Exponential High Frequency Based Volatility Eheavy Models
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Exponential High Frequency Based Volatility Eheavy Models
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Author : Yongdeng Xu
language : en
Publisher:
Release Date : 2022
Exponential High Frequency Based Volatility Eheavy Models written by Yongdeng Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
This paper proposes an Exponential HEAVY (EHEAVY) model. The model specifies the dynamics of returns and realized measures of volatility in an exponential form, which guarantees the positivity of volatility without restrictions on parameters and naturally allows the asymmetric effects. It provides a more flexible modelling of the volatility than the HEAVY models. A joint quasi-maximum likelihood estimation and closed form multi-step ahead forecasting is derived. The model is applied to 31 assets extracted from the Oxford-Man Institute's realized library. The empirical results show that the dynamic of return volatility is driven by the realized measure, while the asymmetric effect is captured by the return shock (not by the realized return shock). Hence, both return and realized measure are included in the return volatility equation. Out-of-sample forecast and portfolio exercise further shows the superior forecasting performance of the EHEAVY model, in both statistical and economic sense.
Handbook Of High Frequency Trading And Modeling In Finance
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Author : Ionut Florescu
language : en
Publisher: John Wiley & Sons
Release Date : 2016-04-05
Handbook Of High Frequency Trading And Modeling In Finance written by Ionut Florescu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-05 with Business & Economics categories.
Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.
Multivariate High Frequency Based Volatility Heavy Models
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Author : Diaa Noureldin
language : en
Publisher:
Release Date : 2011
Multivariate High Frequency Based Volatility Heavy Models written by Diaa Noureldin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
High Frequency Financial Econometrics
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Author : Yacine Aït-Sahalia
language : en
Publisher: Princeton University Press
Release Date : 2014-07-21
High Frequency Financial Econometrics written by Yacine Aït-Sahalia and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-21 with Business & Economics categories.
A comprehensive introduction to the statistical and econometric methods for analyzing high-frequency financial data High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.
Factor High Frequency Based Volatility Heavy Models
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Author : Kevin Sheppard
language : en
Publisher:
Release Date : 2014
Factor High Frequency Based Volatility Heavy Models written by Kevin Sheppard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Topics In Modeling Volatility Based On High Frequency Data
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Author : Constantin A. Roth
language : en
Publisher:
Release Date : 2018
Topics In Modeling Volatility Based On High Frequency Data written by Constantin A. Roth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
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.
High And Low Frequency Exchange Rate Volatility Dynamics
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Author : Sassan Alizadeh
language : en
Publisher:
Release Date : 2001
High And Low Frequency Exchange Rate Volatility Dynamics written by Sassan Alizadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Economics categories.
We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that the range is not only a highly efficient volatility proxy, but also that it is approximately Gaussian and robust to microstructure noise. The good properties of the range imply that range-based Gaussian quasi-maximum likelihood estimation produces simple and highly efficient estimates of stochastic volatility models and extractions of latent volatility series. We use our method to examine the dynamics of daily exchange rate volatility and discover that traditional one-factor models are inadequate for describing simultaneously the high- and low-frequency dynamics of volatility. Instead, the evidence points strongly toward two-factor models with one highly persistent factor and one quickly mean-reverting factor.
Model Based Measurement Of Actual Volatility In High Frequency Data
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Author : B. Jungbacker
language : en
Publisher:
Release Date : 2005
Model Based Measurement Of Actual Volatility In High Frequency Data written by B. Jungbacker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Duration And Volatility Models For Stock Market Data
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Author :
language : pt-BR
Publisher:
Release Date : 2004
Duration And Volatility Models For Stock Market Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
O presente trabalho visa generalizar a modelagem do tempo entre os negócios ocorridos no mercado financeiro, doravante chamado duração, e estudar os impactos destas duraçõoes sobre a volatilidade instântanea. O estudo foi realizado por meio do modelo linear ACD (autoregression conditional duration) proposto por Engel & Russel[3], os quais usaram a distribuição Exponencial e Weibull para as inovações, e o modelo GARCH-t para dados com alta freqüência para modelar a volatilidade instântanea, também usando a proposição de Engel & Russel[3]. A generalização faz uso da Gama Generalizada proposta em Zhang, Russel & Tsay[9] em um modelo de duração não linear conhecido como TACD (threshold autoregressive conditional duration). A justificativa para o estudo das durações com a GamaGeneralizada é obter uma modelo mais flexível que o proposto por Engel & Russel[3]. Os resultados do modelo ACD com as inovações seguindo uma Gama Generalizada se mostrou mais adequado capturando a sub-dispersão dos dados. A seguir estimamos o modelo de volatilidade instantânea usando as durações estimadas como variáveis explicativas encontrando resultados compatíveis com a literatura.
Topics In Modeling Volatility Based On High Frequency Data
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Author : Constantin Roth
language : en
Publisher:
Release Date : 2018
Topics In Modeling Volatility Based On High Frequency Data written by Constantin Roth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
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.