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Full-Text Articles in Social and Behavioral Sciences
Optimal Inference For Spot Regressions, Tim Bollerslev, Jia Li, Yuexuan Ren
Optimal Inference For Spot Regressions, Tim Bollerslev, Jia Li, Yuexuan Ren
Research Collection School Of Economics
Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The "local Gaussian" property of the generic continuous-time benchmark model enables optimal "finite-sample" inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures.
Variation And Efficiency Of High-Frequency Betas, Congshan Zhang, Jia Li, Viktor Todorov, George Tauchen
Variation And Efficiency Of High-Frequency Betas, Congshan Zhang, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
This paper studies the efficient estimation of betas from high-frequency return data on a fixed time interval. Under an assumption of equal diffusive and jump betas, we derive the semiparametric efficiency bound for estimating the common beta and develop an adaptive estimator that attains the efficiency bound. We further propose a Hausman type test for deciding whether the common beta assumption is true from the high-frequency data. In our empirical analysis we provide examples of stocks and time periods for which a common market beta assumption appears true and ones for which this is not the case. We further quantify …
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 …