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on Risk Management |
| By: | Xianhua Peng; Xiang Zhou; Bo Xiao; Yi Wu |
| Abstract: | We propose a new risk sensitive reinforcement learning approach for the dynamic hedging of options. The approach focuses on the minimization of the tail risk of the final P&L of the seller of an option. Different from most existing reinforcement learning approaches that require a parametric model of the underlying asset, our approach can learn the optimal hedging strategy directly from the historical market data without specifying a parametric model; in addition, the learned optimal hedging strategy is contract-unified, i.e., it applies to different options contracts with different initial underlying prices, strike prices, and maturities. Our approach extends existing reinforcement learning methods by learning the tail risk measures of the final hedging P&L and the optimal hedging strategy at the same time. We carry out comprehensive empirical study to show that, in the out-of-sample tests, the proposed reinforcement learning hedging strategy can obtain statistically significantly lower tail risk and higher mean of the final P&L than delta hedging methods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09659 |
| By: | Tiantian Mao; Gilles Stupfler; Fan Yang |
| Abstract: | We study a general risk measure called the generalized shortfall risk measure, which was first introduced in Mao and Cai (2018). It is proposed under the rank-dependent expected utility framework, or equivalently induced from the cumulative prospect theory. This risk measure can be flexibly designed to capture the decision maker's behavior toward risks and wealth when measuring risk. In this paper, we derive the first- and second-order asymptotic expansions for the generalized shortfall risk measure. Our asymptotic results can be viewed as unifying theory for, among others, distortion risk measures and utility-based shortfall risk measures. They also provide a blueprint for the estimation of these measures at extreme levels, and we illustrate this principle by constructing and studying a quantile-based estimator in a special case. The accuracy of the asymptotic expansions and of the estimator is assessed on several numerical examples. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.07212 |
| By: | Christina Brinkmann (University of Bonn) |
| Abstract: | I study how firms’ labor hoarding, driven by their reliance on firm-specific human capital, affects their hedging of other business risks. Leveraging German administrative data on short-time work, combined with matched employer-employee data and firm financial information, I develop a firm-level measure of hoarded labor. I formalize the hypothesized risk trade-off in a stylized model featuring demand uncertainty and uncertainty around an unrelated price risk that can be hedged at a cost. Empirically, labor-hoarding firms exhibit larger comovements of their cash flows (CF) with demand fluctuations, illustrating the upside potential of hoarded labor functioning as a capacity increase. However, labor hoarding is not linked to higher overall CF volatility; instead, it is linked to reduced foreign-exchange (FX) risk as one specific price risk. FX risk can substantially contribute to CF volatility, especially for smaller, globally exporting firms that are sensitive to the driving forces of labor hoarding suggested by the model: idiosyncratic demand risk and reliance on firm-specific human capital. I instrument hoarded labor with proxies for firm-specific human capital and find that firms hedge their FX risk more in response to greater labor hoarding. These findings offer a new perspective on firms’ willingness to assume risk in the context of labor market rigidities and institutions. |
| Keywords: | Labor hoarding, human capital, risk management, FX risk |
| JEL: | J01 J24 G00 G32 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:343 |
| By: | Alfred M\"uller |
| Abstract: | The basic principle of any version of insurance is the paradigm that exchanging risk by sharing it in a pool is beneficial for the participants. In case of independent risks with a finite mean this is the case for risk averse decision makers. The situation may be very different in case of infinite mean models. In that case it is known that risk sharing may have a negative effect, which is sometimes called the nondiversification trap. This phenomenon is well known for infinite mean stable distributions. In a series of recent papers similar results for infinite mean Pareto and Fr\'echet distributions have been obtained. We further investigate this property by showing that many of these results can be obtained as special cases of a simple result demonstrating that this holds for any distribution that is more skewed than a Cauchy distribution. We also relate this to the situation of deadly catastrophic risks, where we assume a positive probability for an infinite value. That case gives a very simple intuition why this phenomenon can occur for such catastrophic risks. We also mention several open problems and conjectures in this context. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10139 |
| By: | Kenjiro Oya |
