Microstructure Noise in the Continuous Case: The Pre-Averaging Approach ∗
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TLDR
In this article, a generalized pre-averaging approach for estimating the integrated volatility is presented, which can generate rate optimal estimators with convergence rate n 1/4. But the convergence rate is not guaranteed.About:
This article is published in Stochastic Processes and their Applications.The article was published on 2009-07-01 and is currently open access. It has received 525 citations till now. The article focuses on the topics: Stochastic volatility & Estimator.read more
Citations
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The Informational Content of High-Frequency Option Prices
TL;DR: In this article, the option realized variance is used as an observable variable to summarize the information from high-frequency option data, aggregating intraday option returns from mid-quote prices.
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A Generalized Heterogeneous Autoregressive Model using the Market Index
TL;DR: In this article, the authors show that generalizing the heterogeneous autoregressive model (HAR) with realized (co)variances and semi-variancies from the index leads to more accurate volatility forecasts, which translates into economic gains such that a risk-averse investor is willing to pay up to 57 annual basis points by adopting a model specification that utilizes the index information.
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Realized Laplace transforms for pure jump semimartingales with presence of microstructure noise
Li Wang,Zhi Liu,Xiaochao Xia +2 more
TL;DR: An efficient estimator for the integrated Laplace transform of volatility via applying the pre-averaging method is proposed and under some mild conditions on the Lévy density, the asymptotic properties of the estimator including consistency and asymPTotic normality are established.
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A Descriptive Study of High-Frequency Trade and Quote Option Data
Torben G. Andersen,Ilya Archakov,Leon Eric Grund,Nikolaus Hautsch,Yifan Li,Sergey Nasekin,Ingmar Nolte,Manh Cuong Pham,Stephen Taylor,Viktor Todorov +9 more
TL;DR: In this paper, the authors provide a comprehensive overview of the U.S. option market, including details on market regulation and the trading processes for all 16 constituent option exchanges, and illustrate the usefulness of the high-frequency option data with two empirical applications: optionimplied variance estimation and risk-neutral density estimation.
Posted Content
Volatility Forecasting when the Noise Variance Is Time-Varying
Selma Chaker,Nour Meddahi +1 more
TL;DR: In this paper, the authors explore the volatility forecasting implications of a model in which the friction in high-frequency prices is related to the true underlying volatility, and propose a framework under which the realized variance may improve volatility forecasting if the noise variance is related with the true return volatility.
References
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Journal ArticleDOI
A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
TL;DR: In this paper, a closed-form solution for the price of a European call option on an asset with stochastic volatility is derived based on characteristi c functions and can be applied to other problems.
Book
Limit Theorems for Stochastic Processes
Jean Jacod,Albert N. Shiryaev +1 more
TL;DR: In this article, the General Theory of Stochastic Processes, Semimartingales, and Stochastically Integrals is discussed and the convergence of Processes with Independent Increments is discussed.
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Answering the skeptics: yes, standard volatility models do provide accurate forecasts*
TL;DR: In this article, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models and it has been shown that volatility models produce strikingly accurate inter-daily forecasts for the latent volatility factor that would be of interest in most financial applications.
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Modeling and forecasting realized volatility
TL;DR: In this article, the authors provide a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions.
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A Simple Implicit Measure of the Effective Bid‐Ask Spread in an Efficient Market
TL;DR: In this article, the effective bid-ask spread is measured by Spread = 2−cov where cov is the first-order serial covariance of price changes, and is shown empirically to be closely related to firm size.