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on Risk Management |
| By: | González Ayestarán, Rodrigo; Garcia Fronti, Javier Ignacio |
| Abstract: | This study delves into the intricate world of risk management at YPF S.A., Argentina's premier energy company, spanning the period from 2007 to 2019. We begin by examining the dynamic landscape of the Argentine oil market and YPF's pivotal role within it. Subsequently, we dissect YPF's initial risk management framework in 2007, tracing its evolution over the subsequent years. Key components such as the Risk and Sustainability Committee, the Global Risk Management Policy and Regulations, and the Risk Management are explored. The study culminates in a detailed analysis of YPF's financial risk management policies, with a particular focus on the strategic utilization of derivative financial instruments. |
| Keywords: | Risk Management, Financial Risks, Oil Market |
| JEL: | M11 |
| Date: | 2024–10–29 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122540 |
| By: | Andrew Fleck; Edward Furman; Yang Shen |
| Abstract: | In order to properly manage risk, practitioners must understand the aggregate risks they are exposed to. Additionally, to properly price policies and calculate bonuses the relative riskiness of individual business units must be well understood. Certainly, Insurers and Financiers are interested in the properties of the sums of the risks they are exposed to and the dependence of risks therein. Realistic risk models however must account for a variety of phenomena: ill-defined moments, lack of elliptical dependence structures, excess kurtosis and highly heterogeneous marginals. Equally important is the concern over industry-wide systematic risks that can affect multiple business lines at once. Many techniques of varying sophistication have been developed with all or some of these problems in mind. We propose a modification to the classical individual risk model that allows us to model company-wide losses via the class of Multivariate Stable Distributions. Stable Distributions incorporate many of the unpleasant features required for a realistic risk model while maintaining tractable aggregation and dependence results. We additionally compute the Tail Conditional Expectation of aggregate risks within the model and the corresponding allocations. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14984 |
| By: | de Oliveira Souza, Thiago |
| Abstract: | I use Financial Economics theory to derive a measure of model risk with clear and actionable implications. The resulting “Model Risk Price” is based on the covariance between the payoffs associated with the model and the Stochastic Discount Factor, setting it fundamentally apart from the model accuracy statistics that have been typically used as model risk measures. Given that it is measured in financial terms, the Model Risk Price and its associated hedging strategies can also be intuitively communicated to non-technical audiences, such as investors, CEOs, other C-suite executives, and risk managers. From a practical standpoint, the paper addresses one of the most critical questions posed to risk managers by the firm’s investors: What is the precise impact of model risk on their investments, and what concrete actions can be taken to mitigate it. More broadly, the paper makes a seminal contribution to the literature by formally defining and economically measuring the risk of using a model, rather than simply estimating the uncertainty in its output as is currently done. |
| Keywords: | Model Risk; Hedging; Equity investors; Asset Pricing; Actionable results |
| JEL: | G11 G12 G20 G32 |
| Date: | 2024–09–21 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121827 |
| By: | Caio Almeida; Maria Grith; Ratmir Miftachov; Zijin Wang |
| Abstract: | Based on options and realized returns, we analyze risk premia in the Bitcoin market through the lens of the Pricing Kernel (PK). We identify that: 1) The projected PK into Bitcoin returns is W-shaped and steep in the negative returns region; 2) Negative Bitcoin returns account for 33% of the total Bitcoin index premium (BP) in contrast to 70% of S&P500 equity premium explained by negative returns. Applying a novel clustering algorithm to the collection of estimated Bitcoin risk-neutral densities, we find that risk premia vary over time as a function of two distinct market volatility regimes. In the low-volatility regime, the PK projection is steeper for negative returns. It has a more pronounced W-shape than the unconditional one, implying particularly high BP for both extreme positive and negative returns and a high Variance Risk Premium (VRP). In high-volatility states, the BP attributable to positive and negative returns is more balanced, and the VRP is lower. Overall, Bitcoin investors are more worried about variance and downside risk in low-volatility states. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.15195 |
| By: | Lukas Gonon; Thilo Meyer-Brandis; Niklas Weber |
| Abstract: | This paper investigates systemic risk measures for stochastic financial networks of explicitly modelled bilateral liabilities. We extend the notion of systemic risk measures from Biagini, Fouque, Fritelli and Meyer-Brandis (2019) to graph structured data. In particular, we focus on an aggregation function that is derived from a market clearing algorithm proposed by Eisenberg and Noe (2001). In this setting, we show the existence of an optimal random allocation that distributes the overall minimal bailout capital and secures the network. We study numerical methods for the approximation of systemic risk and optimal random allocations. We propose to use permutation equivariant architectures of neural networks like graph neural networks (GNNs) and a class that we name (extended) permutation equivariant neural networks ((X)PENNs). We compare their performance to several benchmark allocations. The main feature of GNNs and (X)PENNs is that they are permutation equivariant with respect to the underlying graph data. In numerical experiments we find evidence that these permutation equivariant methods are superior to other approaches. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07222 |
