Download or read book Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging written by Zongwu Cai and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modelling Volatility in Financial Markets written by Chun Liu and published by . This book was released on 2007 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, I study the dynamics of the volatility process and focus on estimation and forecasting. Recent research uses high frequency intraday data to construct ex post measures of daily volatility including realized volatility (RV). Chapter 1 is the introduction. In Chapter 2, I use a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. I focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations I demonstrate that the estimation approach is effective in identifying and dating structural breaks. Applied to daily S & P 500 data, I find strong evidence of a single structural break in log(RV). The main effect of the break is on the long-run mean and variance of log-volatility. Chapter 3 uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include HAR specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and leverage term. The Bayesian model averaging provides very competitive density forecasts and consistent but modest improvements in point forecasts over the benchmarks. Applied to equity and exchange rate volatility over several forecast horizons, the Bayesian model averaging provides the best performance compared to the benchmarks including HAR, AR and simple model averaging models. I discuss the reasons for this, including the importance of using realized power variation as a predictor. In the last chapter, I propose a new joint model of volatility and duration in high frequency framework using tick-by-tick data. This model decomposes the conditional variance into different volatility components associated with different transaction horizons. Using stock market data, I demonstrate its superiority over the traditional GARCH counterpart. In addition, I show that a fat-tailed t-distribution for return innovations and a Burr distribution for duration innovations improve density forecasts, compared with normal and exponential distribution, respectively.

Download or read book Handbook of Volatility Models and Their Applications written by Luc Bauwens and published by John Wiley & Sons. This book was released on 2012-04-17 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.

Download or read book Realized Range Volatility Forecasting written by Massimiliano Caporin and published by . This book was released on 2013 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we estimate, model and forecast Realized Range Volatility, a realized measure and estimator of the quadratic variation of financial prices. This quantity was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We consider the impact of the microstructure noise in high frequency data and correct our estimations, following a known procedure. Then, we model the Realized Range accounting for the well-known stylized effects present in financial data and we investigate the role that macroeconomic and financial variables play when forecasting daily stocks volatility. We consider an HAR model with asymmetric effects with respect to the volatility and the return, and GARCH and GJR specifications for the variance equation. Moreover, we consider a non Gaussian distribution for the innovations. Finally, we extend the model including macroeconomic and financial variables that capture present and the future state of the economy. We find that these variables are significantly correlated with the first common component of the volatility series and they have a highly in-sample explanatory power. The analysis of the forecast performance in 16 NYSE stocks suggests that the introduction of asymmetric effects with respect to the returns and the volatility in the HAR model result in significant improvement in the point forecasting accuracy as well and the variables related with the U.S stock market performance and proxies for the credit risk.

Download or read book Modeling and Forecasting Realized Volatility written by Torben G. Andersen and published by . This book was released on 2001 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions.

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 Quantile Forecast Combinations in Realised Volatility Prediction written by Loukia Meligkotsidou and published by . This book was released on 2015 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper tests whether it is possible to improve point, quantile and density forecasts of realized volatility by conditioning on macroeconomic and financial variables. We employ quantile autoregressive models augmented with a plethora of macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarise the information content in the candidate predictors. Our findings suggest that no single variable is able to provide more information for the evolution of the volatility distribution beyond that contained in its own past. The best performing variable is the return on the stock market followed by the inflation rate. Our complete subset approach achieves superior quantile, density and point predictive performance relative to the univariate models and the autoregressive benchmark.

