Enhancing Volatility Forecasting
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Enhancing Volatility Forecasting
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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.
Forecasting Volatility In The Financial Markets
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Author : Stephen Satchell
language : en
Publisher: Elsevier
Release Date : 2002-08-22
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 2002-08-22 with Business & Economics categories.
'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 then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.The editors have brought together a set of contributors that give the reader a firm grounding in relevant theory and research and an insight into the cutting edge techniques applied in this field of the financial markets.This book is of particular relevance to anyone who wants to understand dynamic areas of the financial markets.* Traders will profit by learning to arbitrage opportunities and modify their strategies to account for volatility.* Investment managers will be able to enhance their asset allocation strategies with an improved understanding of likely risks and returns.* Risk managers will understand how to improve their measurement systems and forecasts, enhancing their risk management models and controls.* Derivative specialists will gain an in-depth understanding of volatility that they can use to improve their pricing models.* Students and academics will find the collection of papers an invaluable overview of this field. This book is of particular relevance to those wanting to understand the dynamic areas of volatility modeling and forecasting of the financial marketsProvides the latest research and techniques for Traders, Investment Managers, Risk Managers and Derivative Specialists wishing to manage their downside risk exposure Current research on the key forecasting methods to use in risk management, including two new chapters
Deep Learning Approaches In Finance
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Author : Marcelo Sardelich Nascimento
language : en
Publisher:
Release Date : 2019
Deep Learning Approaches In Finance written by Marcelo Sardelich Nascimento and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Volatility Forecasts And The Enhancement Of Risk Return Profiles Through Automated Trading Strategies
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Author : Engelbert J. Dockner
language : de
Publisher:
Release Date : 1999
Volatility Forecasts And The Enhancement Of Risk Return Profiles Through Automated Trading Strategies written by Engelbert J. Dockner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
Volatility Forecast Using Garch News Sentiment And Implied Volatility
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Author : Jamie Atkinson
language : en
Publisher:
Release Date : 2019
Volatility Forecast Using Garch News Sentiment And Implied Volatility written by Jamie Atkinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Due to its significance, forecasting asset volatility has been an active area of research in recent decades. In this whitepaper we aim to take into account the stylised facts of volatility to improve predictive power of a simple GARCH model. We investigate the power of three GARCH models (GARCH, EGARCH, GJR- GARCH) using implied volatility and news sentiment data as external regressors in order to enhance forecasts of stock return volatility. We also explore the impact of the use of fat-tailed and skewed distributions. Analysis is conducted on 5 constituents of the S&P500. In terms of in-sample performance, the findings suggest that a GJR-GARCH(1,1) model incorporating a student-t distribution, implied volatility and news sentiment data consistently out-performs a simple GARCH(1,1) with a normal distribution. When comparing out-of-sample forecast performance, the enhanced models were able to improve volatility predictions for four out of five stocks.
Essays On Volatility Forecasting
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Author : Dimos S. Kambouroudis
language : en
Publisher:
Release Date : 2012
Essays On Volatility Forecasting written by Dimos S. Kambouroudis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Accounting and price fluctuations categories.
Stock market volatility has been an important subject in the finance literature for which now an enormous body of research exists. Volatility modelling and forecasting have been in the epicentre of this line of research and although more than a few models have been proposed and key parameters on improving volatility forecasts have been considered, finance research has still to reach a consensus on this topic. This thesis enters the ongoing debate by carrying out empirical investigations by comparing models from the current pool of models as well as exploring and proposing the use of further key parameters in improving the accuracy of volatility modelling and forecasting. The importance of accurately forecasting volatility is paramount for the functioning of the economy and everyone involved in finance activities. For governments, the banking system, institutional and individual investors, researchers and academics, knowledge, understanding and the ability to forecast and proxy volatility accurately is a determining factor for making sound economic decisions. Four are the main contributions of this thesis. First, the findings of a volatility forecasting model comparison reveal that the GARCH genre of models are superior compared to the more 'simple' models and models preferred by practitioners. Second, with the use of backward recursion forecasts we identify the appropriate in-sample length for producing accurate volatility forecasts, a parameter considered for the first time in the finance literature. Third, further model comparisons are conducted within a Value-at-Risk setting between the RiskMetrics model preferred by practitioners, and the more complex GARCH type models, arriving to the conclusion that GARCH type models are dominant. Finally, two further parameters, the Volatility Index (VIX) and Trading Volume, are considered and their contribution is assessed in the modelling and forecasting process of a selection of GARCH type models. We discover that although accuracy is improved upon, GARCH type forecasts are still superior.
Forecasting The Volatility Of Stock Market And Oil Futures Market
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Author : Dexiang Mei
language : en
Publisher: Scientific Research Publishing, Inc. USA
Release Date : 2020-12-17
Forecasting The Volatility Of Stock Market And Oil Futures Market written by Dexiang Mei and has been published by Scientific Research Publishing, Inc. USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-17 with Business & Economics categories.
The volatility has been one of the cores of the financial theory research, in addition to the stock markets and the futures market are an important part of modern financial markets. Forecast volatility of the stock market and oil futures market is an important part of the theory of financial markets research.
Volatility Forecasts Trading Volume And The Arch Versus Option Implied Volatility Trade Off
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Author : Glen Donaldson
language : en
Publisher:
Release Date : 2014
Volatility Forecasts Trading Volume And The Arch Versus Option Implied Volatility Trade Off written by Glen Donaldson 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.
We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t-1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t-1 relative to the recent past, option-implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option-implied forward-looking estimate.
Volatility Forecasting In Futures Markets
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Author : Theo Athanasiadis
language : en
Publisher:
Release Date : 2015
Volatility Forecasting In Futures Markets written by Theo Athanasiadis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Volatility forecasting has paramount importance in position sizing and risk management of CTAs. In this paper we examine the out-of-sample forecasts of widely used volatility estimators for the S&P 500 and the 10-Year US Note from a statistical and Value-at-Risk perspective. Although we do not find evidence for a volatility estimator that is statistically superior, we show that the volatility process of each asset is different with asymmetric GARCH models generating superior forecasts for the S&P 500, whereas symmetric GARCH, the Yang-Zhang estimator along with the implied volatility forecasting better the 10-Year US Note volatility. We also show that the volatility of the 10-Year US Note is more forecastable than that of the S&P 500 producing smaller errors. More importantly, we find that improving the volatility forecast can generate superior VaR estimates that can be accurate under the normal distribution failing only at the lowest quantiles mainly because the distribution is mispecified and badly approximated by the normal. Semi-parametric QML-GARCH models that use the empirical quantiles of the distribution along with GARCH forecasts address that issue and generate superior VaR estimates outperforming all other methods.
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.