| By: | Guibril Zerbo |
| Abstract: | This article studies the effect of preferences on an individual's optimal choice between insurance demand and self-insurance in a risk context and then in an ambiguous context. The innovative idea in this paper is to introduce ambiguity about the effectiveness of self-insurance to understand the nature of the relationship between insurance demand and self-insurance demand. We show that an increase in risk aversion increases the demand for insurance and decreases the demand for self-insurance. However, when risk is introduced on the effectiveness of self-insurance, we show that the individual still prefers self-insurance to market insurance. We also show that when ambiguity is introduced on the efficacy of self-insurance, the individual always prefers market insurance to self-insurance. Finally, we determine the conditions under which the individual's self-insurance effort is higher or lower under ambiguity than risk. |
| Keywords: | Natural risks, arbitration, insurance, self-insurance, efficacy, risk, ambiguity |
| JEL: | D81 G22 Q54 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:drm:wpaper:2024-30 |
| By: | Gulliksson, Mårten (Örebro University School of Science and Technology); Mazur, Stepan (Örebro University School of Business); Oleynik, Anna (Department of Mathematics, University of Bergen) |
| Abstract: | This paper examines optimal portfolio selection using quantile-based risk measures such as Valueat-Risk (VaR) and Conditional Value-at-Risk (CVaR). We address the case of a singular covariance matrix of asset returns, which leads to an optimization problem with infinitely many solutions. An analytical form for a general solution is derived, along with a unique solution that minimizes the L2-norm. We also show that the general solution reduces to the standard optimal portfolio for VaR and CVaR when the covariance matrix is non-singular. |
| Keywords: | Minimum VaR portfolio; Minimum CVaR portfolio; Singular covariance matrix; Linear illposed problems |
| JEL: | C58 G11 G32 |
| Date: | 2024–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2024_009 |
| By: | Masahiro Kato |
| Abstract: | This study explores portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and various methods have been developed to achieve this goal. For example, the mean-variance approach constructs portfolios by balancing the trade-off between the mean and variance of asset returns, while the quantile-based approach optimizes portfolios by accounting for tail risk. These traditional methods often rely on distributional information estimated from historical data. However, a key concern is the uncertainty of future portfolio returns, which may not be fully captured by simple reliance on historical data, such as using the sample average. To address this, we propose a framework for predictive portfolio selection using conformal inference, called Conformal Predictive Portfolio Selection (CPPS). Our approach predicts future portfolio returns, computes corresponding prediction intervals, and selects the desirable portfolio based on these intervals. The framework is flexible and can accommodate a variety of predictive models, including autoregressive (AR) models, random forests, and neural networks. We demonstrate the effectiveness of our CPPS framework using an AR model and validate its performance through empirical studies, showing that it provides superior returns compared to simpler strategies. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.16333 |
| By: | Cisil Sarisoy |
| Abstract: | Option-implied volatilities of U.S. short-term interest rates have risen sharply since late 2021, reaching their highest levels in over a decade. Although these measures declined moderately since early 2023, they remain at around the 70th percentile of their historical distribution. This note links the implied volatility of short-term interest rates to macroeconomic uncertainty and highlights two fundamental drivers of short-term interest rate volatility over the past 30 years: inflation uncertainty and growth uncertainty. |
| Date: | 2024–10–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:2024-10-24 |
| By: | Hennessy, David; Lapan, Harvey |
| Abstract: | We explore connections between the certainty equivalent return (CER) functional and the underlying utility function. Curvature properties of the functional depend upon how utility function attributes relate to Hyperbolic Absolute Risk Aversion (HARA) type utility functions. If the CER functional is concave, i.e., if risk tolerance is concave in wealth, then preferences are standard. The CER functional is linear in lotteries if utility is HARA and lottery payoffs are on a line in state space. Implications for the optimality of portfolio diversification are given. When utility is concave and Non-increasing Relative Risk Averse, then the CER functional is superadditive in lotteries. Depending upon the nature of covariation among lottery payoffs, CERs for Constant Absolute Risk Averse utility functions may be subadditive or superadditive in lotteries. Our approach lends itself to straightforward experiments to elicit higher order attributes on risk preferences. |
| Date: | 2024–10–29 |
| URL: | https://d.repec.org/n?u=RePEc:isu:genstf:202410291658110000 |
| By: | Kira Henshaw; Cedric H. A. Koffi; Olivier Menoukeu Pamen; Raghid Zeineddine |
| Abstract: | In this paper, we investigate a complex variation of the standard joint life annuity policy by introducing three distinct contingent benefits for the surviving member(s) of a couple, along with a contingent benefit for their beneficiaries if both members pass away. Our objective is to price this innovative insurance policy and analyse its sensitivity to key model parameters, particularly those related to the joint mortality framework. We employ the $QP$-rule (described in Section \ref{secgenset}), which combines the real-world probability measure $P$ for mortality risk with risk-neutral valuation under $Q$ for financial market risks. The model enables explicit pricing expressions, computed using efficient numerical methods. Our results highlight the interdependent risks faced by couples, such as broken-heart syndrome, providing valuable insights for insurers and policyholders regarding the pricing influences of these factors. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.11849 |
