An MCMC Approach to Multivariate Density Forecasting: An Application to Liquidity

30 Pages Posted: 20 Jan 2011 Last revised: 5 May 2013

See all articles by Fabian Krueger

Fabian Krueger

Heidelberg Institute for Theoretical Studies (HITS) gGmbH

Ingmar Nolte

Lancaster University - Department of Accounting and Finance

Date Written: May 5, 2013

Abstract

We analyze the construction of multivariate forecasting densities based on conditional models for each variable, given the other variables; a joint predictive density is obtained by iteratively simulating from the conditional models. This idea has been pursued in the context of missing data imputation, but is new to the field of econometric forecasting. Its main advantage is that only univariate models for the variables in question are needed as inputs. Within a Monte Carlo study we illustrate the flexibility and robustness of this approach especially for the case of model misspecification. We then consider forecasting the bivariate mixed discrete-continuous distribution of returns and order flows on a high frequency level. This distribution can be related to an ex-post concept of market liquidity. A simulation-based forecasting distribution constructed from the conditional models for returns and order flows is found to outperform a vector autoregressive benchmark for several large-cap US stocks.

Keywords: Multivariate Density Forecasting, Liquidity, Financial Econometrics

JEL Classification: C53, C58

Suggested Citation

Krueger, Fabian and Nolte, Ingmar, An MCMC Approach to Multivariate Density Forecasting: An Application to Liquidity (May 5, 2013). Available at SSRN: https://ssrn.com/abstract=1743707 or http://dx.doi.org/10.2139/ssrn.1743707

Fabian Krueger (Contact Author)

Heidelberg Institute for Theoretical Studies (HITS) gGmbH ( email )

Schloss-Wolfsbrunnenweg 35
Heidelberg, D-69118
Germany

Ingmar Nolte

Lancaster University - Department of Accounting and Finance ( email )

Lancaster, Lancashire LA1 4YX
United Kingdom

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