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on Risk Management |
| By: | E. Ruben van Beesten |
| Abstract: | I propose a functional on the space of spectral risk measures that quantifies their ``degree of risk aversion''. This quantification formalizes the idea that some risk measures are ``more risk-averse'' than others. I construct the functional using two axioms: a normalization on the space of CVaRs and a linearity axiom. I present two formulas for the functional and discuss several properties and interpretations. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.15675 |
| By: | L. Ponta; A. Carbone |
| Abstract: | The Kullback-Leibler cluster entropy $\mathcal{D_{C}}[P \| Q] $ is evaluated for the empirical and model probability distributions $P$ and $Q$ of the clusters formed in the realized volatility time series of five assets (SP\&500, NASDAQ, DJIA, DAX, FTSEMIB). The Kullback-Leibler functional $\mathcal{D_{C}}[P \| Q] $ provides complementary perspectives about the stochastic volatility process compared to the Shannon functional $\mathcal{S_{C}}[P]$. While $\mathcal{D_{C}}[P \| Q] $ is maximum at the short time scales, $\mathcal{S_{C}}[P]$ is maximum at the large time scales leading to complementary optimization criteria tracing back respectively to the maximum and minimum relative entropy evolution principles. The realized volatility is modelled as a time-dependent fractional stochastic process characterized by power-law decaying distributions with positive correlation ($H>1/2$). As a case study, a multiperiod portfolio built on diversity indexes derived from the Kullback-Leibler entropy measure of the realized volatility. The portfolio is robust and exhibits better performances over the horizon periods. A comparison with the portfolio built either according to the uniform distribution or in the framework of the Markowitz theory is also reported. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.10543 |
| By: | Anthony Coache; Sebastian Jaimungal |
| Abstract: | In a reinforcement learning (RL) setting, the agent's optimal strategy heavily depends on her risk preferences and the underlying model dynamics of the training environment. These two aspects influence the agent's ability to make well-informed and time-consistent decisions when facing testing environments. In this work, we devise a framework to solve robust risk-aware RL problems where we simultaneously account for environmental uncertainty and risk with a class of dynamic robust distortion risk measures. Robustness is introduced by considering all models within a Wasserstein ball around a reference model. We estimate such dynamic robust risk measures using neural networks by making use of strictly consistent scoring functions, derive policy gradient formulae using the quantile representation of distortion risk measures, and construct an actor-critic algorithm to solve this class of robust risk-aware RL problems. We demonstrate the performance of our algorithm on a portfolio allocation example. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.10096 |
| By: | John Armstrong; Cristin Buescu; James Dalby |
| Abstract: | We study the optimal investment problem for a homogeneous collective of $n$ individuals investing in a Black-Scholes model subject to longevity risk with Epstein--Zin preferences. %and with preferences given by power utility. We compute analytic formulae for the optimal investment strategy, consumption is in discrete-time and there is no systematic longevity risk. We develop a stylised model of systematic longevity risk in continuous time which allows us to also obtain an analytic solution to the optimal investment problem in this case. We numerically solve the same problem using a continuous-time version of the Cairns--Blake--Dowd model. We apply our results to estimate the potential benefits of pooling longevity risk over purchasing an insurance product such as an annuity, and to estimate the benefits of optimal longevity risk pooling in a small heterogeneous fund. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.15325 |
| By: | Andres Fernandez; Martin Hiti; Asani Sarkar |
| Abstract: | Recent events have heightened awareness of systemic risk stemming from nonbank financial sectors. For example, during the COVID-19 pandemic, liquidity demand from nonbank financial entities caused a “dash for cash” in financial markets that required government support. In this post, we provide a quantitative assessment of systemic risk in the nonbank sectors. Even though these sectors have heterogeneous business models, ranging from insurance to trading and asset management, we find that their systemic risk has common variation, and this commonality has increased over time. Moreover, nonbank sectors tend to become more systemic when banking sector systemic risk increases. |
| Keywords: | nonbank financial institutions (NBFIs); nonbanks; banks; systemic risk; Interconnections |
| JEL: | G01 G21 G22 G23 G24 |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:98893 |
| By: | Zhengyang Chi; Junbin Gao; Chao Wang |
| Abstract: | This study introduces a global stock market volatility forecasting model that enhances forecasting accuracy and practical utility in real-world financial decision-making by integrating dynamic graph structures and encompassing the union of active trading days of different stock markets. The model employs a spatial-temporal graph neural network (GNN) architecture to capture the volatility spillover effect, where shocks in one market spread to others through the interconnective global economy. By calculating the volatility spillover index to depict the volatility network as graphs, the model effectively mirrors the volatility dynamics for the chosen stock market indices. In the empirical analysis, the proposed model surpasses the benchmark model in all forecasting scenarios and is shown to be sensitive to the underlying volatility interrelationships. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.15320 |
