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Exploiting High Frequency Data For Volatility Forecasting And Portfolio Selection Kumulative Dissertation
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Book Synopsis Exploiting high frequency data for volatility forecasting and portfolio selection : [kumulative Dissertation] by : Yujia Hu
Download or read book Exploiting high frequency data for volatility forecasting and portfolio selection : [kumulative Dissertation] written by Yujia Hu and published by . This book was released on 2012 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: An instant may matter for the course of an entire life. It is with this idea that the present research had its inception. High frequency financial data are becoming increasingly available and this has triggered research in financial econometrics where information at high frequency can be exploited for different purposes. The most prominent example of this is the estimation and forecast of financial volatility. The research, chapter by chapter is summarized below. Chapter 1 provides empirical evidence on univariate realized volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. It examines leverage and volatility feedback effects among continuous and jump components of the S&P500 price and volatility dynamics, using recently developed methodologies to detect jumps and to disentangle their size from the continuous return and the continuous volatility. The research finds that jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improves the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative than positive jump semivariance. A simple autoregressive model is proposed and this is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated. Chapter 2 investigates the out-of-sample performance and the economic value of multivariate forecasting models for volatility of exchange rate returns. It finds that, when the realized covariance matrix approximates the true latent covariance, a model that uses high frequency information for the correlation is more appropriate compared to alternative models that uses only low-frequency data. However multivariate FX returns standardized by the.
Book Synopsis Exploiting High Frequency Data for Volatility Forecasting and Portfolio Selection by : Yujia Hu
Download or read book Exploiting High Frequency Data for Volatility Forecasting and Portfolio Selection written by Yujia Hu and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An instant may matter for the course of an entire life. It is with this idea that the present research had its inception. High frequency financial data are becoming increasingly available and this has triggered research in financial econometrics where information at high frequency can be exploited for different purposes. The most prominent example of this is the estimation and forecast of financial volatility. The research, chapter by chapter is summarized below. Chapter 1 provides empirical evidence on univariate realized volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. It examines leverage and volatility feedback effects among continuous and jump components of the S & P500 price and volatility dynamics, using recently developed methodologies to detect jumps and to disentangle their size from the continuous return and the continuous volatility. The research finds that jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improves the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative than positive jump semivariance. A simple autoregressive model is proposed and this is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated. Chapter 2 investigates the out-of-sample performance and the economic value of multivariate forecasting models for volatility of exchange rate returns. It finds that, when the realized covariance matrix approximates the true latent covariance, a model that uses high frequency information for the correlation is more appropriate compared to alternative models that uses only low-frequency data. However multivariate FX returns standardized by the.
Book Synopsis High Frequency Data, Frequency Domain Inference and Volatility Forecasting by : Jonathan H. Wright
Download or read book High Frequency Data, Frequency Domain Inference and Volatility Forecasting written by Jonathan H. Wright and published by . This book was released on 1999 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: While it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high frequency data. The method avoids using a tight parametric model, by instead simply fitting a long autoregression to log-squared, squared or absolute high frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise.
Book Synopsis Forecasting High-Frequency Volatility Shocks by : Holger Kömm
Download or read book Forecasting High-Frequency Volatility Shocks written by Holger Kömm and published by Springer. This book was released on 2016-02-08 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks. Holger Kömm embeds the proposed strategy in a monitoring system, using first, a sequence of competing estimators to compute the unobservable volatility; second, a new two-state Markov switching mixture model for autoregressive and zero-inflated time-series to identify structural breaks in a latent data generation process and third, a selection of competing pattern recognition algorithms to classify the potential information embedded in unexpected, but public observable text data in shock and nonshock information. The monitor is trained, tested, and evaluated on a two year survey on the prime standard assets listed in the indices DAX, MDAX, SDAX and TecDAX.
Book Synopsis Forecasting Volatility Using High Frequency Data by : Peter Reinhard Hansen
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.
Book Synopsis Volatility and Correlation by : Riccardo Rebonato
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
Book Synopsis Hybrid Volatility Forecasting Models Based on Machine Learning of High-Frequency Data by : Xiaolin Wang
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.
