Chapter 15 Queuing Theoretic Approaches to Financial Price Fluctuations

Erhan Bayraktar, Ulrich Horst, Kaushik Ronnie Sircar

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

One approach to the analysis of stochastic fluctuations in market prices is to model characteristics of investor behavior and the complex interactions between market participants, with the aim of extracting consequences in the aggregate. This agent-based viewpoint in finance goes back at least to the work of Garman [Garman, M. (1976). Market microstructure. Journal of Financial Economics 3, 257-275] and shares the philosophy of statistical mechanics in the physical sciences. We discuss recent developments in market microstructure models. They are capable, often through numerical simulations, to explain many stylized facts like the emergence of herding behavior, volatility clustering and fat tailed returns distributions. They are typically queuing-type models, that is, models of order flows, in contrast to classical economic equilibrium theories of utility-maximizing, rational, "representative" investors. Mathematically, they are analyzed using tools of functional central limit theorems, strong approximations and weak convergence. Our main examples focus on investor inertia, a trait that is well-documented, among other behavioral qualities, and modeled using semi-Markov switching processes. In particular, we show how inertia may lead to the phenomenon of long-range dependence in stock prices.

Original languageEnglish (US)
Pages (from-to)637-677
Number of pages41
JournalHandbooks in Operations Research and Management Science
Volume15
Issue numberC
DOIs
StatePublished - Dec 1 2007

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics
  • Computer Science Applications
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Chapter 15 Queuing Theoretic Approaches to Financial Price Fluctuations'. Together they form a unique fingerprint.

Cite this