| By: | Elie Bouri (School of Business, Lebanese American University, Lebanon); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark.); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia) |
| Abstract: | This paper analyses the effect of supply constraints on international stock market volatility and while also considering their effect on stock returns. Using higher-order nonparametric causality-in-quantiles tests and daily data for China, France, Germany, Italy, Spain, the United Kingdom, the United States, and overall Europe, we find strong evidence of Granger causality flowing from supply constraints to the entire conditional distribution of stock returns and volatility. Notably, supply constraints positively predict stock volatility. This positive predictability remains robust when using alternative measures, including monthly realized variance and different metrics of supply constraints. Our findings have implications for investors and policymakers. |
| Keywords: | Supply Constraints, Stock Markets Volatility, Higher-Order Nonparametric Causality-in-Quantiles Test |
| JEL: | C21 C22 E23 G15 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202441 |
| By: | Bradley, Richard |
| Abstract: | Insurers draw on sophisticated models for the probability distributions over losses associated with catastrophic events that are required to price insurance policies. But prevailing pricing methods don’t factor in the ambiguity around model-based projections that derive from the relative paucity of data about extreme events. I argue however that most current theories of decision making under ambiguity only partially support a solution to the challenge that insurance decision makers face and propose an alternative approach that allows for decision making that is responsive to both the evidential situation of the insurance decision maker and their attitude to ambiguity. |
| Keywords: | ambiguity; insurance decision making; reinsurance; natural catastrophes; catastrophe modelling |
| JEL: | J1 |
| Date: | 2024–09–11 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:122508 |
| By: | Zhang, Pengcheng; Chen, Zezhun; Tzougas, George; Calderín–Ojeda, Enrique; Dassios, Angelos; Wu, Xueyuan |
| Abstract: | The objective of this article is to propose a comprehensive solution for analyzing multidimensional non-life claim count data that exhibits time and cross-dependence, as well as zero inflation. To achieve this, we introduce a multivariate INAR(1) model, with the innovation term characterized by either a multivariate zero-inflated Poisson distribution or a multivariate zero-inflated hurdle Poisson distribution. Additionally, our modeling framework accounts for the impact of individual and coverage-specific covariates on the mean parameters of each model, thereby facilitating the computation of customized insurance premiums based on varying risk profiles. To estimate the model parameters, we employ a novel expectation-maximization (EM) algorithm. Our model demonstrates satisfactory performance in the analysis of European motor third-party liability claim count data. |
| JEL: | C1 |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:124317 |
| By: | Pedro Bordalo; Nicola Gennaioli; Rafael La Porta; Andrei Shleifer |
| Abstract: | We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional differences in required returns. We obtain three results. First, variation in EBRs accounts for a large chunk of cross-sectional return spreads in value, investment, size, and momentum factors. Second, time variation in these spreads is predictable, and proxied by predictable time variation in EBRs. This result holds even controlling for scaled price variables, which may capture time varying required return differentials. Third, firm characteristics typically viewed as capturing risk predict disappointment of expectations (and of EBRs). Overall, return spreads typically attributed to exotic risk factors are explained by predictable movements in non-rational expectations of firms’ earnings growth. |
| JEL: | G02 G1 G14 G4 G41 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33004 |
| By: | Thiago Trafane Oliveira Santos (Central Bank of Brazil, Bras\'ilia, Brazil. Department of %Economics, University of Brasilia, Brazil); Daniel Oliveira Cajueiro (Department of Economics, University of Brasilia, Brazil. National Institute of Science and Technology for Complex Systems) |
| Abstract: | Even though practitioners often estimate Pareto exponents running OLS rank-size regressions, the usual recommendation is to use the Hill MLE with a small-sample correction instead, due to its unbiasedness and efficiency. In this paper, we advocate that you should also apply OLS in empirical applications. On the one hand, we demonstrate that, with a small-sample correction, the OLS estimator is also unbiased. On the other hand, we show that the MLE assigns significantly greater weight to smaller observations. This suggests that the OLS estimator may outperform the MLE in cases where the distribution is (i) strictly Pareto but only in the upper tail or (ii) regularly varying rather than strictly Pareto. We substantiate our theoretical findings with Monte Carlo simulations and real-world applications, demonstrating the practical relevance of the OLS method in estimating tail exponents. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.10448 |