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Implied Garch Volatility Forecasting
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Book Synopsis Implied GARCH Volatility Forecasting by : Thorsten Lehnert
Download or read book Implied GARCH Volatility Forecasting written by Thorsten Lehnert and published by . This book was released on 2002 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper empirically investigates a method to quantify volatility using the information content of index options. We derive the parameters of a GARCH option pricing model from the term structure of the observed market smile of DAX 30 index. We find the EGARCH option pricing model (Duan, 1995) performs well in determining the shape of the volatility smile for different maturities in the period of January 2000 to August 2001. Based on the implied EGARCH methodology we use the information in option prices to derive a theoretically sound 'new' measure for local volatility and analyze how well it explains and forecasts actual realized volatility. The daily realized volatility measure is constructed with 5-minute interval transaction prices in the DAX 30 future. The local volatility measure explains a large part of realized volatility and performs considerably better in one day ahead volatility forecasting than conventional time-series models.
Book Synopsis Implied Volatility and Predictability of GARCH Models by : Vivek Rajvanshi
Download or read book Implied Volatility and Predictability of GARCH Models written by Vivek Rajvanshi and published by . This book was released on 2019 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: We have examined the predictive power of GARCH model to forecast return volatility for Nifty 50 index. Realized volatility, which is the sum of intraday squared returns, is used as the proxy for the true volatility. Three models of the GARCH family have been used to forecast return volatility i.e., GARCH, GJR-GARCH and EGARCH along with their implied volatility (IV) augmented counterparts i.e., GARCH IV, GJR-GARCH IV and EGARCH IV. Implied Volatility forecasting has been done using AR, MA, ARMA, ARIMA and Random Walk. But GARCH model augmented with implied volatility performs better than GARCH models without augmentation or implied volatility alone. Forecasting performance of the competing models has been judged by using mean absolute error (MAE) and root mean squared error (RMSE). MAE and RMSE show that GARCH IV model is best suited for the volatility forecasting in the context of Nifty 50 index.
Book Synopsis A Practical Guide to Forecasting Financial Market Volatility by : Ser-Huang Poon
Download or read book A Practical Guide to Forecasting Financial Market Volatility written by Ser-Huang Poon and published by John Wiley & Sons. This book was released on 2005-08-19 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Forecasting Volatility in the Financial Markets by : Stephen Satchell
Download or read book Forecasting Volatility in the Financial Markets written by Stephen Satchell and published by Elsevier. This book was released on 2011-02-24 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting Volatility in the Financial Markets, Third Edition 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
Book Synopsis Volatility Forecast Using GARCH, News Sentiment and Implied Volatility by : Jamie Atkinson
Download or read book Volatility Forecast Using GARCH, News Sentiment and Implied Volatility written by Jamie Atkinson and published by . This book was released on 2019 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Forecasting Volatility in the Financial Markets by : John L. Knight
Download or read book Forecasting Volatility in the Financial Markets written by John L. Knight and published by Butterworth-Heinemann. This book was released on 2002 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Trading and Pricing Financial Derivatives by : Patrick Boyle
Download or read book Trading and Pricing Financial Derivatives written by Patrick Boyle and published by Walter de Gruyter GmbH & Co KG. This book was released on 2018-12-17 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trading and Pricing Financial Derivatives is an introduction to the world of futures, options, and swaps. Investors who are interested in deepening their knowledge of derivatives of all kinds will find this book to be an invaluable resource. The book is also useful in a very applied course on derivative trading. The authors delve into the history of options pricing; simple strategies of options trading; binomial tree valuation; Black-Scholes option valuation; option sensitivities; risk management and interest rate swaps in this immensely informative yet easy to comprehend work. Using their vast working experience in the financial markets at international investment banks and hedge funds since the late 1990s and teaching derivatives and investment courses at the Master's level, Patrick Boyle and Jesse McDougall put forth their knowledge and expertise in clearly explained concepts. This book does not presuppose advanced mathematical knowledge, though it is presented for completeness for those that may benefit from it, and is designed for a general audience, suitable for beginners through to those with intermediate knowledge of the subject.
Book Synopsis An International Comparison of Implied, Realized and GARCH Volatility Forecasts by : Apostolos Kourtis
Download or read book An International Comparison of Implied, Realized and GARCH Volatility Forecasts written by Apostolos Kourtis and published by . This book was released on 2016 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compare the predictive ability and economic value of implied, realized and GARCH volatility models for 13 equity indices from 10 countries. Model ranking is similar across countries, but varies with the forecast horizon. At the daily horizon, the Heterogeneous Autoregressive model offers the most accurate predictions while an implied volatility model that corrects for the volatility risk premium is superior at the monthly horizon. Widely used GARCH models have inferior performance in almost all cases considered. All methods perform significantly worse over the 2008-09 crisis period. Finally, implied volatility offers significant improvements against historical methods for international portfolio diversification.
Book Synopsis A Test of Efficiency for the S & P 500 Index Option Market Using Variance Forecasts by : Jaesun Noh
Download or read book A Test of Efficiency for the S & P 500 Index Option Market Using Variance Forecasts written by Jaesun Noh and published by . This book was released on 1993 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: To forecast future option prices, autoregressive models of implied volatility derived from observed option prices are commonly employed [see Day and Lewis (1990), and Harvey and Whaley (1992)]. In contrast, the ARCH model proposed by Engle (1982) models the dynamic behavior in volatility, forecasting future volatility using only the return series of an asset. We assess the performance of these two volatility prediction models from S&P 500 index options market data over the period from September 1986 to December 1991 by employing two agents who trade straddles, each using one of the two different methods of forecast. Straddle trading is employed since a straddle does not need to be hedged. Each agent prices options according to her chosen method of forecast, buying (selling) straddles when her forecast price for tomorrow is higher (lower) than today's market closing price, and at the end of each day the rates of return are computed. We find that the agent using the GARCH forecast method earns greater profit than the agent who uses the implied volatility regression (IVR) forecast model. In particular, the agent using the GARCH forecast method earns a profit in excess of a cost of $0.25 per straddle with the near-the-money straddle trading.