| Abstract: | Abstract This paper proposes a novel approach to Bermudan swaption hedging by applying the deep hedging framework to address limitations of traditional arbitrage-free methods. Conventional methods assume ideal conditions, such as zero transaction costs, perfect liquidity, and continuous-time hedging, which often differ from real market environments. This discrepancy can lead to residual profit and loss (P&L), resulting in two primary issues. First, residual P&L may prevent achieving the initial model price, especially with improper parameter settings, potentially causing a negative P&L trend and significant financial impacts. Second, controlling the distribution of residual P&L to mitigate downside risk is challenging, as hedged positions may become curve gamma-short, making them vulnerable to large interest rate movements. The deep hedging approach enables flexible selection of convex risk measures and hedge strategies, allowing for improved residual P&L management. This study also addresses challenges in applying the deep hedging approach to Bermudan swaptions, such as efficient arbitrage-free market scenario generation and managing early exercise conditions. Additionally, we introduce a unique "Option Spread Hedge" strategy, which allows for robust hedging and provides intuitive interpretability. Numerical analysis results demonstrate the effectiveness of our approach. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10079 |
| By: | Hengxin Cui; Ken Seng Tan; Fan Yang |
| Abstract: | In this paper, we study large losses arising from defaults of a credit portfolio. We assume that the portfolio dependence structure is modelled by the Archimedean copula family as opposed to the widely used Gaussian copula. The resulting model is new, and it has the capability of capturing extremal dependence among obligors. We first derive sharp asymptotics for the tail probability of portfolio losses and the expected shortfall. Then we demonstrate how to utilize these asymptotic results to produce two variance reduction algorithms that significantly enhance the classical Monte Carlo methods. Moreover, we show that the estimator based on the proposed two-step importance sampling method is logarithmically efficient while the estimator based on the conditional Monte Carlo method has bounded relative error as the number of obligors tends to infinity. Extensive simulation studies are conducted to highlight the efficiency of our proposed algorithms for estimating portfolio credit risk. In particular, the variance reduction achieved by the proposed conditional Monte Carlo method, relative to the crude Monte Carlo method, is in the order of millions. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06640 |
| By: | Kamil Fortuna; Janusz Szwabi\'nski |
| Abstract: | The fragility of financial systems was starkly demonstrated in early 2023 through a cascade of major bank failures in the United States, including the second, third, and fourth largest collapses in the US history. The highly interdependent financial networks and the associated high systemic risk have been deemed the cause of the crashes. The goal of this paper is to enhance existing systemic risk analysis frameworks by incorporating essential debt valuation factors. Our results demonstrate that these additional elements substantially influence the outcomes of risk assessment. Notably, by modeling the dynamic relationship between interest rates and banks' credibility, our framework can detect potential cascading failures that standard approaches might miss. The proposed risk assessment methodology can help regulatory bodies prevent future failures, while also allowing companies to more accurately predict turmoil periods and strengthen their survivability during such events. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10386 |
| By: | Saija Toivonen |
| Abstract: | Real estate market actors are navigating in an increasingly challenging market environment where volatility of changes, interconnected drivers and ambiguity of impacts are typical characteristics. As one crisis after another has followed, the traditional probability-based and narrow scoped risk management has been found to be inadequate to cope with the constantly changing landscape of possible risks. Therefore, there is an urgent need to recognize not only the most probable future threats born in the real estate market environment itself but also shed light on the underlying and creeping drivers originating from the different domains of society that can cause crises and lead to a variety of negative impacts on real estate, space and land use. The aim of this study is to increase the understanding of the black swan type of events and their impacts on the real estate market environment. Black swans possess low probability, but their impacts are considered highly significant when realized. Our focus is on the identification of both direct and indirect impacts. We employ a futures-oriented research approach to reveal the possible black swans of the future and utilize the futures wheel method to analyze their impacts in multidisciplinary workshops, together with experts representing academia and practice. The findings of this study contribute to understanding what the low probability events are that hold the potential for significant impacts in the real estate market environment. Our findings serve as a starting point for developing more holistic risk management and resilience building in the field of real estate. |
