Logotipo NIPE

Eventos

Seminars

Dynamic Autoregressive Liquidity (DArLiQ)

Guest speaker

Oliver Linton (U. Cambridge)

Local

Room 0.03/DST

Date

Start13.07.2023 11:00End13.07.2023 12:00

Event summary

Bio: 

Oliver Linton is the Professor of Political Economy in the Faculty of Economics and a Fellow of Trinity College, University of Cambridge. Previously, he was Professor of Econometrics at LSE. He is a Fellow of the British Academy, a Fellow of the Econometric Society, and a Fellow of the Institute of Mathematical Statistics. Oliver’s research interests have mostly been to do with nonparametric and semiparametric methods, and he is also interested in Financial Econometrics. He was Co-Editor of the Journal of Econometrics, Econometric Theory, and the Econometrics Journal; and Associate Editor of Econometrica. Oliver has published, among several other journals, in Econometrica, Journal of Econometrics, Econometric Theory, and The Review of Economic Studies. He is the author of two textbooks.

Abstract

We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator based on conditional moment restrictions and an efficient semiparametric ML estimator based on an i.i.d. assumption. We derive large sample properties for our estimators. We further develop a methodology to detect the occurrence of permanent and transitory breaks in the illiquidity process. Finally, we demonstrate the model performance and its empirical relevance on two applications. First, we study the impact of stock splits on the illiquidity dynamics of the five largest US technology company stocks. Second, we investigate how the different components of the illiquidity process obtained from our model relate to the stock market risk premium using data on the S&P 500 stock market index.

Join the NIPE seminars on Google calendar: https://bit.ly/2LKkPyV