Jia li duke

Date: March 25 th Wed. Time: pmpm. Location: Building 1, RoomFaculty Lounge. Language: English.

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 diffusive volatility around the jump times. We derive the asymptotic limit of the estimator, a semiparametric lower efficiency bound for the linear jump regression, and show that our estimator attains the latter. The analysis covers both deterministic and random jump arrivals.

Jia li duke

We develop robust inference methods for studying linear dependence between the jumps of discretely observed processes at high frequency. Unlike classical linear regressions, jump regressions are determined by a small number of jumps occurring over a fixed time interval and the rest of the components of the processes around the jump times. The latter are the continuous martingale parts of the processes as well as observation noise. By sampling more frequently the role of these components, which are hidden in the observed price, shrinks asymptotically. The robustness of our inference procedure is with respect to outliers, which are of particular importance in the current setting of relatively small number of jump observations. This is achieved by using nonsmooth loss functions like L1 in the estimation. Unlike classical robust methods, the limit of the objective function here remains nonsmooth. The proposed method is also robust to measurement error in the observed processes, which is achieved by locally smoothing the high-frequency increments. Robust jump regressions. Journal of the American Statistical Association. Econometrics Commons , Economic Theory Commons. Advanced Search. Privacy Copyright. Skip to main content.

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Download CV , updated on Nov 24, School of Economics, Singapore Management University. Visiting Professor Spring. Department of Economics, Duke University. Professor January — June

In the last three decades, technological innovations, like the adoption of algorithmic trading, have paved the way for many changes in the U. By that I mean: What is the risk of an extreme event, or how much information are in prices in the stock market? His specialties are asset pricing and market structure, specifically as they relate to risk sharing and management. As a high school junior, the economist first became interested in the discipline because it merged his interests in quantitative science and political science and provided a vehicle through which he could understand how the world works. He demonstrated that finance interacts uniquely with the world. Many financial regularities can be evaluated immediately in dollars and cents. His passion just infected me. That interaction is also one of the reasons why Weller is always happy to sit down and talk with students about finance, regardless of their interests. Related Articles.

Jia li duke

Download CV , updated on Nov 24, School of Economics, Singapore Management University. Visiting Professor Spring. Department of Economics, Duke University. Professor January — June Associate Professor with tenure July — December Assistant Professor July — June

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The proposed method is also robust to measurement error in the observed processes, which is achieved by locally smoothing the high-frequency increments. The population moment conditions take the form of temporally integrated functionals of state-variable processes that include the latent stochastic volatility process of an asset. Public access. Copyright Owner and License Publisher. Programs Ph. He is currently working on spot variance regressions, volatility occupation times, and forecast evaluation with latent variables. Econometrics Commons , Economic Theory Commons. These infill asymptotic results are based on a novel empirical-process-type theory for general integrated functionals of noisy semimartingale processes. Publication Journal of the American Statistical Association. The robustness of our inference procedure is with respect to outliers, which are of particular importance in the current setting of relatively small number of jump observations. This "Cited by" count includes citations to the following articles in Scholar. Elsevier - Digital Commons. Verified email at meta.

James L. Meriam Distinguished Professor of Biomedical Engineering. Director, Center for Quantitative Biodesign.

Email: jiali smu. He is currently working on spot variance regressions, volatility occupation times, and forecast evaluation with latent variables. My profile My library Metrics Alerts. Add co-authors Co-authors. Publication Type Journal Article. Time: pmpm Location: Building 1, Room , Faculty Lounge Language: English Abstract: We propose a semiparametric two-step inference procedure for a finite-dimensional parameter based on moment conditions constructed from high-frequency data. Robust jump regressions. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. By sampling more frequently the role of these components, which are hidden in the observed price, shrinks asymptotically. Professor January — June