Download or read book Forecasting Volatility Using Historical Data written by Stephen Figlewski and published by . This book was released on 2008 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying modern option valuation theory requires the user to forecast the volatility of the underlying asset over the remaining life of the option, a formidable estimation problem for long maturity instruments. The standard statistical procedures using historical data are based on assumptions of stability, either constant variance, or constant parameters of the variance process, that are unlikely to hold over long periods. This paper examines the empirical performance of different historical variance estimators and of the GARCH (1,1) model for forecasting volatility in important financial markets over horizons up to five years. We find several surprising results: In general, historical volatility computed over many past periods provides the most accurate forecasts for both long and short horizons; root mean squared forecast errors are substantially lower for long term than for short term volatility forecasts; it is typically better to compute volatility around an assumed mean of zero than around the realized mean in the data sample, and the GARCH model tends to be less accurate and much harder to use than the simple historical volatility estimator for this application.

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.

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

Download or read book Forecasting Volatility Using Long Memory and Comovements written by George J. Jiang and published by . This book was released on 2013 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Horizon-matched historical volatility is commonly used to forecast future volatility for option valuation under the Statement of Financial Accounting Standards 123R. In this paper, we empirically investigate the performance of using historical volatility to forecast long-term stock return volatility in comparison with a number of alternative forecasting methods. Analyzing forecasting errors and their impact on reported income due to option expensing, we find that historical volatility is a poor forecast for long-term volatility and shrinkage adjustment towards comparable-firm volatility only slightly improves its performance. Forecasting performance can be improved substantially by incorporating both long memory and comovements with common market factors. We also experiment with a simple mixed-horizon realized volatility model and find its long-term forecasting performance to be more accurate than historical forecasts but less accurate than long-memory forecasts.

Download or read book Forecasting Volatility written by Stephen Figlewski and published by . This book was released on 1997 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting Volatility Using High Frequency Data written by Peter Reinhard Hansen and published by . This book was released on 2018 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook chapter on volatility forecasting using high-frequency data, with surveys of reduced-form volatility forecasts and model-based volatility forecasts.

Download or read book The Oxford Handbook of Economic Forecasting written by Michael P. Clements and published by OUP USA. This book was released on 2011-07-08 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.

Download or read book Forecasting Volatility in Canadian Markets written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Long Term Volatility Forecasting written by Nicholas Reitter and published by . This book was released on 2018 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: A variety of historical-volatility, peer-historical-volatility, implied-volatility and blended estimators of stock price volatility are developed and tested for a group of large U.S. companies over roughly a thirty-year window. Longer-term historical estimators (up to fifteen years) are found to outperform shorter-term estimators as forecasts of five- to seven-year realized volatility. Inclusion of implied volatility into forecasts at low weightings is found to have little discernible effect on overall results; at higher weightings, implied volatility appears actually to detract modestly from forecast accuracy. Nevertheless, certain correlations show that implied volatility may contribute strongly toward forecasting volatility in some situations. Finally, patterns of apparently-cyclical variation in historical forecast-errors are presented for exploration and inclusion in potential future modeling.

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.

Download or read book The Comparison of Forecasting Performance of Historical Volatility Versus Realized Volatility written by Linkai Huang and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: When forecasting stock market volatility with a standard volatility method (GARCH), it is common that the forecast evaluation criteria often suggests that the realized volatility (the sum of squared high-frequency returns) has a better prediction performance compared to the historical volatility (extracted from the close-to-close return). Since many extensions of the GARCH model have been developed, we follow the previous works to compare the historical volatility with many new GARCH family models (i.e., EGARCH, TGARCH, and APARCH model) and realized volatility with the ARMA model. Our analysis is based on the S&P 500 index from August 1st, 2018 to February 1st, 2019 (127 trading days), and the data has been separated into an estimation period (90 trading days) and an evaluation period (37 trading days). In the evaluation period, by taking realized volatility as the proxy of the true volatility, our empirical result shows that the realized volatility with ARMA model provides more accurate predictions, compared to the historical volatility with the GARCH family models.

Download or read book Volatility Forecasting and Liquidity written by Peter A. Brous and published by . This book was released on 2008 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: We find that, on average, for the full sample of stocks comprising the Samp;P 100 index, theoretically superior implied volatilities are less accurate forecasts than historically based forecasts of future volatilities. However, when we split our sample based on proxies for liquidity, we find that implied volatility forecasts are more accurate than historical volatility forecasts for more liquid stocks, and the inverse is true for the subsample of less liquid stocks. Additionally, we document that among historical measures, Parkinson's extreme value estimator and the adjusted mean absolute deviation are more accurate than the alternative historical estimators. Overall, our results suggest that, for high liquidity stocks, the implied volatility measure is likely to provide a more accurate forecast of future volatility but for low liquidity stocks Parkinson's extreme value estimator and the adjusted mean absolute deviation using historical prices are likely to provide the best forecasts.