| Keywords: | resilience, futures studies, risk management, black swans |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-256 |
| By: | Sayyed Faraz Mohseni; Hamid R. Arian; Jean-Fran\c{c}ois B\'egin |
| Abstract: | Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets that can impact portfolio stability, particularly during market stresses. This paper introduces the lexical ratio (LR), a novel metric that leverages textual data to capture diversification dimensions absent in standard approaches. By treating each asset as a unique document composed of sectorspecific and financial keywords, the LR evaluates portfolio diversification by distributing these terms across assets, incorporating entropy-based insights from information theory. We thoroughly analyze LR's properties, including scale invariance, concavity, and maximality, demonstrating its theoretical robustness and ability to enhance risk-adjusted portfolio returns. Using empirical tests on S&P 500 portfolios, we compare LR's performance to established metrics such as Markowitz's volatility-based measures and diversification ratios. Our tests reveal LR's superiority in optimizing portfolio returns, especially under varied market conditions. Our findings show that LR aligns with conventional metrics and captures unique diversification aspects, suggesting it is a viable tool for portfolio managers. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06080 |
| By: | Sydow, Matthias; Fukker, Gábor; Dubiel-Teleszynski, Tomasz; Franch, Fabio; Gründl, Helmut; Miccio, Debora; Pellegrino, Michela; Gallet, Sébastien; Kotronis, Stelios; Schlütter, Sebastian; Sottocornola, Matteo |
| Abstract: | This paper documents the extension of the system-wide stress testing framework of the ECB with the insurance sector for a more thorough assessment of risks to financial stability. The special nature of insurers is captured by the modelling of the liability side and its loss absorbing capacity of technical provisions as the main novel feature of the model. Leveraging on highly granular data and information on bilateral exposures, we assess the impact of liquidity and solvency shocks and demonstrate how a combined endogenous reactions of banks, investment funds and insurance companies can further amplify losses in the financial system. The chosen hypothetical scenario and subsequent simulation results show that insurers’ ability to transfer losses to policyholders reduces losses for the entire financial sector. Furthermore, beyond a certain threshold, insurance companies play a crucial role in mitigating both direct and indirect contagion. JEL Classification: D85, G01, G21, G23, L14 |
| Keywords: | contagion, financial stability, fire sales, insurance companies, interconnectedness, stress test |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20243000 |
| By: | Jan Dhaene; Rodrigue Kazzi; Emiliano A. Valdez |
| Abstract: | In this paper, we present axiomatic characterizations of some simple risk-sharing (RS) rules, such as the uniform, the mean-proportional and the covariance-based linear RS rules. These characterizations make it easier to understand the underlying principles when applying these rules. Such principles typically include maintaining some degree of anonymity regarding participants' data and/or incident-specific data, adopting non-punitive processes and ensuring the equitability and fairness of risk sharing. By formalizing key concepts such as the reshuffling property, the source-anonymous contributions property and the strongly aggregate contributions property, along with their generalizations, we develop a comprehensive framework that expresses these principles clearly and defines the relevant rules. To illustrate, we demonstrate that the uniform RS rule, a simple mechanism in which risks are shared equally, is the only RS rule that satisfies both the reshuffling property and the source-anonymous contributions property. This straightforward axiomatic characterization of the uniform RS rule serves as a foundation for exploring similar principles in two broad classes of risk-sharing rules, which we baptize the $q$-proportional RS rules and the $(q_1, q_2)$-based linear RS rules, respectively. This framework also allows us to introduce novel particular RS rules, such as the scenario-based RS rules. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06240 |
| By: | Sergio A. Correia; Stephan Luck; Emil Verner |
| Abstract: | Can bank failures be predicted before they happen? In a previous post, we established three facts about failing banks that indicated that failing banks experience deteriorating fundamentals many years ahead of their failure and across a broad range of institutional settings. In this post, we document that bank failures are remarkably predictable based on simple accounting metrics from publicly available financial statements that measure a bank’s insolvency risk and funding vulnerabilities. |
| Keywords: | bank runs; financial crises; deposit insurance; bank failures |
| JEL: | G01 G2 |
| Date: | 2024–11–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:99163 |
| By: | Alhassan S. Yasin; Prabdeep S. Gill |
