Volatility Analysis With Unified Discrete And Continuous Time Models By Combining Low Frequency High Frequency And Option Data
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| Author | : Xinyu Song |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
In this dissertation, we present the topic on volatility analysis with combined discrete-time and continuous-time models by employing low-frequency, high-frequency and option data. We first investigate the traditional low-frequency approach for volatility analysis that frequently adopts generalized autoregressive conditional heteroscedastic (GARCH) type models and modern high-frequency approach for volatility estimation that often employs realized volatility type estimators, examples include multi-scale realized volatility estimators, pre-averaging realized volatility estimators and kernel realized volatility estimators. We introduce a new model for volatility analysis by combining low-frequency and high-frequency approaches. The proposed model is an Ito diffusion process where the instantaneous volatility depends on integrated volatility and squared log return. When the model is restricted to integer times, conditional volatility of the process adopts an analogous structure with the one seen in a standard GARCH model and includes one additional innovation: the integrated volatility. The proposed model is named as generalized unified GARCH-Ito model. Parameter estimation is built on the marriage of a quasi-likelihood function obtained based on conditional volatility structure from the proposed model and common realized volatility estimators obtained based on high-frequency financial data. To improve the performance of proposed estimators, we also provide the option of incorporating option data by adopting a joint quasi-likelihood function. We study the asymptotic behaviors of proposed estimators and conduct a simulation study that confirms proposed estimators have good finite sample statistical performance. An empirical study has been carried out to demonstrate the ease of implementation of the proposed model in daily volatility estimation.
| Author | : |
| Publisher | : |
| Total Pages | : 69 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Modeling and estimating volatility plays a crucial role in financial practice. Devoted efforts are made to investigate this topic using both low-frequency and high-frequency financial data. Traditionally, volatility modeling and analysis are based on either historical price data or option data. Finance theory shows that option prices heavily depend on the underlying stocks' prices, and thus the two kinds of data are related. This thesis explores the approach that combines both stock price data and option data to perform the statistical analysis of volatility. We investigate the Black-Scholes model and an exponential GARCH model and derive the relationship among the Fisher information for volatility estimation based on stock price data alone or option data alone as well as joint volatility estimation for combining stock price data and option data. Under the Block-Scholes model, asymptotic theory for the joint estimation is established, and a simulation study was conducted to check finite sample performances of the proposed joint estimator. Being more accessible than ever, high-frequency data have provided researchers and practitioners with incredible tools to investigate assets pricing and market dynamics. Non-synchronous observations, microstructure noise, and complex pricing models are challenges coming along with high-frequency data. Moreover, large volatility matrix estimation is involved in many finance practices and encounters "curse of dimensionality". Although it is widely used in large covariance estimation, imposing sparsity assumption on the entire volatility matrix is not reasonable in financial practice. In fact, due to the existence of common factors, assets are widely correlated with each other and their volatility matrix is not sparse. In this thesis, we focus on incorporating the factor influence in asset price modeling and volatility matrix estimation. We propose to model asset price using a factor-based diffusion process. The idea is that assets' prices are governed by a common factor, and that assets with similar characteristics share the same association with the factor. Under the proposed factor-based model, we developed an estimation scheme called "Blocking and Regularizing", which deals with all of the four changeless. The asymptotic properties of the proposed estimator are studied, while its finite sample performance is tested via extensive numerical studies to support theoretical results.
| Author | : Luc Bauwens |
| Publisher | : John Wiley & Sons |
| Total Pages | : 566 |
| Release | : 2012-03-22 |
| Genre | : Business & Economics |
| ISBN | : 1118272056 |
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.
