Volatility Decomposition And Nonparametric Estimation Of Spot Volatility Of Models With Poisson Sampling Under Market Microstructure Noise
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Volatility Decomposition And Nonparametric Estimation Of Spot Volatility Of Models With Poisson Sampling Under Market Microstructure Noise
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Author : Sophon Tunyavetchakit
language : en
Publisher:
Release Date : 2016
Volatility Decomposition And Nonparametric Estimation Of Spot Volatility Of Models With Poisson Sampling Under Market Microstructure Noise written by Sophon Tunyavetchakit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Liquidity Based Estimation Of Spot Volatility Under Microstructure Noise
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Author : Oliver Grothe
language : en
Publisher:
Release Date : 2010
Liquidity Based Estimation Of Spot Volatility Under Microstructure Noise written by Oliver Grothe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Recent literature on realized volatility suggests that the observed price process of an asset may be decomposed into two parts: the unobservable, efficient price process and microstructure noise. In this article we present a methodology to sequentially estimate spot volatility from noisy data by separating these components. We use different liquidity-based measures, traded volume and quoted spread, for the noise variance of single price observations. Nonlinear Kalman filters provide us with sequential estimates of the unobservable price process and its parameters. Our approach is implemented in a continuous-discrete state space model to cope with irregular trading frequencies.
High Frequency Volatility Of Volatility Estimation Free From Spot Volatility Estimates
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Author : Simona Sanfelici
language : en
Publisher:
Release Date : 2015
High Frequency Volatility Of Volatility Estimation Free From Spot Volatility Estimates written by Simona Sanfelici and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
We define a new consistent estimator of the integrated volatility of volatility based only on a pre-estimation of the Fourier coefficients of the volatility process. We investigate the finite sample properties of the estimator in the presence of noise contamination by computing the bias of the estimator due to noise and showing that it vanishes as the number of observations increases, under suitable assumptions. In both simulated and empirical studies, the performance of the Fourier estimator with high frequency data is investigated and it is shown that the proposed estimator of volatility of volatility is easily implementable, computationally stable and even robust to market microstructure noise.
When Is Noise Not Noise A Microstructure Estimate Of Realized Volatility
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Author : Zheng Sun
language : en
Publisher:
Release Date : 2008
When Is Noise Not Noise A Microstructure Estimate Of Realized Volatility written by Zheng Sun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
This paper studies the joint distribution of tick by tick returns and durations between trades. Returns are decomposed into changes in full information prices and microstructure noise, but the noise is modeled in accordance with various models of market microstructure allowing rich correlation structures both with the efficient price and over time. The full information price has time varying volatility which depends upon the arrival time of trades. The paper aims at three contributions: First, the noise is modeled to allow asymmetric information, inventory and order processing costs, and delayed quote setting. Second, the response to the trade arrival times allows trade durations to be informative on future volatility. Third, the estimated state space models can act as a laboratory to examine various non-parametric approaches to realized volatility estimation. Both simulated and actual data can be compared across methods and the accuracy and efficiency assessed as long as the parameteric model is viewed as a sufficiently accurate representation. We apply the above model to 10 NYSE stock transactions data series with varying transaction rates. It appears that contemporaneous duration has little effect on the volatility per trade after conditioning on the past, which means average per second volatility is inversely related to the duration between trades. Microstructure noise is found to be informative about the unobserved efficient price, and the informational component explains 45% of the total variation of the microstructure noise.
Nonparametric Estimation Of Stochastic Volatility Models
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Author : Steven Cannon Hogan
language : en
Publisher:
Release Date : 2000
Nonparametric Estimation Of Stochastic Volatility Models written by Steven Cannon Hogan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
Nonparametric Estimation In A Stochastic Volatility Model
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Author : Jürgen Franke
language : en
Publisher:
Release Date : 1998
Nonparametric Estimation In A Stochastic Volatility Model written by Jürgen Franke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Nonparametric Estimation Of Large Spot Volatility Matrices For High Frequency Financial Data
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Author : Ruijun Bu
language : en
Publisher:
Release Date : 2022
Nonparametric Estimation Of Large Spot Volatility Matrices For High Frequency Financial Data written by Ruijun Bu 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.