| Abstract: | The inherent volatility and dynamic fluctuations within the financial stock market underscore the necessity for investors to employ a comprehensive and reliable approach that integrates risk management strategies, market trends, and the movement trends of individual securities. By evaluating specific data, investors can make more informed decisions. However, the current body of literature lacks substantial evidence supporting the practical efficacy of reinforcement learning (RL) agents, as many models have only demonstrated success in back testing using historical data. This highlights the urgent need for a more advanced methodology capable of addressing these challenges. There is a significant disconnect in the effective utilization of financial indicators to better understand the potential market trends of individual securities. The disclosure of successful trading strategies is often restricted within financial markets, resulting in a scarcity of widely documented and published strategies leveraging RL. Furthermore, current research frequently overlooks the identification of financial indicators correlated with various market trends and their potential advantages. This research endeavors to address these complexities by enhancing the ability of RL agents to effectively differentiate between positive and negative buy/sell actions using financial indicators. While we do not address all concerns, this paper provides deeper insights and commentary on the utilization of technical indicators and their benefits within reinforcement learning. This work establishes a foundational framework for further exploration and investigation of more complex scenarios. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.07585 |
| By: | Jihyun Park; Andrey Sarantsev |
| Abstract: | Classic stochastic volatility models assume volatility is unobservable. We use the VIX for consider it observable, and use the Volatility Index: S\&P 500 VIX. This index was designed to measure volatility of S&P 500. We apply it to a different segment: Corporate bond markets. We fit time series models for spreads between corporate and 10-year Treasury bonds. Next, we divide residuals by VIX. Our main idea is such division makes residuals closer to the ideal case of a Gaussian white noise. This is remarkable, since these residuals and VIX come from separate market segments. We conclude with the analysis of long-term behavior of these models. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.22498 |
| By: | Kristina Trajkovic (National Bank of Serbia) |
| Abstract: | Prevention of money laundering and other abuses in the digital assets sector is a major step in the preservation of financial system stability. Non-alignment of regulatory regimes in an environment of rapid market development creates a potential for abuse and illicit activities. Monitoring the market requires systematic analysis in order to define clear guidelines for mitigating identified risks. Regular implementation of risk assessment and the regulator’s supervisory function facilitate the identification of the riskiness of the entire digital assets sector. In addition to an overview of regulations and standards governing the prevention of money laundering, the paper looks into the risks to which the digital assets sector is exposed, including the conduct of supervision and, in this sense, implementation of the risk-based approach. |
| Keywords: | regulation, digital assets, virtual currency, supervision, money laundering, abuse |
| JEL: | E30 K20 K23 G18 |
| Date: | 2023–09 |
| URL: | https://d.repec.org/n?u=RePEc:nsb:bilten:17 |
| By: | Graham L. Giller |
| Abstract: | This brief note discusses some of the aspects of a model for the covariance of equity returns based on a simple "isotropic" structure in which all pairwise correlations are taken to be the same value. The effect of the structure on feasible values for the common correlation of returns and on the "effective degrees of freedom" within the equity cross-section are discussed, as well as the impact of this constraint on the asymptotic Normality of portfolio returns is examined. An eigendecomposition of the covariance matrix is presented and used to decompose returns into a common market factor and "non-diversifiable" idiosyncratic risk. A empirical analysis of the recent history of the returns of S&P 500 Index members is presented and compared to the expectations from both this model and linear factor models. This analysis supports the isotropic covariance model and does not seem to provide evidence in support of linear factor models. The fact that idiosyncratic risk may not be removed in a model that that data supports undermines the basic premises of structures such as the C.A.P.M. and A.P.T. If the cross-section of equity returns is more accurately described by this structure then an inevitable consequence is that picking stocks is not a "pointless" activity, as the returns to residual risk would be non-zero. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08864 |
| By: | Philippe Bergault; Louis Bertucci; David Bouba; Olivier Gu\'eant; Julien Guilbert |
