Measuring The Unmeasurable: An Application Of Uncertainty Quantification To Financial Portfolios

Measuring The Unmeasurable: An Application Of Uncertainty Quantification To Financial Portfolios by OFR

Abstract

We extract from the yield curve a new measure of fundamental economic uncertainty, based on McDiarmid's distance and related methods for optimal uncertainty quantification (OUQ). OUQ seeks analytical bounds on a system's behavior, even where the underlying data-generating process and system response function are incompletely specified. We use OUQ to stress test a simple fixed-income portfolio, certifying its safety|i.e., that potential losses will be small" in an appropriate sense. The results give explicit tradeoffs between: scenario count, maximum loss, test horizon, and confidence level. Unfortunately, uncertainty peaks in late 2008, weakening certification assurances just when they are needed most.

Measuring The Unmeasurable: An Application Of Uncertainty Quantification To Financial Portfolios - Introduction

This paper extracts a new measure of fundamental economic uncertainty, based on McDiarmid's distance, from the Treasury yield curve. McDiarmid's distance is a centerpiece of a set of methods for optimal uncertainty quantification (OUQ) recently developed by the engineering community. The OUQ approach seeks analytical bounds on a system's behavior, even where the underlying data-generating process and system response function are not fully specified. We adapt the methods to the problem of stress testing financial portfolios.

Uncertainty plays an important role in economics and finance, where the term traditionally refers to the statistically unmeasurable situation of Knightian uncertainty, where the event space is known but probabilities are not [18]. Full ignorance of the underlying...