Download or read book Volatility and Correlation written by Riccardo Rebonato and published by John Wiley & Sons. This book was released on 2005-07-08 with total page 864 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Volatility and Correlation 2nd edition: The Perfect Hedger and the Fox, Rebonato looks at derivatives pricing from the angle of volatility and correlation. With both practical and theoretical applications, this is a thorough update of the highly successful Volatility & Correlation – with over 80% new or fully reworked material and is a must have both for practitioners and for students. The new and updated material includes a critical examination of the ‘perfect-replication’ approach to derivatives pricing, with special attention given to exotic options; a thorough analysis of the role of quadratic variation in derivatives pricing and hedging; a discussion of the informational efficiency of markets in commonly-used calibration and hedging practices. Treatment of new models including Variance Gamma, displaced diffusion, stochastic volatility for interest-rate smiles and equity/FX options. The book is split into four parts. Part I deals with a Black world without smiles, sets out the author’s ‘philosophical’ approach and covers deterministic volatility. Part II looks at smiles in equity and FX worlds. It begins with a review of relevant empirical information about smiles, and provides coverage of local-stochastic-volatility, general-stochastic-volatility, jump-diffusion and Variance-Gamma processes. Part II concludes with an important chapter that discusses if and to what extent one can dispense with an explicit specification of a model, and can directly prescribe the dynamics of the smile surface. Part III focusses on interest rates when the volatility is deterministic. Part IV extends this setting in order to account for smiles in a financially motivated and computationally tractable manner. In this final part the author deals with CEV processes, with diffusive stochastic volatility and with Markov-chain processes. Praise for the First Edition: “In this book, Dr Rebonato brings his penetrating eye to bear on option pricing and hedging.... The book is a must-read for those who already know the basics of options and are looking for an edge in applying the more sophisticated approaches that have recently been developed.” —Professor Ian Cooper, London Business School “Volatility and correlation are at the very core of all option pricing and hedging. In this book, Riccardo Rebonato presents the subject in his characteristically elegant and simple fashion...A rare combination of intellectual insight and practical common sense.” —Anthony Neuberger, London Business School

Download or read book Price Volatility Forecasts for Agricultural Commodities written by Guillermo Benavides and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been substantial research effort aimed to forecast futures price return volatilities of financial and commodity assets. Some part of this research focuses on the performance of time-series models (in particular ARCH models) versus option implied volatility models. A significant part of the literature related to this topic shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of volatility forecast models for the case of corn and wheat futures price returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), an option implied and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. The results show that the option implied model is superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-square-errors. Given these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied. In addition, the results of this paper are consistent to that part of the literature that emphasizes the difficulty on being accurate about forecasting asset price return volatility. This is because the explanatory power (coefficient of determination) calculated in the forecast regressions were relatively low.

Download or read book Forecasting Volatility in Options Trading Nexus Between Historical Volatility and Implied Volatility written by Shabarisha N. and published by . This book was released on 2016 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book VIX Forecasting written by Utkarsh Majmudar and published by . This book was released on 2004 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: The celebrated Black-Scholes model for valuing options uses a number of inputs - current stock price, risk-free interest rate, exercise price, time to maturity and volatility of returns. One critical input is the volatility of returns. Historical volatility is of little use as what is relevant is future volatility. Assuming efficient markets, a good source of volatility estimate is the implied volatility. Among the inputs to the Black-Scholes model all except volatility are known in advance. The output - the current call price is also known. Implied volatility is arrived at using the current call prices and all other inputs in the Black-Scholes formula to ascertain the volatility. This is a forward looking volatility estimate. We forecast volatility using VIX data obtained from CBOE. This paper adds value to extant literature by forecasting the revised VIX using a variety of forecasting tools like GARCH, EGARCH, APARCH, GJR and IGARCH. The EGARCH model is selected as it performs well on forecast accuracy. Using combinations of options, it is possible to trade volatility as if it were any other commodity, so that accurate predictions of future volatility give the forecaster the potential to make a more direct profit.

Download or read book Forecasting Expected Returns in the Financial Markets written by Stephen Satchell and published by Elsevier. This book was released on 2011-04-08 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting returns is as important as forecasting volatility in multiple areas of finance. This topic, essential to practitioners, is also studied by academics. In this new book, Dr Stephen Satchell brings together a collection of leading thinkers and practitioners from around the world who address this complex problem using the latest quantitative techniques.*Forecasting expected returns is an essential aspect of finance and highly technical *The first collection of papers to present new and developing techniques *International authors present both academic and practitioner perspectives