| By: | J\'er\^ome Lelong (LJK); V\'eronique Maume-Deschamps (ICJ, PSPM); William Thevenot (ICJ, PSPM) |
| Abstract: | We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.10239 |
| By: | Luca Vincenzo Ballestra; Enzo D'Innocenzo; Christian Tezza |
| Abstract: | We introduce a novel GARCH model that integrates two sources of uncertainty to better capture the rich, multi-component dynamics often observed in the volatility of financial assets. This model provides a quasi closed-form representation of the characteristic function for future log-returns, from which semi-analytical formulas for option pricing can be derived. A theoretical analysis is conducted to establish sufficient conditions for strict stationarity and geometric ergodicity, while also obtaining the continuous-time diffusion limit of the model. Empirical evaluations, conducted both in-sample and out-of-sample using S\&P500 time series data, show that our model outperforms widely used single-factor models in predicting returns and option prices. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14585 |
| By: | Emmanuel Gnabeyeu; Omar Karkar; Imad Idboufous |
| Abstract: | The volatility fitting is one of the core problems in the equity derivatives business. Through a set of deterministic rules, the degrees of freedom in the implied volatility surface encoding (parametrization, density, diffusion) are defined. Whilst very effective, this approach widespread in the industry is not natively tailored to learn from shifts in market regimes and discover unsuspected optimal behaviors. In this paper, we change the classical paradigm and apply the latest advances in Deep Reinforcement Learning(DRL) to solve the fitting problem. In particular, we show that variants of Deep Deterministic Policy Gradient (DDPG) and Soft Actor Critic (SAC) can achieve at least as good as standard fitting algorithms. Furthermore, we explain why the reinforcement learning framework is appropriate to handle complex objective functions and is natively adapted for online learning. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.11789 |
| By: | Andrew Fleck; Edward Furman; Yang Shen |
| Abstract: | Nowadays insurers have to account for potentially complex dependence between risks. In the field of loss reserving, there are many parametric and non-parametric models attempting to capture dependence between business lines. One common approach has been to use additive background risk models (ABRMs) which provide rich and interpretable dependence structures via a common shock model. Unfortunately, ABRMs are often restrictive. Models that capture necessary features may have impractical to estimate parameters. For example models without a closed-form likelihood function for lack of a probability density function (e.g. some Tweedie, Stable Distributions, etc). We apply a modification of the continuous generalised method of moments (CGMM) of [Carrasco and Florens, 2000] which delivers comparable estimators to the MLE to loss reserving. We examine models such as the one proposed by [Avanzi et al., 2016] and a related but novel one derived from the stable family of distributions. Our CGMM method of estimation provides conventional non-Bayesian estimates in the case where MLEs are impractical. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14985 |
| By: | Chaudhari, Saurav L. (HTNP Industries) |
| Abstract: | This paper explores the application of the Binomial Distribution Theorem in optimizing portfolio rebalancing strategies to outperform the Nifty Index. A model based on the Binomial distribution is proposed for identifying entry and exit points in stocks, aiming for a 30\% Compound Annual Growth Rate (CAGR). Our empirical analysis demonstrates that by systematically applying this technique, portfolio managers can significantly enhance returns while maintaining risk levels comparable to the benchmark index. This method shows potential for outperforming traditional rebalancing strategies. Extensions of this theorem, including Monte Carlo simulations and Black-Scholes adjustments, are incorporated to further refine the model and enhance its effectiveness. |
| Date: | 2024–10–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:u5q97 |
| By: | Tsiflikidou, Ioanna-Maria; METAXAS, THEODORE |
| Abstract: | This study aims to review major economic crises throughout the centuries, in order to see what valuable lessons can be learned from re-examining them. As the 20th century was marked by tremendous economic turmoils, that changed the world economy of today, we will focus on the Great Depression of 1929 and the Oil Crisis of 1973, comparing them also with the 21st century global crisis of 2008. Through a lens of historic and periodic analysis, content analysis, and comparative analysis, this study seeks to unravel the intricacies of these financial crises, their similarities, differences and what went wrong in each case and what role the Federal Reserve’s System played in in shaping economic outcomes. The findings underscore the significance of macroeconomic imbalances, poorly regulated financial markets, and inadequate risk management in amplifying the impact of economic events. Policymakers' responses and reforms after each crisis are examined, highlighting the recurring theme of claims of increased preparedness for future scenarios. The study concludes by urging a re-evaluation of the Federal Reserve's policies, emphasizing the need for a proactive and informed approach to address potential future crises and advocating for a better understanding of the global impact of national policies. |
| Keywords: | economic crises; 20th century, qualitative analysis, review and comparison |
| JEL: | G01 G18 G38 |
| Date: | 2023 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122466 |