Book Synopsis Stock Index Volatility Forecasting with High Frequency Data by : Eugenie M. J. H. Hol
Download or read book Stock Index Volatility Forecasting with High Frequency Data written by Eugenie M. J. H. Hol and published by . This book was released on 2002 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multivariate Volatility Estimation with High Frequency Data Using Fourier Method by : Maria Elvira Mancino
Download or read book Multivariate Volatility Estimation with High Frequency Data Using Fourier Method written by Maria Elvira Mancino and published by . This book was released on 2013 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Availability of high frequency data has improved the capability of computing volatility in an efficient way. Nevertheless, measuring volatility/covariance from the observation of the asset price is challenging for two main reasons: observed asset prices are generally affected by noise microstructure effects and tick-by-tick returns are asynchronous across different assets. In this paper we review the definition and the statistical properties of the so called Fourier estimator of multivariate volatility, with particular focus on using high frequency data. Exploiting the fact that the method allows to compute both the integrated and the instantaneous volatility, we show how to obtain estimators of the volatility of the volatility and the leverage as well. Further, we study the performance of the estimator in forecasting and in terms of portfolio utility in the presence of microstructure noise contaminations.
Book Synopsis Uncovering the Benefit of High-Frequency Data in Portfolio Allocation by : Ingmar Nolte
Download or read book Uncovering the Benefit of High-Frequency Data in Portfolio Allocation written by Ingmar Nolte and published by . This book was released on 2015 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: In previous studies, high-frequency data has been used to improve portfolio allocation by estimating the full realized covariance matrix. In this paper, we show that strategies using high-frequency data for measuring and forecasting univariate realized volatility alone can already generate statistically significant and economically tangible benefits compared to low-frequency strategies. Most importantly, however, high-frequency data also allow us to separate realized volatility into different components and construct realized higher moments. Strategies using upside and downside volatility components as well as realized skewness are shown to reveal additional information and deliver incremental economic benefits over strategies using total realized volatility alone.
Book Synopsis Fourier Volatility Forecasting with High Frequency Data and Microstructure Noise by : Emilio Barucci
Download or read book Fourier Volatility Forecasting with High Frequency Data and Microstructure Noise written by Emilio Barucci and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the forecasting performance of the Fourier volatility estimator in the presence of microstructure noise. Analytical comparison and simulation studies indicate that the Fourier estimator significantly outperforms realized volatility type estimators in particular for high frequency data and when the noise component is relevant. We show that Fourier estimator in general has a better performance even in comparison with methods specifically designed to handle market microstructure contaminations.
Book Synopsis Measuring and Forecasting Financial Market Volatility Using High-frequency Data by : Karim Bannouh
Download or read book Measuring and Forecasting Financial Market Volatility Using High-frequency Data written by Karim Bannouh and published by . This book was released on 2013 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Topics in Modeling Volatility Based on High-frequency Data by : Constantin A. Roth
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.
Book Synopsis Ph.D. Dissertation by : Simon Bodilsen
Download or read book Ph.D. Dissertation written by Simon Bodilsen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forecasting Volatility Using High Frequency Data by :
Download or read book Forecasting Volatility Using High Frequency Data written by and published by . This book was released on 2016 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Methods for High Frequency Financial Data by : Xin Zhang
Download or read book Statistical Methods for High Frequency Financial Data written by Xin Zhang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation work focuses on developing statistical methods for volatility estimation and prediction with high frequency financial data. We consider two kinds of volatility: integrated volatility and jump variation. In the first part, we introduce the methods for integrated volatility estimation with the presence of microstructure noise. We will first talk about the optimal sampling frequency for integrated volatility estimation since subsampling is very popular in practice. Then we will discuss about those methods based on subsampling. Two-scale estimator is developed using the subsampling idea while taking advantage of all of the data. An extension to the multi-scale further improves the efficiency of the estimation. In the second part, we propose a heterogenous autoregressive model for the integrated volatility estimators based on subsampling. An empirical approach is to estimate integrated volatility using high frequency data and then fit the estimates to a low frequency heterogeneous autoregressive volatility model for prediction. We provide some theoretical justifications for the empirical approach by showing that these estimators approximately obey a heterogenous autoregressive model for some appropriate underlying price and volatility processes. In the third part, we propose a method for jump variation estimation using wavelet techniques. Previously, jumps are not assumed in the model. In this part, we will concentrate on jump variation estimation and there- fore, we will be able to estimate the integrated volatility and jump variation individually. We show that by choosing a threshold, we will be able to detect the jump location, and by using the realized volatility processes instead of the original price process, we will be able to improve the convergence rate of estimation. We include both numerical and empirical results of this method.
Book Synopsis Topics in Modeling Volatility Based on High-frequency Data by : Constantin Roth
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.
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