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Allied Social Science Associations (ASSA), January 6-8, Chicago, US
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Financial Econometrics, Continuous Time Finance, Statistical Computations, Non-Parametrical Estimations
"Detecting Price Jumps in the Presence of Market Microstructure Noise"" (Job Market Paper)
In this paper we design a test to detect the arrivals of jumps in asset prices contaminated by market microstructure noise. This test is designed by means of the truncated two-scales realized volatility estimator, recently introduced in Brownlees, Nualart, and Sun (2016), which is a robust estimator of the realized volatility in the presence of price jumps and market microstructure noise. We derive the asymptotic value of the power of the test given the significance level, and provide conditions for the test to be consistent. Simulations show that the test performs satisfactorily when the sampling frequency is large. In particular, we show that the test performs better than some prevalent jump tests.
“Realized Networks” with Eulalia Nualart and Christian Brownlees, under review by “Journal of Applied Econometrics” revise and resubmit
We introduce LASSO-type regularization for large dimensional realized covariance estimators of log-prices. The procedure consists of shrinking the off-diagonal entries of the inverse realized covariance matrix towards zero. This technique produces covariance estimators that are positive definite and with a sparse inverse. We name the estimator realized network, since estimating a sparse inverse realized covariance matrix is equivalent to detecting the partial correlation network structure of the daily log-prices. The large sample consistency and selection properties of the estimator are established. An application to a panel of US blue-chips shows the advantages of the estimator for out-of-sample GMV asset allocation.
“A Truncated Two-Scale Realized Variance Estimator”, with Eulalia Nualart and Christian Brownlees, under review by “Journal of the American Statistical Association”
This paper introduces a novel estimator of the integrated volatility of asset prices based on high frequency data that is consistent in the presence of price jumps and market microstructure noise. We begin by introducing a jump signaling indicator based on a local average of intra-daily returns that allows to detect jumps when the price is contaminated by noise. We then combine this technique with the two-scales realized volatility estimator to introduce the so called truncated two-scales realized volatility estimator (TTSRV). We establish consistency of the TTSRV in the presence of finite or infinity activity jumps and noise. In case of finite activity jumps, we also establish the asymptotic distribution of the estimator. A simulation study shows that the TTSRV performs satisfactorily in finite samples and that it out-performs a number of alternative estimators recently proposed in the literature.