Book Synopsis The Forecasting Ability of Correlations Implied in Foreign Exchange Options by : José Campa
Download or read book The Forecasting Ability of Correlations Implied in Foreign Exchange Options written by José Campa and published by . This book was released on 1997 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper evaluates the forecasting accuracy of correlation derived from implied volatilities in dollar-mark, dollar-yen, and mark-yen options from January 1989 to May 1995. As a forecast of realized correlation between the dollar-mark and dollar-yen, implied correlation is compared against three alternative forecasts based on time series data: historical correlation, RiskMetrics' exponentially weighted moving average correlation, and correlation estimated using a bivariate GARCH (1,1) model. At the one-month and three-month forecast horizons, we find that implied correlation outperforms, often significantly, these alternative forecasts. In combinations, implied correlation always incrementally improves the performance of other forecasts, but not the converse; in certain cases historically based forecasts contribute no incremental information to implied forecasts. The superiority of the implied correlation forecast holds even when forecast errors are weighted by realized variances, reflecting correlation's contribution to the dollar variance of a multicurrency portfolio.
Book Synopsis Volatility Forecasting in Futures Markets by : Theo Athanasiadis
Download or read book Volatility Forecasting in Futures Markets written by Theo Athanasiadis and published by . This book was released on 2015 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Forecasting Power of Implied Volatility by : Jonathan M. Godbey
Download or read book Forecasting Power of Implied Volatility written by Jonathan M. Godbey and published by . This book was released on 2005 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assuming use of the correct option pricing model and an efficient market, an option's implied volatility is the market's consensus forecast of future realized volatility over the remaining life of that option. We examine 460 of the Samp;P 500 firms to demonstrate that 1) implied volatility is a better forecaster of realized volatility than historic volatility or GARCH models and 2) the information content of implied volatility significantly decreases with liquidity. Since individual equity options are American style, we obtain implied volatility from calls and puts separately rather than only calls or pooled data.
Book Synopsis Volatility Forecasts and the At-the-Money Implied Volatility by : Gilles O. Zumbach
Download or read book Volatility Forecasts and the At-the-Money Implied Volatility written by Gilles O. Zumbach and published by . This book was released on 2008 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a given time horizon $ DT$, this article explores the relationship between the realized volatility (the volatility that will occur between $t$ and $t DT$), the implied volatility (corresponding to at-the-money option with expiry at $t DT$), and several forecasts for the volatility build from multi-scales linear ARCH processes. The forecasts are derived from the process equations, and the parameters set { it a priori}. An empirical analysis across multiple time horizons $ DT$ shows that a forecast provided by an I-GARCH(1) process (1 time scale) does not capture correctly the dynamic of the realized volatility. An I-GARCH(2) process (2 time scales, similar to GARCH(1,1)) is better, while a long memory LM-ARCH process (multiple time scales) replicates correctly the dynamic of the realized volatility and delivers consistently good forecast for the implied volatility. The relationship between market models for the forward variance and the volatility forecasts provided by ARCH processes is investigated. The structure of the forecast equations is identical, but with different coefficients. Yet the process equations for the variance are very different (postulated for a market model, induced by the process equations for an ARCH model), and not of any usual diffusive type when derived from ARCH.
Book Synopsis Forecasting Currency Volatility by : Shiu-yan Eddie Pong
Download or read book Forecasting Currency Volatility written by Shiu-yan Eddie Pong and published by . This book was released on 2003 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compare forecasts of the realized volatility of the pound, mark and yen exchange rates against the dollar, calculated from intraday rates, over horizons ranging from one day to three months. Our forecasts are obtained from a short memory ARMA model, a long memory ARFIMA model, a GARCH model and option implied volatilities. We find intraday rates provide the most accurate forecasts for the one-day and one-week forecast horizons while implied volatilities are at least as accurate as the historical forecasts for the one-month and three-month horizons. The superior accuracy of the historical forecasts, relative to implied volatilities, comes from the use of high frequency returns, and not from a long memory specification. We find significant incremental information in historical forecasts, beyond the implied volatility information, for forecast horizons up to one week.
Book Synopsis GARCH, Implied Volatilities and Implied Distribution by : Javiera Aguilar
Download or read book GARCH, Implied Volatilities and Implied Distribution written by Javiera Aguilar and published by . This book was released on 1999 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigates the information content and predictive power of implied volatility from currency options traded on the OTC-market.
Book Synopsis Multifractal Volatility by : Laurent E. Calvet
Download or read book Multifractal Volatility written by Laurent E. Calvet and published by Academic Press. This book was released on 2008-10-13 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Predicting Financial Volatility by : Martin Martens
Download or read book Predicting Financial Volatility written by Martin Martens and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent evidence suggests option implied volatility provides better forecasts of financial volatility than time-series models based on historical daily returns. In particular it is found that daily GARCH forecasts have no or little incremental information over that already contained in implied volatilities. In this study both the measurement and the forecasting of financial volatility is improved using high-frequency data and the latest proposed model for volatility, a long memory model. The results indicate that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with implied volatility and sometimes even outperform implied volatility.
