EconPapers    
Economics at your fingertips  
 

Extreme Returns and Intensity of Trading

Gloria Gonzalez-Rivera () and Wei Lin ()
Additional contact information
Wei Lin: Capital University of Economics and Business, China

No 201801, Working Papers from University of California at Riverside, Department of Economics

Abstract: Asymmetric information models of market microstructure claim that variables like trading intensity are proxies for latent information on the value of financial assets. We consider the interval-valued time series (ITS) of low/high returns and explore the relationship between these extreme returns and the intensity of trading. We assume that the returns (or prices) are generated by a latent process with some unknown conditional density. At each period of time, from this density, we have some random draws (trades) and the lowest and highest returns are the realized extreme observations of the latent process over the sample of draws. In this context, we propose a semiparametric model of extreme returns that exploits the results provided by extreme value theory. If properly centered and standardized extremes have well defined limiting distributions, the conditional mean of extreme returns is a highly nonlinear function of conditional moments of the latent process and of the conditional intensity of the process that governs the number of draws. We implement a two-step estimation procedure. First, we estimate parametrically the regressors that will enter into the nonlinear function, and in a second step, we estimate nonparametrically the conditional mean of extreme returns as a function of the generated regressors. Unlike current models for ITS, the proposed semiparametric model is robust to misspecification of the conditional density of the latent process. We fit several nonlinear and linear models to the 5-min and 1-min low/high returns to seven major banks and technology stocks, and find that the nonlinear specification is superior to the current linear models and that the conditional volatility of the latent process and the conditional intensity of the trading process are major drivers of the dynamics of extreme returns.

Keywords: Trading intensity; Interval-valued Time Series; Generalized Extreme Value Distribution; Nonparametric regression; Generated Regressor (search for similar items in EconPapers)
JEL-codes: C01 C14 C32 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-mst
Date: 2017-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://economics.ucr.edu/repec/ucr/wpaper/201801.pdf First version, 2017 (application/pdf)

Related works:
Working Paper: Extreme Returns and Intensity of Trading (2016) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201801

Access Statistics for this paper

More papers in Working Papers from University of California at Riverside, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Kelvin Mac ().

 
Page updated 2019-04-16
Handle: RePEc:ucr:wpaper:201801