Estimating the Variance Parameter from Noisy High Frequency Financial Data (Classic Reprint) PDF Download
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Author: Bin Zhou
Publisher: Forgotten Books
ISBN: 9780332147468
Category :
Languages : en
Pages : 30
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Book Description
Excerpt from Estimating the Variance Parameter From Noisy High Frequency Financial Data I call the diffusion process the signal process and the fit observation noise. The observation noise is the deviation of data from the continuous process and is assumed to be independent from the diffusion process. Many things contribute to this observation noise. In the currency market, for example, non-binding quoting error is part of the noise. In other markets, bid and offer difference also contributes to the observation noise. Many other micro structural behaviors are all included in this so - called observation noise. For low frequency observations, the observation noise is overwhelmed by the sig nal change. When observation frequency increases, the signal change becomes smaller and smaller while the size of the noise remains the same. The noise totally dominates the price change in ultra-high frequency data. Viewing high frequency data as observation with noise certainly captures many basic characteristics of high frequency financial time series mentioned above. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Author: Bin Zhou
Publisher: Forgotten Books
ISBN: 9780332147468
Category :
Languages : en
Pages : 30
Get Book Here
Book Description
Excerpt from Estimating the Variance Parameter From Noisy High Frequency Financial Data I call the diffusion process the signal process and the fit observation noise. The observation noise is the deviation of data from the continuous process and is assumed to be independent from the diffusion process. Many things contribute to this observation noise. In the currency market, for example, non-binding quoting error is part of the noise. In other markets, bid and offer difference also contributes to the observation noise. Many other micro structural behaviors are all included in this so - called observation noise. For low frequency observations, the observation noise is overwhelmed by the sig nal change. When observation frequency increases, the signal change becomes smaller and smaller while the size of the noise remains the same. The noise totally dominates the price change in ultra-high frequency data. Viewing high frequency data as observation with noise certainly captures many basic characteristics of high frequency financial time series mentioned above. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Author: Bin Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
Author: BIN. ZHOU
Publisher:
ISBN: 9781033587287
Category :
Languages : en
Pages : 0
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Book Description
Author: Bin Zhou
Publisher: Forgotten Books
ISBN: 9780332800066
Category :
Languages : en
Pages : 28
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Book Description
Excerpt from Estimating the Covariance Matrix From Unsynchronized High Frequency Financial Data The estimation of the covariance matrix of financial prices is necessary in port folio optimization and risk management. Besides sample covariance, many other estimators have been proposed (stein 1975, Dey and Srinivasan However, estimating the covariance matrix from daily data can have serious problems. Jobson and Korkie (1980) indicated that, in some cases, it is better to use the identical matrix instead of the sample covariance matrix in the port folio selection. The problem is that the number of observations is not enough to estimate all entries of a big covariance matrix. To get around the problem, one may want to collect more data over longer time interval. However, the changing condition of markets may prevent us to do so. Another approach is to impose constrains on the covariance matrix to reduce the number of free parameters (frost and Savaino, The constrain may be subjective and not reflect the reality of the market. This paper explores another possibility of using high frequency data. Because of fast-growing computer power, data is now available in ultra - high frequency, such as tick-by - tick. Exchange rates, for example, can easily have over one million observations in one year. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Author: Yacine Aït-Sahalia
Publisher: Princeton University Press
ISBN: 0691161437
Category : Business & Economics
Languages : en
Pages : 683
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Book Description
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.
Author: Yucheng Sun
Publisher:
ISBN:
Category :
Languages : en
Pages : 125
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Book Description
Based on high-frequency price data, this thesis focuses on estimating the realized covariance and the integrated volatility of asset prices, and applying volatility estimation to price jump detection. The first chapter uses the LASSO procedure to regularize some estimators of high dimensional realized covariance matrices. We establish theoretical properties of the regularized estimators that show its estimation precision and the probability that they correctly reveal the network structure of the assets. The second chapter proposes a novel estimator of the integrated volatility which is the quadratic variation of the continuous part in the price process. This estimator is obtained by truncating the two-scales realized variance estimator. We show its consistency in the presence of market microstructure noise and finite or infinite activity jumps in the price process. The third chapter employs this estimator to design a test to explore the existence of price jumps with noisy price data.
Author: Bin Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
Author: Peter Reinhard Hansen
Publisher:
ISBN:
Category :
Languages : en
Pages : 58
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Book Description
We study market microstructure noise in high-frequency data and analyze its implications for the realized variance (RV) under a general specification for the noise. We show that kernel-based estimators can unearth important characteristics of marketmicrostructure noise and that a simple kernel-based estimator dominates the RV for the estimation of integrated variance (IV). An empirical analysis of the Dow Jones Industrial Average stocks reveals that market microstructure noise is time-dependent and correlated with increments in the efficient price. This has important implications for volatility estimation based on high-frequency data. Finally, we apply cointegration techniques to decompose transaction prices and bid-ask quotes into an estimate of the efficient price and noise. This framework enables us to study the dynamic effects on transaction prices and quotes caused by changes in the efficient price.
Author: David Schreindorfer
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 164
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Book Description
Author: Xin Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This dissertation work focuses on developing statistical methods for volatility estimation and prediction with high frequency financial data. We consider two kinds of volatility: integrated volatility and jump variation. In the first part, we introduce the methods for integrated volatility estimation with the presence of microstructure noise. We will first talk about the optimal sampling frequency for integrated volatility estimation since subsampling is very popular in practice. Then we will discuss about those methods based on subsampling. Two-scale estimator is developed using the subsampling idea while taking advantage of all of the data. An extension to the multi-scale further improves the efficiency of the estimation. In the second part, we propose a heterogenous autoregressive model for the integrated volatility estimators based on subsampling. An empirical approach is to estimate integrated volatility using high frequency data and then fit the estimates to a low frequency heterogeneous autoregressive volatility model for prediction. We provide some theoretical justifications for the empirical approach by showing that these estimators approximately obey a heterogenous autoregressive model for some appropriate underlying price and volatility processes. In the third part, we propose a method for jump variation estimation using wavelet techniques. Previously, jumps are not assumed in the model. In this part, we will concentrate on jump variation estimation and there- fore, we will be able to estimate the integrated volatility and jump variation individually. We show that by choosing a threshold, we will be able to detect the jump location, and by using the realized volatility processes instead of the original price process, we will be able to improve the convergence rate of estimation. We include both numerical and empirical results of this method.
