Jia li duke

Download CVupdated on Nov 24, School of Economics, Singapore Management University. Visiting Professor Spring.

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.

Jia li duke

Date: March 25 th Wed. Time: pmpm. Location: Building 1, Room , Faculty Lounge. Language: English. We propose a semiparametric two-step inference procedure for a finite-dimensional parameter based on moment conditions constructed from high-frequency data. 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. In the first step, we nonparametrically recover the volatility path from high-frequency asset returns. The nonparametric volatility estimator is then used to form sample moment functions in the second-step GMM estimation, which requires the correction of a high-order nonlinearity bias from the first step. We show that the proposed estimator is consistent and asymptotically mixed Gaussian and propose a consistent estimator for the conditional asymptotic variance. We also construct a Bierens-type consistent specification test. These infill asymptotic results are based on a novel empirical-process-type theory for general integrated functionals of noisy semimartingale processes. About the speaker:. Such data exhibit a microscopic view of asset price behaviors, but also raise new challenges for econometricians. He is currently working on spot variance regressions, volatility occupation times, and forecast evaluation with latent variables.

Robust jump regressions.

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.

He was also the ninth ruler of Jin in the Spring and Autumn period and the second duke of Jin. He reigned for 26 years. During his reign, the State of Jin was one of the most powerful and largest states due to his conquests in many small neighboring states. He is also renowned for the slaughter and exile of many royal family members of Jin and for favoring one of his concubines named Li Ji. When he ascended the throne, Duke Xian of Jin and the duke of Guo visited King Hui of Zhou and they were given rewards which resulted to the increase of their popularity throughout the states. This resulted to the increase of the power of the duke and the loss of political power of the clan of the duke since the clan was almost annihilated.

Jia li duke

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.

Karakura

This site uses cookies from Google to deliver its services and to analyze traffic. Adaptive estimation of continuous-time regression models using high-frequency data J Li, V Todorov, G Tauchen Journal of Econometrics 1 , , Find it in your library. Journal of the American Statistical Association , , About the speaker:. Co-Editor, Econometric Theory Professor January — June Try again later. Econometrics Finance Economics. Information about your use of this site is shared with Google. About Scholar Search help. Econometrics Commons , Economic Theory Commons. Research Areas Econometrics. Research Areas Econometrics. Associate Professor with tenure July — December

James L.

Tim Bollerslev Duke University Verified email at duke. By sampling more frequently the role of these components, which are hidden in the observed price, shrinks asymptotically. Privacy Copyright. Report abuse. About Scholar Search help. 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. Econometrics Finance Economics. Co-Editor, Econometric Theory The latter are the continuous martingale parts of the processes as well as observation noise. Journal of the American Statistical Association , , Copyright Owner and License Authors.