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Full-Text Articles in Social and Behavioral Sciences
Business Time Sampling Scheme With Applications To Testing Semi-Martingale Hypothesis And Estimating Integrated Volatility, Yingjie Dong, Yiu Kuen Tse
Business Time Sampling Scheme With Applications To Testing Semi-Martingale Hypothesis And Estimating Integrated Volatility, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We propose a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data. We compute a time-transformation (TT) function using the intraday integrated volatility estimated by a jump-robust method. The BTS transactions are obtained using the inverse of the TT function. Using our sampled BTS transactions, we test the semi-martingale hypothesis of the stock log-price process and estimate the daily realized volatility. Our method improves the normality approximation of the standardized business-time return distribution. Our Monte Carlo results show that the integrated volatility estimates using our proposed sampling strategy provide smaller root mean-squared error.
Adaptive Estimation Of Continuous-Time Regression Models Using High-Frequency Data, Jia Li, Viktor Todorov, George Tauchen
Adaptive Estimation Of Continuous-Time Regression Models Using High-Frequency Data, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We derive the asymptotic efficiency bound for regular estimates of the slope coefficient in a linear continuous-time regression model for the continuous martingale parts of two Itô semimartingales observed on a fixed time interval with asymptotically shrinking mesh of the observation grid. We further construct an estimator from high-frequency data that achieves this efficiency bound and, indeed, is adaptive to the presence of infinite-dimensional nuisance components. The estimator is formed by taking optimal weighted average of local nonparametric volatility estimates that are constructed over blocks of high-frequency observations. The asymptotic efficiency bound is derived under a Markov assumption for the …
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We study the market microstructure noise-variance estimation of high-frequency stock prices. Based on the Hansen and Lunde (2006) approach, we propose estimates using subsampling method at different time scales. We conduct a Monte Carlo study to compare our method against others in the literature. Our results show that our proposed estimates have lower (absolute) mean error and root mean-squared error, and their performance is quite stable at different time scales.
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the …