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on Risk Management |
| By: | Cyril Bénézet (LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - UEVE - Université d'Évry-Val-d'Essonne - ENSIIE - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise); Stéphane Crépey (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité); Dounia Essaket (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité) |
| Abstract: | Darwinian model risk is the risk of mis-price-and-hedge biased toward short-to-medium systematic profits of a trader, which are only the compensator of long term losses becoming apparent under extreme scenarios where the bad model of the trader no longer calibrates to the market. The alpha leakages that characterize Darwinian model risk are undetectable by the usual market risk tools such as value-at-risk, expected shortfall, or stressed value-at-risk. Darwinian model risk can only be seen by simulating the hedging behavior of a bad model within a good model. In this paper we extend to callable assets the notion of hedging valuation adjustment introduced in previous work for quantifying and handling such risk. The mathematics of Darwinian model risk for callable assets are illustrated by exact numerics on a stylized callable range accrual example. Accounting for the wrong hedges and exercise decisions, the magnitude of the hedging valuation adjustment can be several times larger than the mere difference, customarily used in banks as a reserve against model risk, between the trader's price of a callable asset and its fair valuation. |
| Keywords: | Financial derivatives pricing and hedging, Callable asset, Model risk, Model calibration, Hedging Valuation Adjustment (HVA) |
| Date: | 2023–04–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-04057045&r=rmg |
| By: | Jianjun Gao; Yu Lin; Weiping Wu; Ke Zhou |
| Abstract: | This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale representation, we offer a solution framework for these issues and also develop a closed-form portfolio policy when all market parameters are deterministic. Our hybrid model outperforms the classical continuous-time mean-variance portfolio policy by allocating a higher position of the risky asset in favorable market states and a less risky asset in unfavorable market states. This desirable property leads to promising numerical experiment results, including improved Sortino ratio and reduced downside risk compared to the benchmark models. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.15830&r=rmg |
| By: | David Wu; Sebastian Jaimungal |
| Abstract: | The objectives of option hedging/trading extend beyond mere protection against downside risks, with a desire to seek gains also driving agent's strategies. In this study, we showcase the potential of robust risk-aware reinforcement learning (RL) in mitigating the risks associated with path-dependent financial derivatives. We accomplish this by leveraging the Jaimungal, Pesenti, Wang, Tatsat (2022) and their policy gradient approach, which optimises robust risk-aware performance criteria. We specifically apply this methodology to the hedging of barrier options, and highlight how the optimal hedging strategy undergoes distortions as the agent moves from being risk-averse to risk-seeking. As well as how the agent robustifies their strategy. We further investigate the performance of the hedge when the data generating process (DGP) varies from the training DGP, and demonstrate that the robust strategies outperform the non-robust ones. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.15216&r=rmg |
| By: | Armantier, Olivier; Foncel, Jérôme; Treich, Nicolas |
| Abstract: | We study insurance and portfolio decisions, two opposite risk retention tradeoffs. Using household level data, we identify the first joint determinants (e.g. subjective expecta-tions, risk attitude) and frictions (e.g. liquidity constraints, financial literacy) in the literature. We also find key differences between the two decisions. Notably, contrary to economic intuition, risky asset holding and insurance coverage both increase with wealth. We show that this apparent puzzle is driven in part by a specific behavioral pattern (the poor invest too conservatively, while the rich over-insure), and can be explained by two factors: regret avoidance and nonperformance risk. |
| JEL: | D14 D81 C3 |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:128034&r=rmg |
| By: | Zhou, Renee (University of Warwick) |
| Abstract: | To test whether education can change risk preference, I exploit the Indonesian school construction programme and the Mexican education reform in compulsory schooling as two separate natural experiments. Applying the instrumental variable approach, I do not find a causal effect of education on risk preference. The results are consistent in the two different settings, so my findings are externally valid. The results suggest that a change in risk preference may not be the channel via which the impact of education on risk-taking in real life. This paper contributes to the literature on the determinants of social preferences and the outcomes of education. |
| Keywords: | Risk preference ; risk aversion ; education JEL classifications: I25 ; D90 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkesp:45&r=rmg |