Frequency Of Observation And The Estimation Of Integrated Volatility In Deep And Liquid Financial Markets
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Author : Alain P. Chaboud
language : en
Publisher:
Release Date : 2007
Frequency Of Observation And The Estimation Of Integrated Volatility In Deep And Liquid Financial Markets written by Alain P. Chaboud and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Exchange rate pass-through categories.
Using two newly available ultrahigh-frequency datasets, we investigate empirically how frequently one can sample certain foreign exchange and U.S. Treasury security returns without contaminating estimates of their integrated volatility with market microstructure noise. Using volatility signature plots and a recently-proposed formal decision rule to select the sampling frequency, we find that one can sample FX returns as frequently as once every 15 to 20 seconds without contaminating volatility estimates; bond returns may be sampled as frequently as once every 2 to 3 minutes on days without U.S. macroeconomic announcements, and as frequently as once every 40 seconds on announcement days. With a simple realized kernel estimator, the sampling frequencies can be increased to once every 2 to 5 seconds for FX returns and to about once every 30 to 40 seconds for bond returns. These sampling frequencies, especially in the case of FX returns, are much higher than those often recommended in the empirical literature on realized volatility in equity markets. We suggest that the generally superior depth and liquidity of trading in FX and government bond markets contributes importantly to this difference.
Volatility Forecasting And Microstructure Noise
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Author : Arthur Sinko
language : en
Publisher:
Release Date : 2012
Volatility Forecasting And Microstructure Noise written by Arthur Sinko and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
It is common practice to use the sum of frequently sampled squared returns to estimate volatility, yielding so called realized volatility. Unfortunately, returns are contaminated by market microstructure noise. Several noise-corrected realized volatility measures have been proposed. We assess to what extend correction for microstructure noise improves forecasting future volatility using the MIxed DAta Sampling (MIDAS) framework. We start by studying the population properties of predictions using various realized volatility measures. We do this in a general regression setting and with both i.i.d. as well as depend microstructure noise. Next we study optimal sampling issues theoretically, when the objective is forecasting and microstructure noise contaminates realized volatility. For the volatility measures constructed using five-minute returns, microstructure corrections tend to reduce predictability. The subsampling and averaging class of estimators (Zhang, Mykland, and Aamp;ıt-Sahalia 2005) predicts volatility the best at this frequency. In particular, a new power variation estimator constructed by averaging over subsamples has the best performance. This result reinforces earlier findings of (Ghysels, Santa-Clara, and Valkanov 2006) and Forsberg and Ghysels (2004). Finally, the volatility dynamics are more complicated for one-minute returns and the results are not that clear-cut. Moreover, when we study optimal sampling empirically, we find its implementation hampered by the requirement to estimate fourth order moments.
Frequency Of Observation And The Estimation Of Integrated Volatility In Deep And Liquid Financial Markets
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Author : Alain Chaboud
language : en
Publisher:
Release Date : 2008
Frequency Of Observation And The Estimation Of Integrated Volatility In Deep And Liquid Financial Markets written by Alain Chaboud and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Bond market categories.
Using two newly available ultrahigh-frequency datasets, we investigate empirically how frequently one can sample certain foreign exchange and U.S. Treasury security returns without contaminating estimates of their integrated volatility with market microstructure noise. We find that one can sample FX returns as frequently as once every 15 to 20 seconds without contaminating volatility estimates; bond returns may be sampled as frequently as once every 2 to 3 minutes on days without U.S. macroeconomic announcements, and as frequently as once every 40 seconds on announcement days. With a simple realized kernel estimator, the sampling frequencies can be increased to once every 2 to 5 seconds for FX returns and to about once every 30 to 40 seconds for bond returns. These sampling frequencies, especially in the case of FX returns, are much higher than those often recommended in the empirical literature on realized volatility in equity markets. The higher sampling frequencies for FX and bond returns likely reflects the superior depth and liquidity of these markets.