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on Risk Management |
| By: | Motte, Edouard (Université catholique de Louvain, LIDAM/ISBA, Belgium); Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | This paper investigates the hedging of equity-linked life insurance portfolio for loss-averse insurers. We consider a general arbitrage-free financial market and an actuarial market composed of n−independent policyholders. As the combined market is incomplete, perfect hedging of any actuarial-financial payoff is not possible. Instead, we study the efficient hedging of n−size equity-linked life insurance portfolio for insurers who are only concerned with their losses. To this end, we consider stochastic control problems (under the real-world measure) in order to determine the optimal hedging strategies that either maximize the probability of successful hedge (called quantile hedging) or minimize the expectation for a class of shortfall loss functions (called shortfall hedging). We show that the optimal strategies depend both on actuarial and financial risks. Moreover, these strategies adapt not only to the size of the insurance portfolio but also to the risk-aversion of the insurer. Under the additional assumption of complete financial market, we derive the explicit forms of the optimal hedging strategies. The numerical results show that, for loss-averse insurers, the optimal strategies outperform the optimal mean-variance hedging strategy, demonstrating the relevance of adopting the optimal strategy according to the insurers' risk aversion and portfolio size. |
| Keywords: | Efficient hedging ; Super hedging ; Incomplete market ; Insurance portfolio ; Loss-averse |
| Date: | 2024–04–15 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024013 |
| By: | William C. Dudley (Princeton University) |
| JEL: | D82 E32 E44 G21 G28 G32 L25 |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:pri:cepsud:329 |
| By: | Jorge Abad (BANCO DE ESPAÑA); David Martínez-Miera (UC3M AND CEPR); Javier Suárez (CEMFI AND CEPR) |
| Abstract: | We study banks’ systemic risk-taking decisions in a dynamic general equilibrium model, highlighting the macroprudential role of bank capital requirements. Banks decide on their unobservable exposure to systemic shocks by balancing risk-shifting gains against the value of preserving their capital after such shocks. Capital requirements reduce systemic risk taking, but at the cost of reducing credit and output in calm times, generating welfare trade-offs. We find that systemic risk taking is maximal after long periods of calm and may worsen if capital requirements are countercyclically adjusted. Removing deposit insurance introduces market discipline but increases the bank capital necessary to support credit, implies lower (though far from zero) optimal capital requirements and has nuanced social welfare effects. |
| Keywords: | capital requirements, risk shifting, deposit insurance, systemic risk, financial crises, macroprudential policies |
| JEL: | G01 G21 G28 E44 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2441 |
| By: | Allen, Sam (ETH Zurich); Koh, Jonathan (University of Bern); Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium); Ziegel, Johanna (ETH Zurich) |
| Abstract: | Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we introduce a general notion of tail calibration for probabilistic forecasts, which allows forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between tail calibration and standard notions of forecast calibration, and discuss connections to peaks-over-threshold models in extreme value theory. Diagnostic tools are introduced and applied in a case study on European precipitation forecasts. |
| Keywords: | Extreme event ; proper scoring rule ; forecast evaluation ; tail calibration diagnostic plot ; precipitation forecast |
| Date: | 2024–07–04 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024018 |
| By: | Aya Nasreddine; Yasmine Essafi Zouari |
| Abstract: | Using the wavelet quantile correlation (WQC) methodology, we measure the suitability of gold, silver, oil, stocks as well as the French and the G7 countries indirect real estate to hedge against global and energy inflation. The WQC allows us to deal with time-varying characteristics of time series and to capture tail dependence. Besides, it has the advantage of dissolving the correlation structure between asset returns and inflation across different timescales, enabling us to consider different investment horizons. Recorded results over the 2000-2023 period show that the response to inflationary pressures varies according to the asset class, the holding period as well as the type of inflation considered. Whereas precious metals seem to be suitable over short term maturities, French listed real estate displays interesting inflation hedging features as the investment horizon lengthens. Oil emerges as an equivocal hedge against both global and energy inflation. |
| Keywords: | Indirect real estate; Inflation Hedging; Investment horizon; Wavelet quantile correlation |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-092 |
| By: | Montes-Galdón, Carlos; Ajevskis, Viktors; Brázdik, František; Garcia, Pablo; Gatt, William; Lima, Diana; Mavromatis, Kostas; Ortega, Eva; Papadopoulou, Niki; De Lorenzo, Ivan; Kolb, Benedikt |
