Profit and loss attribution: An empirical study

TL;DR

This paper empirically compares three profit and loss attribution methods, revealing limitations of traditional approaches and recommending the average sequential updating method for more accurate risk factor attribution in financial portfolios.

Contribution

The study provides an empirical evaluation of P&L decomposition methods, highlighting the advantages of the ASU approach over OAT and SU, especially in practical risk management.

Findings

OAT can produce significant unexplained P&L.

SU depends heavily on risk factor order and labeling.

ASU provides a more reliable attribution method.

Abstract

The profit and loss (p&l) attrition for each business year into different risk or risk factors (e.g., interest rates, credit spreads, foreign exchange rate etc.) is a regulatory requirement, e.g., under Solvency 2. Three different decomposition principles are prevalent: one-at-a-time (OAT), sequential updating (SU) and average sequential updating (ASU) decompositions. In this research, using financial market data from 2003 to 2022, we demonstrate that the OAT decomposition can generate significant unexplained p&l and that the SU decompositions depends significantly on the order or labeling of the risk factors. On the basis of an investment in a foreign stock, we further explain that the SU decomposition is not able to identify all relevant risk factors. This potentially effects the hedging strategy of the portfolio manager. In conclusion, we suggest to use the ASU decomposition in practice.