Measuring Tail Risks

TL;DR

This paper introduces MPMR, a new tail risk measure that depends on the time frame and scales with data size, providing a robust way to estimate long-term risks without needing confidence levels.

Contribution

The paper proposes MPMR, a novel tail risk measure that scales with observation length and offers a new method for estimating tail indices in risk management.

Findings

MPMR scales with the number of observations as a power law.

MPMR provides a scale-invariant estimate of tail risk.

The method applies to financial markets and natural hazard risks.

Abstract

Value at risk (VaR) and expected shortfall (ES) are common high quantile-based risk measures adopted in financial regulations and risk management. In this paper, we propose a tail risk measure based on the most probable maximum size of risk events (MPMR) that can occur over a length of time. MPMR underscores the dependence of the tail risk on the risk management time frame. Unlike VaR and ES, MPMR does not require specifying a confidence level. We derive the risk measure analytically for several well-known distributions. In particular, for the case where the size of the risk event follows a power law or Pareto distribution, we show that MPMR also scales with the number of observations $n$ (or equivalently the length of the time interval) by a power law, $\text{MPMR}(n) \propto n^{\eta}$, where $\eta$ is the scaling exponent. The scale invariance allows for reasonable estimations of long-term risks based on the extrapolation of more reliable estimations of short-term risks. The scaling relationship also gives rise to a robust and low-bias estimator of the tail index (TI) $\xi$ of the size distribution, $\xi = 1/\eta$. We demonstrate the use of this risk measure for describing the tail risks in financial markets as well as the risks associated with natural hazards (earthquakes, tsunamis, and excessive rainfall).