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 Modeling and Forecasting Long Range Dependence in Volatility written by Nan Qu and published by . This book was released on 2010 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis conducts three exercises on volatility modeling of financial assets. We are essentially interested in the estimation and forecasting of daily volatility, a measure of the strength of price movements over daily intervals. Two of the exercises are in the realm of high frequency data: modeling and forecasting realized volatility which is constructed from intra-day returns. The other exercise is concerned with discrete stochastic volatility modeling using daily returns. The main focus of each exercise is to represent the high degree of volatility persistence, which is an important stylized fact of daily volatility.In the first exercise, daily realized volatility of the Yen/USD exchange rate is modeled through an autoregressive and moving-average fractionally integrated (ARFIMA) process. We differ from previous studies by averaging across a set of ARFIMA and ARMA models with different orders of autoregressive and moving-average polynomials. The vehicle used to execute this averaging exercise is Bayesian model averaging, through which part of the uncertainty introduced by model selection is integrated out. We examine the practical usefulness of our method by conducting a rolling-sample estimation, and the results indicate the weighted average forecast out-performs that of a single model at long-term horizons by providing smaller mean squared forecast errors.The second exercise is concerned with Bayesian estimation of a long memory stochastic volatility (SV) model. We use a high-order moving-average process to approximate the fractional integration specified for the latent log volatility. As such, the long memory SV model can be expressed in a state-space form, which facilitates the implementation of Markov chain Monte Carlo (MCMC) simulation when parameters and latent volatility are estimated. We update the set of memory parameter and volatility of volatility parameter in one block in the MCMC algorithm, by using the hessian matrix. A Monte Carlo study indicates in general, when the posterior mean is treated as a point estimator of parameters, our Bayesian method compares well with classical methods. Furthermore, the Bayesian estimator tends to outperform the popular frequency quasi maximum likelihood estimator, according to the root mean square error criterion, with small and medium sample size. An empirical analysis of the daily Yen/USD exchange rate spanning 26 years is conducted, and the degree of persistency in volatility is found to be consistent with that from the first exercise when high frequency data are used.In the third exercise, we look at the long memory property from a different angle. There has been a large literature using specifications other than fractional integration to mimic the long memory property in time series analysis, although there are few applications to realized volatility. In this exercise, regime switching models are fitted to daily realized volatility of the JPY/USD exchange rate from 1996 to 2009. Both in-sample fit and out-of-sample forecasting are used to compare across the three types of models, including ARFIMA, regime switching and sum of short memory processes. An extensive recursive estimation over one year suggests that regime switching is superior in capturing the dynamics of the time series examined, and generating more accurate out-of-sample forecasts.

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 The Quantile Heterogeneous Autoregressive Model of Realized Volatility written by Konstantin Kuck and published by . This book was released on 2018 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: We provide a comprehensive view on volatility dynamics in precious metals and crude oil markets. Using high-frequency futures data, we construct realized volatilities and estimate (Quantile) Heterogeneous Autoregressive models for the daily volatility of Gold, Silver and Crude Oil futures. We model realized volatility as a linear function of lagged realized volatility measured over different time resolutions to explicitly account for the potentially heterogeneous impact of market participants with different trading motives and investment horizons. Using quantile regression allows us to identify potential non-linearities and asymmetries in the short-, mid- and long-term autoregressive dynamics with respect to different levels of current volatility. We document considerable changes in the relative importance of short-, mid-, and long-term volatility components under varying market conditions. The patterns that we identify are remarkably similar across the three assets. Specifically, past daily and monthly volatility have a strong impact on today's volatility, when current volatility is low (lower quantiles of the volatility distribution). The effect of past weekly volatility, however, increases distinctly from lower to higher quantiles of the conditional volatility distribution. The results might indicate considerable investor attention shifts and changes in the proportions of traders with different time horizons.

Download or read book Topics in Modeling Volatility Based on High frequency Data written by Constantin A. Roth and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.

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 Topics in Modeling Volatility Based on High frequency Data written by Constantin Roth and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.

Download or read book Forecasting Realized Volatility with Changes of Regimes written by Giampiero M. Gallo and published by . This book was released on 2014 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Realized volatility of financial time series generally shows a slow-moving average level from the early 2000s to recent times, with alternating periods of turmoil and quiet. Modeling such a pattern has been variously tackled in the literature with solutions spanning from long-memory, Markov switching and spline interpolation. In this paper, we explore the extension of Multiplicative Error Models to include a Markovian dynamics (MS-MEM). Such a model is able to capture some sudden changes in volatility following an abrupt crisis and to accommodate different dynamic responses within each regime. The model is applied to the realized volatility of the S&P500 index: next to an interesting interpretation of the regimes in terms of market events, the MS-MEM has better in-sample fitting capability and achieves good out-of-sample forecasting performances relative to alternative specifications.

Download or read book Model Averaging for Volatility Forecasting Option Pricing and Asset Allocation written by Georgia Papadaki and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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