| Author | : Constantin Roth |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
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 | : Ser-Huang Poon |
| Publisher | : John Wiley & Sons |
| Total Pages | : 236 |
| Release | : 2005-08-19 |
| Genre | : Business & Economics |
| ISBN | : 0470856157 |
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 | : Laurent E. Calvet |
| Publisher | : Academic Press |
| Total Pages | : 273 |
| Release | : 2008-10-13 |
| Genre | : Business & Economics |
| ISBN | : 0080559964 |
Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and mathematics and provides a unified treatment of the use of multifractal techniques in finance. A large existing literature (e.g., Engle, 1982; Rossi, 1995) models volatility as an average of past shocks, possibly with a noise component. This approach often has difficulty capturing sharp discontinuities and large changes in financial volatility. Their research has shown the advantages of modelling volatility as subject to abrupt regime changes of heterogeneous durations. Using the intuition that some economic phenomena are long-lasting while others are more transient, they permit regimes to have varying degrees of persistence. By drawing on insights from the use of multifractals in the natural sciences and mathematics, they show how to construct high-dimensional regime-switching models that are easy to estimate, and substantially outperform some of the best traditional forecasting models such as GARCH. The goal of Multifractal Volatility is to popularize the approach by presenting these exciting new developments to a wider audience. They emphasize both theoretical and empirical applications, beginning with a style that is easily accessible and intuitive in early chapters, and extending to the most rigorous continuous-time and equilibrium pricing formulations in final chapters. - Presents a powerful new technique for forecasting volatility - Leads the reader intuitively from existing volatility techniques to the frontier of research in this field by top scholars at major universities - The first comprehensive book on multifractal techniques in finance, a cutting-edge field of research
| Author | : Kin Keung Lai |
| Publisher | : Routledge |
| Total Pages | : 113 |
| Release | : 2013-09-11 |
| Genre | : Business & Economics |
| ISBN | : 1135006989 |
This book provides different financial models based on options to predict underlying asset price and design the risk hedging strategies. Authors of the book have made theoretical innovation to these models to enable the models to be applicable to real market. The book also introduces risk management and hedging strategies based on different criterions. These strategies provide practical guide for real option trading. This book studies the classical stochastic volatility and deterministic volatility models. For the former, the classical Heston model is integrated with volatility term structure. The correlation of Heston model is considered to be variable. For the latter, the local volatility model is improved from experience of financial practice. The improved local volatility surface is then used for price forecasting. VaR and CVaR are employed as standard criterions for risk management. The options trading strategies are also designed combining different types of options and they have been proven to be profitable in real market. This book is a combination of theory and practice. Users will find the applications of these financial models in real market to be effective and efficient.
| Author | : Torben Gustav Andersen |
| Publisher | : |
| Total Pages | : 84 |
| Release | : 2002 |
| Genre | : Securities |
| ISBN | : |
Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuous-time, frictionless, no-arbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the ex-post sample-path return variability over a fixed time interval, (ii) the ex-ante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discrete-time ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete- and continuous-time stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recently-popularized realized volatility measures for (non-trivial) fixed-length time intervals.
| Author | : Peter Eric Rossi |
| Publisher | : |
| Total Pages | : 485 |
| Release | : 1996 |
| Genre | : Business & Economics |
| ISBN | : 9780125982757 |
Finance professionals in government and in the trading and investment banking industry use time models to provide necessary data for pricing options and related securities. This volume provides a wealth of practical guidance for these professionals to successfully implement continuous-time models.
| Author | : Alireza Javaheri |
| Publisher | : John Wiley & Sons |
| Total Pages | : 323 |
| Release | : 2015-07-27 |
| Genre | : Business & Economics |
| ISBN | : 1118943996 |
A new, more accurate take on the classical approach to volatility evaluation Inside Volatility Filtering presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of "filtering", this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author's statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You'll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit. Financial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it's not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit. Base volatility estimations on more accurate data Integrate past observation with Bayesian probability Exploit posterior distribution of the hidden state for optimal estimation Boost trade profitability by utilizing "skewness" opportunities Wall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. Inside Volatility Filtering shows you a better way to approach non-normal distributions for more accurate volatility estimation.