| Abstract: | In this paper, we introduce a novel framework to model the exchange rate dynamics between two intrinsically linked cryptoassets, such as stablecoins pegged to the same fiat currency or a liquid staking token and its associated native token. Our approach employs multi-level nested Ornstein-Uhlenbeck (OU) processes, for which we derive key properties and develop calibration and filtering techniques. Then, we design an automated market maker (AMM) model specifically tailored for the swapping of closely related cryptoassets. Distinct from existing models, our AMM leverages the unique exchange rate dynamics provided by the multi-level nested OU processes, enabling more precise risk management and enhanced liquidity provision. We validate the model through numerical simulations using real-world data for the USDC/USDT and wstETH/WETH pairs, demonstrating that it consistently yields efficient quotes. This approach offers significant potential to improve liquidity in markets for pegged assets. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08145 |
| By: | Maximilian Grimm (University of Bonn) |
| Abstract: | Does monetary policy affect funding vulnerabilities of the banking system? I show that contractionary monetary policy shocks cause an aggregate outflow of retail deposits and an inflow of non-core market-based funding. Using a newly constructed worldwide dataset covering the liability structure of banking systems at monthly frequency, I demonstrate that a growing reliance on wholesale funding is associated with increasing risks of financial instability and subsequent contractions in lending and real activity. I rationalize this effect of monetary policy on banks' funding structure and ultimately on financial stability risk in a model where profit-maximizing banks do not internalize the heightened systemic risk stemming from the rise of runnable debt in the system. This paper shows that monetary policy has direct consequences for financial stability by changing the liability structure of the banking sector. |
| Keywords: | Monetary policy, bank funding, banking fragility |
| JEL: | E44 E52 E58 G01 G21 N10 N20 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:341 |
| By: | Hao Qin; Charlie Che; Ruozhong Yang; Liming Feng |
| Abstract: | The Bass Local Volatility Model (Bass-LV), as studied in \citep{henry2021bass}, stands out for its ability to eliminate the need for interpolation between maturities. This offers a significant advantage over traditional LV models. However, its performance highly depends on accurate construction of state price densities and the corresponding marginal distributions and efficient numerical convolutions which are necessary when solving the associated fixed point problems. In this paper, we propose a new approach combining local quadratic estimation and lognormal mixture tails for the construction of state price densities. We investigate computational efficiency of trapezoidal rule based schemes for numerical convolutions and show that they outperform commonly used Gauss-Hermite quadrature. We demonstrate the performance of the proposed method, both in standard option pricing models, as well as through a detailed market case study. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.04321 |
| By: | Sergio A. Correia; Stephan Luck; Emil Verner |
| Abstract: | Why do banks fail? In a new working paper, we study more than 5, 000 bank failures in the U.S. from 1865 to the present to understand whether failures are primarily caused by bank runs or by deteriorating solvency. In this first of three posts, we document that failing banks are characterized by rising asset losses, deteriorating solvency, and an increasing reliance on expensive noncore funding. Further, we find that problems in failing banks are often the consequence of rapid asset growth in the preceding decade. |
| Keywords: | financial crises; deposit insurance; bank runs; bank failures |
| JEL: | G21 |
| Date: | 2024–11–21 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:99160 |
| By: | Bindseil, Ulrich; Marrazzo, Marco; Sauer, Stephan |
| Abstract: | As digital payments become increasingly popular, many central banks are looking into the issuance of retail central bank digital currency (CBDC) as a new central bank monetary liability in addition to banknotes and commercial bank reserves. CBDC will have broadly the same balance sheet and profit implications as the issuance of banknotes. While the decision to issue CBDC is often thought to likely increase the size of central banks’ balance sheets, the net impact of digitalisation on balance sheet size could also be negative, as the number of banknotes in circulation may decline and CBDC’s design features could limit its take-up as a store of value. We use scenario analyses to illustrate the key drivers of the impact of CBDC on central bank profitability, with the part of CBDC that does not derive from an exchange of banknotes being an important factor. The financial risk implications of CBDC for central banks can be managed via well-established frameworks and relate primarily to the impact on balance sheet size and asset composition. The paper concludes with a discussion on how the profit and risk channels affect central bank capital. JEL Classification: E58 |
| Keywords: | central bank capital, central bank digital currency, digital money, financial risk management, seigniorage |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbops:2024360 |