Volatility Prediction
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Forecasting Volatility In The Financial Markets
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Author : Stephen Satchell
language : en
Publisher: Elsevier
Release Date : 2011-02-24
Forecasting Volatility In The Financial Markets written by Stephen Satchell and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-24 with Business & Economics categories.
This new edition of Forecasting Volatility in the Financial Markets 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 modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition: * What good is a volatility model? Engle and Patton * Applications for portfolio variety Dan diBartolomeo * A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish * Volatility modeling and forecasting in finance Xiao and Aydemir * An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey * Leading thinkers present newest research on volatility forecasting *International authors cover a broad array of subjects related to volatility forecasting *Assumes basic knowledge of volatility, financial mathematics, and modelling
A Practical Guide To Forecasting Financial Market Volatility
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Author : Ser-Huang Poon
language : en
Publisher: John Wiley & Sons
Release Date : 2005-08-19
A Practical Guide To Forecasting Financial Market Volatility written by Ser-Huang Poon 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 2005-08-19 with Business & Economics categories.
Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.
Forecasting Volatility In The Financial Markets
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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.
Asset Price Dynamics Volatility And Prediction
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Author : Stephen J. Taylor
language : en
Publisher: Princeton University Press
Release Date : 2011-02-11
Asset Price Dynamics Volatility And Prediction written by Stephen J. Taylor and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-11 with Business & Economics categories.
This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions. Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions. Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.
Modelling And Forecasting Stock Return Volatility And The Term Structure Of Interest Rates
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Author : Michiel de Pooter
language : en
Publisher: Rozenberg Publishers
Release Date : 2007
Modelling And Forecasting Stock Return Volatility And The Term Structure Of Interest Rates written by Michiel de Pooter and has been published by Rozenberg Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.
Improved Volatility Prediction And Trading Using Stocktwits Sentiment Data
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Author : Shradha Berry
language : en
Publisher:
Release Date : 2020
Improved Volatility Prediction And Trading Using Stocktwits Sentiment Data written by Shradha Berry and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Volatility prediction plays an important role in the financial domain. The GARCH family of prediction models is very popular and efficient in using past returns to forecast volatility. It has also been observed that news, scheduled and unscheduled, have an impact on return volatility of assets. An enhanced GARCH model, called News Augmented GARCH (NAGARCH) includes an additional component for news sentiment. With the rise in popularity of the world wide web and social media, it has become a rich source for opinions and sentiments. Twitter is one such platform. It is a micro-blogging site and a popular source for public view on different topics. StockTwits is a social media platform that started as an application built using Twitter's API. It has since grown into an independent financial social media platform for news and sentiment. StockTwits is a rich source of opinions from subject experts and analysts. This data provides first systematic exploration of social media. It reflects raw sentiments of traders, investors, media, public companies, and investment professionals as opposed to sentiments from curated news wires. This research attempts to determine if the sentiment on stocks from StockTwits micro-blogs can improve volatility prediction. The experiment is performed on 9 NASDAQ100 stocks. The GARCH model with stock returns, and the NA-GARCH model with stock returns and micro-blog sentiment are tuned and their prediction results are evaluated. NA-GARCH, with the sentiment data from StockTwits performed better than the GARCH model in 7 out of the 9 cases.
The Empirical Similarity Approach For Volatility Prediction
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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.
Volatility Prediction
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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.
Forecasting Volatility
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Author : Stephen Figlewski
language : en
Publisher:
Release Date : 1997
Forecasting Volatility written by Stephen Figlewski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Stock exchanges categories.
Comparison Of Volatility Predi
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Author : Ka-Chung Law
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-27
Comparison Of Volatility Predi written by Ka-Chung Law and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with Business & Economics categories.
This dissertation, "A Comparison of Volatility Predictions in the HK Stock Market" by Ka-chung, Law, 羅家聰, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: ABSTRACT With the introduction of new financial instruments in recent years especially a variety of derivative securities, financial markets have become more complex and, to certain degree, more volatile. Volatility prediction has thus become more important for both practitioners and academics. Using only historical data, this paper examines a number of existing volatility predicting models. Among them, the Random Walk model, the GARCH model, the EGARCH model and the Stochastic Volatility model are examined with certain modifications. In addition, Hang Seng Index Option prices are used as an instrument for analysis. DOI: 10.5353/th_b3016353 Subjects: Stock price forecasting - China - Hong Kong Stock price forecasting - Mathematical models