The Comparison of Forecasting Performance of Historical Volatility Versus Realized Volatility PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download The Comparison of Forecasting Performance of Historical Volatility Versus Realized Volatility PDF full book. Access full book title The Comparison of Forecasting Performance of Historical Volatility Versus Realized Volatility by Linkai Huang. Download full books in PDF and EPUB format.
Author: Linkai Huang
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
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.
Author: Linkai Huang
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
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.
Author: Ser-Huang Poon
Publisher: John Wiley & Sons
ISBN: 0470856157
Category : Business & Economics
Languages : en
Pages : 236
Get Book Here
Book Description
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.
Author: George J. Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
Get Book Here
Book Description
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.
Author: Daniele Mastro
Publisher:
ISBN:
Category :
Languages : en
Pages : 53
Get Book Here
Book Description
Volatility forecasting is a critical task in financial markets and its importance has increased exponentially after the 2007-2008 financial crisis. As today, there is a lack of consensus among academics and practitioners on which is the most suitable forecasting model.This study contemplates two different categories of models: the well-known ARCH-family models, which model the historical volatility (or conditional variance) and the HAR-RV developed by Corsi (2004), which considers realized measures (the so called realized volatility). To compare the performance of the selected models the study proposes an in-sample as well as an out-of-sample comparison of the Mean Squared Errors (MSE) between the forecasted volatilities versus the actual or observed volatilities. The research focuses on four of the major equity indexes worldwide: the Standard and Poor's 500 (SPX), the FTSE 100 (UKX), the Deutsche Börse Stock Index (DAX) and the Nikkei 255 (NKY) from the 1st September 2009 to the 30th June 2014.The results of this paper are consistent with the recent literature. The HAR- RV outperforms ARCH-family models no matter the index and the time horizon, confirming that the realized volatility is by far a more precise measure of volatility than conditional variance. Also, log-realized volatilities are to be preferred in using the HAR-RV given the lognormal distribution of realized volatility, as suggested by Corsi (2009).
Author: Stephen Figlewski
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 98
Get Book Here
Book Description
Author: Riccardo Rebonato
Publisher: John Wiley & Sons
ISBN: 0470091401
Category : Business & Economics
Languages : en
Pages : 864
Get Book Here
Book Description
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
Author: Stephen Satchell
Publisher: Elsevier
ISBN: 0080471420
Category : Business & Economics
Languages : en
Pages : 428
Get Book Here
Book Description
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
Author: Christopher Todd Higgins
Publisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 70
Get Book Here
Book Description
Author: Takahiro Hattori
Publisher:
ISBN:
Category :
Languages : en
Pages : 9
Get Book Here
Book Description
This paper first evaluates the volatility modeling in the Bitcoin market in terms of its realized volatility, which is considered to be a reliable proxy of its true volatility. In addition, we also rely on the important work by Patton (2011), which shows good measures for making the forecast accuracy robust to noise in the imperfect volatility proxy. We empirically show that (1) the asymmetric volatility models such as EGARCH and APARCH have a higher predictability, and (2) the volatility model with normal distribution performs better than the fat-tailed distribution such as skewed t distribution.
Author: Dexiang Mei
Publisher: Scientific Research Publishing, Inc. USA
ISBN: 164997048X
Category : Business & Economics
Languages : en
Pages : 139
Get Book Here
Book Description
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.
