over the traditional GARCH counterpart. In addition, I show that a fat-tailed t-distribution for return innovations and a Burr distribution for duration innovations improve density forecasts, compared with normal and exponential distribution, respectively.
Alternative Approach To Volatility Forecasting And Evaluating Forecasting Performance
DOWNLOAD
Author : Hyungjin Lim
language : en
Publisher:
Release Date : 2012
Alternative Approach To Volatility Forecasting And Evaluating Forecasting Performance written by Hyungjin Lim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.