Impact of value-at-risk models on market stability

Authored by Barbara Llacay, Gilbert Peffer

Date Published: 2017

DOI: 10.1016/j.jedc.2017.07.002

Sponsors: No sponsors listed

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Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Financial institutions around the world use value-at-risk (VaR) models to manage their market risk and calculate their capital requirements under Basel Accords. VaR models, as any other risk management system, are meant to keep financial institutions out of trouble by, among other things, guiding investment decisions within established risk limits so that the viability of a business is not put unduly at risk in a sharp market downturn. However, some researchers have warned that the widespread use of VaR models creates negative externalities in financial markets, as it can feed market instability and result in what has been called endogenous risk, that is, risk caused and amplified by the system itself, rather than being the result of an exogenous shock. This paper aims at analyzing the potential of VaR systems to amplify market disturbances with an agent-based model of fundamentalist and technical traders which manage their risk with a simple VaR model and must reduce their positions when the risk of their portfolio goes above a given threshold. We analyse the impact of the widespread use of VaR systems on different financial instability indicators and confirm that VaR models may induce a particular price dynamics that rises market volatility. These dynamics, which we have called VaR cycles', take place when a sufficient dumber of traders reach their VaR limit and are forced to simultaneously reduce their portfolio; the reductions cause a sudden price movement, raise volatility and force even more traders to liquidate part of their positions. The model shows that market is more prone to suffer VaR cycles when investors use a short-term horizon to calculate asset volatility or a not-too-extreme value for their risk threshold. (C) 2017 Elsevier B.V. All rights reserved.
Tags
agent-based simulation Price dynamics bubbles financial instability Financial-markets Traders Crashes Value-at-risk Volatility risk limit Volatility window Leverage cycle