High Frequency Volatility of Volatility Estimation Free from Spot Volatility Estimates PDF Download
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Author: Simona Sanfelici
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
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.
Author: Simona Sanfelici
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
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.
Author: Gilles O. Zumbach
Publisher:
ISBN:
Category :
Languages : en
Pages : 22
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Book Description
The limitations of volatilities computed with daily data as well as simple statistical considerations strongly suggest to use intraday data in order to obtain accurate volatility estimates. Under a continuous time arbitrage-free setup, the quadratic variations of the prices would allow us, in principle, to construct an approximately error free estimate of volatility by using data at the highest frequency available. Yet, empirical data at very short time scales differ in many ways from the arbitrage-free continuous time price processes. For foreign exchange rates, the main difference originates in the incoherent structure of the price formation process. This market micro-structure effect introduces a noisy component in the price process leading to a strong overestimation of volatility when using naive estimators. Therefore, to be able to fully exploit the information contained in high frequency data, this incoherent effect needs to be discounted. In this contribution, we investigate several unbiased estimators that take into account the incoherent noise. One approach is to use a filter for pre-whitening the prices, and then using volatility estimators based on the filtered series. Another solution is to directly define a volatility estimator using tick-by-tick price differences, and including a correction term for the price formation effect. The properties of these estimators are investigated by Monte Carlo simulations. A number of important real-world effects are included in the simulated processes: realistic volatility and price dynamic, the incoherent effect, seasonalities, and random arrival time of ticks. Moreover, we investigate the robustness of the estimators with respect to data frequency changes and gaps. Finally, we illustrate the behavior of the best estimators on empirical data.
Author: Yacine Ait-Sahalia
Publisher:
ISBN:
Category :
Languages : en
Pages : 60
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Book Description
We analyze the impact of time series dependence in market microstructure noise on the properties of.
Author: Yifan Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 63
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Book Description
In this paper we examine the relative importance of trading volume, bid-ask spread, order flow, order imbalance, total quote depth, quote depth difference and trading intensity for high-frequency volatility estimation. By using a best subset regression approach, we fi nd that contemporaneous trading intensity and order flow contains the most important information about volatility estimation in general, but the rankings of the importance of the market microstructure (MMS) variables vary between securities. Using a Lognormal Log-Autoregressive Conditional Duration (LL-ACD) model, we show that the inclusion of MMS covariates signi ffcantly improves the goodness-of- fit of the model. Furthermore, we show that the inclusion of MMS covariates in the LL-ACD model leads to substantial improvements in the quality of volatility estimates, both on a daily and an intraday level.
Author:
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ISBN:
Category :
Languages : en
Pages :
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Author: Yucheng Sun
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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: David Schreindorfer
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 164
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Author: Alain P. Chaboud
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ISBN:
Category : Exchange rate pass-through
Languages : en
Pages : 58
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Book Description
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.
Author: Yifan Li
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Carson Drummond
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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Book Description
In this paper we introduce a new way to estimate the spot volatility of high frequency foreign exchange data using the Hilbert-Huang Transform. We also propose and test a consistent spot volatility estimate in the presence of microstructure noise. The problem of assessing the validity of latent variable estimates is overcome by setting up a virtual options trading market in which competing volatility forecasts buy and sell straddle options to one another using real high frequency foreign exchange data.
