[PDF] Volatility models | Semantic ScholarWith an OverDrive account, you can save your favorite libraries for at-a-glance information about availability. Find out more about OverDrive accounts. 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.
Range Volatility: A Review of Models and Empirical Studies
Econometric Reviews, - Constant conditional correlation in a bivariate garch model: Evidence from the stock market in China, E. Ruiz. Amsterdam: North-Holland.
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Estimation of Multivariate Stochastic Volatility Models: A Comparative Monte Carlo Study
Multivariate Stochastic Volatility: An Overview. Improving the Parkinson method of estimating security price volatilities. Journal of Futures Markets, 43- Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in.
Handbook of Financial Econometrics and Statistics pp Cite as. The literature on range volatility modeling has been rapidly expanding due to its importance and applications. This chapter provides alternative price range estimators and discusses their empirical properties and limitations. Besides, we review some relevant financial applications for range volatility, such as value-at-risk estimation, hedge, spillover effect, portfolio management, and microstructure issues. In this chapter, we survey the significant development of range-based volatility models, beginning with the simple random walk model up to the conditional autoregressive range CARR model. For the extension to range-based multivariate volatilities, some approaches developed recently are adopted, such as the dynamic conditional correlation DCC model, the double smooth transition conditional correlation DSTCC GARCH model, and the copula method. At last, we introduce different approaches to build bias-adjusted realized range to obtain a more efficient estimator.