An Economic Evaluation of Model Risk in Long‐term Asset Allocations

Abstract

Following the recent crisis and the revealed weakness of risk management practices, regulators of developed markets have recommended that financial institutions assess model risk. Standard risk measures, such as the value-at-risk (VaR), emerged during the 1990s as the industry standard for risk management and become today a key tool for asset allocation. This paper illustrates and estimates model risk, and focuses on the evaluation of its impact on optimal portfolios at various time horizons. Based on a long sample of US data, the paper finds a non-linear relation between VaR model errors and the horizon that impacts optimal asset allocations.

Monte carlo within simulated annealing for integral constrained optimizations

“…In the this case, efficient results are obtained. We also complement this later result using the dataset used in Boucher et al (2013) for the sake of robustness and show the impact of some potential estimation errors on the estimated frontiers. Our results finally show that the two proposed algorithms quickly converge to practical optimal solutions, within a decent and operational computational time for risk and asset managers.…”

“…In the second exercize dedicated to Mean-Expected Shortfall optimization, we consider the asset classes used in the long-term global asset allocation with model risk studied in Boucher et al (2013). The asset classes are represented by cash, stock and bond markets.…”

“…We followBoucher et al (2013) to build up our database: the "Equity" asset class is represented by a composite index of 95% of the MSCI Europe and 5% of the MSCI World; the "Bonds" asset class comes from the x-trackers II iBoxx Sovereigns Eurozone AAA UCITS 1C index (denoted as XBAT index); the "Money Market" is represented by the Euro Overnight Index Average (EONIA).…”