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Author: Xiaolin Wang
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 0
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Book Description
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.
Author: Xiaolin Wang
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 0
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Book Description
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.
Author: Peter Reinhard Hansen
Publisher:
ISBN:
Category :
Languages : en
Pages : 37
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Book Description
Handbook chapter on volatility forecasting using high-frequency data, with surveys of reduced-form volatility forecasts and model-based volatility forecasts.
Author: Jonathan H. Wright
Publisher:
ISBN:
Category : Rate of return
Languages : en
Pages : 38
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Book Description
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.
Author: Yujia Hu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Constantin Roth
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Constantin A. Roth
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Vladimir Pyrlik
Publisher:
ISBN: 9788073446154
Category :
Languages : en
Pages :
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Book Description
Author: Satchidananda Dehuri
Publisher: Springer Nature
ISBN: 3030390330
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems. This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing.
Author: Holger Kömm
Publisher: Springer
ISBN: 3658125969
Category : Business & Economics
Languages : en
Pages : 188
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Book Description
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.
Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 1118272056
Category : Business & Economics
Languages : en
Pages : 566
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Book Description
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.