| Abstract: | Understanding asymmetric risks in macroeconomic variables is challenging. Most structural models used for policy analysis are linearised and therefore cannot generate asymmetries such as those documented in the empirical growth-at-risk (GaR) literature. This report examines how structural models can incorporate non-linearities to generate tail risks. The first part reviews the various extensions to dynamic stochastic general equilibrium (DSGE) models and the computational challenges involved in accounting for risk distributions. This includes the use of occasionally binding constraints and more recent developments, such as deep learning, to solve non-linear versions of DSGEs. The second part shows how the New Keynesian DSGE model, augmented with the vulnerability channel as proposed by Adrian et al. (2020a, b), satisfactorily replicates key empirical facts from the GaR literature for the euro area. Furthermore, introducing a vulnerability channel into an open-economy set-up and a medium-sized DSGE highlights the importance of foreign financial shocks and financial frictions, respectively. Other non-linearities arising from financial frictions are also addressed, such as borrowing constraints that are conditional on an asset’s value, and the way macroprudential policies acting against those constraints can help stabilise the economy and generate positive spillovers to monetary policy. Finally, the report examines how other types of tail risk beyond financial frictions – such as the recent asymmetric supply-side shocks – can be incorporated into macroeconomic models used for policy analysis. JEL Classification: E70, D50, G10, G12, E52 |
| Keywords: | asymmetric shocks, DSGE, macroprudential policies, non-linearities, structural models, tail risks, vulnerability channel |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbops:2024357 |
| By: | Gaurab Aryal; Isabelle Perrigne; Quang Vuong; Haiqing Xu |
| Abstract: | In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurers choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification strategy exploits the observed number of claims as well as an exclusion restriction, and a full support assumption. Furthermore, our results apply to any form of competition. We propose a novel estimation procedure combining nonparametric estimators and GMM estimation that we illustrate in a Monte Carlo study. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.08416 |
| By: | Müller, Lukas; Stöckl, Sebastian; Müller, Johanna; Schiereck, Dirk |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:150247 |
| By: | Bauwens, Luc (Université catholique de Louvain, LIDAM/CORE, Belgium); Dzuverovic, Emilija (Universita di Pisa); Hafner, Christian (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | We introduce asymmetric effects in the BEKK-type conditional autoregressive Wishart model for realized covariance matrices. The asymmetry terms are specified either by interacting the lagged realized covariances with the signs of the lagged daily returns or by using the decomposition of the lagged realized covariance matrix into positive, negative, and mixed semi-covariances, thus relying on the lagged intra-daily returns and their signs. We provide a detailed comparison of models with different complexity, for example with respect to restrictions on the parameter matrices. In an extensive empirical study, our results suggest that the asymmetric models outperform the symmetric one in terms of statistical and economic criteria. The asymmetric models using the signs of the daily returns tend to have a better in-sample fit and out-of-sample predictive ability than the models using the signed intra-daily returns. |
| Keywords: | High frequency data ; asymmetric volatility ; realized covariance ; conditional autoregressive Wishart model |
| Date: | 2024–10–08 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024022 |
| By: | Heifetz, Stephen |
| Abstract: | As the U.S. government has become increasingly intolerant of national security risks sometimes attendant to foreign investment, Stephen Heifetz suggests the government should consider the costs of that risk avoidance and should alter its approach. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:colfdi:303496 |
| By: | Nigar Karimova |
| Abstract: | The research investigates how the application of a machine-learning random forest model improves the accuracy and precision of a Delphi model. The context of the research is Azerbaijani SMEs and the data for the study has been obtained from a financial institution which had gathered it from the enterprises (as there is no public data on local SMEs, it was not practical to verify the data independently). The research used accuracy, precision, recall and F-1 scores for both models to compare them and run the algorithms in Python. The findings showed that accuracy, precision, recall and F- 1 all improve considerably (from 0.69 to 0.83, from 0.65 to 0.81, from 0.56 to 0.77 and from 0.58 to 0.79, respectively). The implications are that by applying AI models in credit risk modeling, financial institutions can improve the accuracy of identifying potential defaulters which would reduce their credit risk. In addition, an unfair rejection of credit access for SMEs would also go down having a significant contribution to an economic growth in the economy. Finally, such ethical issues as transparency of algorithms and biases in historical data should be taken on board while making decisions based on AI algorithms in order to reduce mechanical dependence on algorithms that cannot be justified in practice. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.05330 |