Higher-Frequency Analysis of Low-Frequency Data

A QWAFAFEW discussion led by:

Mark Szigety

and

Mickael Mallinger-Dogan

Abstract

Low-frequency data present significant challenges to investors because infrequent observations lead to imperfect intra-period understandings of market behavior, which in turn frustrate risk management, rebalancing, and portfolio monitoring.  Nowhere is this phenomenon more vexing than in portfolios that include both liquid and illiquid investments, whose low-frequency reporting compromises the ability for an investor to form a holistic portfolio perspective.  In this paper, we examine the consequences of this issue and we evaluate two methodologies for interpolating low-frequency data to facilitate use in higher-frequency settings.  Through a numerical study, we determine that the decision to use one methodology over another—or whether to interpolate at all—is context dependent, and we illustrate the impact of both on risk evaluation, attribution, and other common risk measures. 

Bios 

Mark Szigety is Head of Quantitative Risk Analytics at Harvard Management Company.  Mark holds bachelor’s and master’s degrees in Physics from MIT and Harvard, and a doctorate in business administration from Harvard.  His current research focuses on illiquids analytics and management.

Mickael Mallinger-Dogan is a Senior Associate in the Quantitative Risk Analytics Group at Harvard Management Company.  Mickael holds an MSc in Computational Finance from Carnegie Mellon University and an MSc in Banking, Finance and Insurance from Paris Dauphine University.  His current research focuses on illiquid investment modeling and asset allocation.

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