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, jia li duke.

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

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.

Chair of junior mid-term review committee: In the first step, we nonparametrically recover the volatility path from high-frequency asset returns.

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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.

Jia li duke

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. To increase the military power of the state, he expanded his army into 2 troops, each having 10, men some say 12, Both women were favored by Duke Xian of Jin.

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Learn more Got it. Merged citations. We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. New articles by this author. Email address for updates. Try again later. Their combined citations are counted only for the first article. Visiting Professor Spring. 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. In this context, only segments of data around a few outlying observations are informative for the inference. Elsevier - Digital Commons. Page details.

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Co-Editor, Econometric Theory Search this site. Hack Professor, Princeton University Verified email at princeton. Google Sites. Email address for updates. Econometrics; Financial Economics; Macroeconomics. Information about your use of this site is shared with Google. The latter are the continuous martingale parts of the processes as well as observation noise. Download CV , updated on Nov 24, In the first step, we nonparametrically recover the volatility path from high-frequency asset returns. The analysis covers both deterministic and random jump arrivals. Articles 1—20 Show more.