Download or read book Evaluating the Forecast Performance of Alternative Volatility Estimators Realized Volatility written by Hui Ma and published by . This book was released on 2004 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 Alternative Approach to Volatility Forecasting and Evaluating Forecasting Performance written by Hyungjin Lim and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Measuring Market Risk written by Kevin Dowd and published by John Wiley & Sons. This book was released on 2003-02-28 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most up-to-date resource on market risk methodologies Financial professionals in both the front and back office require an understanding of market risk and how to manage it. Measuring Market Risk provides this understanding with an overview of the most recent innovations in Value at Risk (VaR) and Expected Tail Loss (ETL) estimation. This book is filled with clear and accessible explanations of complex issues that arise in risk measuring-from parametric versus nonparametric estimation to incre-mental and component risks. Measuring Market Risk also includes accompanying software written in Matlab—allowing the reader to simulate and run the examples in the book.
Download or read book Predictive Ability of Asymmetric Volatility Models at Medium Term Horizons written by Turgut Kisinbay and published by International Monetary Fund. This book was released on 2003-06 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.
Download or read book Volatility Forecasting written by Torben Gustav Andersen and published by . This book was released on 2005 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
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 Handbook of Economic Forecasting written by Graham Elliott and published by Elsevier. This book was released on 2013-08-23 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics
Download or read book Hybrid Volatility Forecasting Models Based on Machine Learning of High Frequency Data written by Xiaolin Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility modeling and forecasting are crucial in risk management and pricing derivatives. High-frequency financial data are dynamic and affected by the microstructure noise. For the univariate case, we define the two-scale realized volatility estimator as the measure of the volatility of high-frequency financial data. Two main models for volatility, Generalized Autoregressive Conditional Heteroscedastic (GARCH) and Heterogeneous Autoregressive (HAR), are evaluated and compared for the realized volatility forecast of four major stock indices high-frequency data. We also consider the measures of jump component and heteroskedasticity of the error in the extended HAR models. For the improvement of forecasting accuracy of realized volatility, this dissertation develops hybrid forecasting models combining the GARCH and HAR family models with the machine learning methods, Support Vector Regression(SVR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Transformer. We construct hybrid models using the outputs of the GARCH and HAR family models. In the empirical application, we demonstrate improvements of the hybrid models for one-day ahead realized volatility forecast accuracy. The results show that the hybrid LSTM and Transformer based models provide more accurate forecasts than the other models. In the financial markets, it is well accepted that the volatilities are time-varying correlated across the indices. We construct two portfolios, the Index portfolio and the Forex portfolio. The Index portfolio contains three major stock indices, and the Forex portfolio includes three major exchange rates. We model the conditional covariances of the two portfolios with BEKK, DCC-GARCH, and Vector HAR. The hybrid models combine the estimations of traditional multivariate models and the machine learning framework. Results of the study indicate that for one-day ahead volatility matrix forecasting, these hybrid models can achieve better performance than the traditional models for the two portfolios.
Download or read book Correcting the Errors written by Torben Gustav Andersen and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Evaluation of Realized Volatility Predictions from Models with Leptokurtically and Asymmetrically Distributed Forecast Errors written by Stavros Antonios Degiannakis and published by . This book was released on 2018 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distribution. Additionally, the widely applied forecasting evaluation function, the predicted mean squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version.
Download or read book Modelling and Forecasting Noisy Realized Volatility written by M. Asai and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Volatility Forecasts written by I-Ming Jiang and published by . This book was released on 2013 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates the volatility forecasting abilities of return-based and range-based estimators for two stock indices and two individual stocks in the U.S. stock market. The forecasting performances are evaluated by two robust statistical loss functions, and further by financial applications in risk management and option pricing. Consistent with previous studies, the range-based volatility forecasts outperform in terms of statistical evaluation, Value-at-Risk calculation, and option pricing. However, return-based volatility forecasts prove superior in the evaluation of market risk capital requirements.
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 The One Trading Day Ahead Forecast Errors of Intra Day Realized Volatility written by Stavros Antonios Degiannakis and published by . This book was released on 2018 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more appropriate metric for evaluating volatility forecasts. We highlight the importance of standardizing the forecast errors with their volatility. The predictive accuracy of the models is investigated for the FTSE100, DAX30 and CAC40 European stock indices and the exchange rates of Euro to British Pound, US Dollar and Japanese Yen. Additionally, a trading strategy defined by the standardized forecast errors provides higher returns compared to the strategy based on the simple forecast errors. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized volatility models.
