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on Risk Management |
| By: | Richard Gerlach; Antonio Naimoli; Giuseppe Storti |
| Abstract: | A method for quantile-based, semi-parametric historical simulation estimation of multiple step ahead Value-at-Risk (VaR) and Expected Shortfall (ES) models is developed. It uses the quantile loss function, analogous to how the quasi-likelihood is employed by standard historical simulation methods. The returns data are scaled by the estimated quantile series, then resampling is employed to estimate the forecast distribution one and multiple steps ahead, allowing tail risk forecasting. The proposed method is applicable to any data or model where the relationship between VaR and ES does not change over time and can be extended to allow a measurement equation incorporating realized measures, thus including Realized GARCH and Realized CAViaR type models. Its finite sample properties, and its comparison with existing historical simulation methods, are evaluated via a simulation study. A forecasting study assesses the relative accuracy of the 1% and 2.5% VaR and ES one-day-ahead and ten-day-ahead forecasting results for the proposed class of models compared to several competitors. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.20978 |
| By: | Ying-Hui Shao; Yan-Hong Yang; Wei-Xing Zhou |
| Abstract: | This paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method. We reveal several key insights. First, the overall transmission connectedness index (TCI) closely aligns with the contemporaneous TCI, while the lagged TCI is significantly lower. Second, AI ETFs and clean energy act as risk transmitters, whereas AI tokens and green bond function as risk receivers. Third, AI tokens are difficult to hedge and provide limited hedging ability compared to AI ETFs and green assets. However, multivariate portfolios effectively reduce AI tokens investment risk. Among them, the minimum correlation portfolio outperforms the minimum variance and minimum connectedness portfolios. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.01148 |
| By: | Rath Minati; Date Hema |
| Abstract: | The integration of Quantum Deep Learning (QDL) techniques into the landscape of financial risk analysis presents a promising avenue for innovation. This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA). By leveraging RTDPA, the proposed approach tailors predictive models to different loan categories, aiming to enhance the accuracy and efficiency of credit risk evaluation. While this work explores the potential of integrating quantum methods with classical deep learning for risk assessment, it focuses on the feasibility and performance of this hybrid framework rather than claiming transformative industry-wide impacts. The findings offer insights into how quantum techniques can complement traditional financial analysis, paving the way for further advancements in predictive modeling for credit risk. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.07806 |
| By: | Paul McCloud |
| Abstract: | As operators acting on the undetermined final settlement of a derivative security, expectation is linear but price is non-linear. When the market of underlying securities is incomplete, non-linearity emerges from the bid-offer around the mid price that accounts for the residual risks of the optimal funding and hedging strategy. At the extremes, non-linearity also arises from the embedded options on capital that are exercised upon default. In this essay, these convexities are quantified in an entropic risk metric that evaluates the strategic risks, which is realised as a cost with the introduction of bilateral margin. Price is then adjusted for market incompleteness and the risk of default caused by the exhaustion of capital. In the complete market theory, price is derived from a martingale condition. In the incomplete market theory presented here, price is instead derived from a log-martingale condition: \begin{equation} p=-\frac{1}{\alpha}\log\mathbb{E}\exp[-\alpha P] \notag \end{equation} for the price $p$ and payoff $P$ of a funded and hedged derivative security, where the price measure $\mathbb{E}$ has minimum entropy relative to economic expectations, and the parameter $\alpha$ matches the risk aversion of the investor. This price principle is easily applied to standard models for market evolution, with applications considered here including model risk analysis, deep hedging and decentralised finance. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.08613 |
| By: | Junyi Guo; Xia Han; Hao Wang |
| Abstract: | This paper investigates the dynamic reinsurance design problem under the mean-variance criterion, incorporating heterogeneous beliefs between the insurer and the reinsurer, and introducing an incentive compatibility constraint to address moral hazard. The insurer's surplus process is modeled using the classical Cram\'er-Lundberg risk model, with the option to invest in a risk-free asset. To solve the extended Hamilton-Jacobi-Bellman (HJB) system, we apply the partitioned domain optimization technique, transforming the infinite-dimensional optimization problem into a finite-dimensional one determined by several key parameters. The resulting optimal reinsurance contracts are more complex than the standard proportional and excess-of-loss contracts commonly studied in the mean-variance literature with homogeneous beliefs. By further assuming specific forms of belief heterogeneity, we derive the parametric solutions and obtain a clear optimal equilibrium solution. Finally, we compare our results with models where the insurer and reinsurer share identical beliefs or where the incentive compatibility constraint is relaxed. Numerical examples are provided to illustrate the impact of belief heterogeneity on optimal reinsurance strategies. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.05474 |
| By: | Mostapha Benhenda (LAGA) |
| Abstract: | This paper presents a novel risk-sensitive trading agent combining reinforcement learning and large language models (LLMs). We extend the Conditional Value-at-Risk Proximal Policy Optimization (CPPO) algorithm, by adding risk assessment and trading recommendation signals generated by a LLM from financial news. Our approach is backtested on the Nasdaq-100 index benchmark, using financial news data from the FNSPID dataset and the DeepSeek V3, Qwen 2.5 and Llama 3.3 language models. The code, data, and trading agents are available at: https://github.com/benstaf/FinRL_DeepSee k |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.07393 |
| By: | Jan Fialkowski; Christian Diem; Andr\'as Borsos; Stefan Thurner |
| Abstract: | Supply chain disruptions constitute an often underestimated risk for financial stability. As in financial networks, systemic risks in production networks arises when the local failure of one firm impacts the production of others and might trigger cascading disruptions that affect significant parts of the economy. Here, we study how systemic risk in production networks translates into financial systemic risk through a mechanism where supply chain contagion leads to correlated bank-firm loan defaults. We propose a financial stress-testing framework for micro- and macro-prudential applications that features a national firm level supply chain network in combination with interbank network layers. The model is calibrated by using a unique data set including about 1 million firm-level supply links, practically all bank-firm loans, and all interbank loans in a small European economy. As a showcase we implement a real COVID-19 shock scenario on the firm level. This model allows us to study how the disruption dynamics in the real economy can lead to interbank solvency contagion dynamics. We estimate to what extent this amplifies financial systemic risk. We discuss the relative importance of these contagion channels and find an increase of interbank contagion by 70% when production network contagion is present. We then examine the financial systemic risk firms bring to banks and find an increase of up to 28% in the presence of the interbank contagion channel. This framework is the first financial systemic risk model to take agent-level dynamics of the production network and shocks of the real economy into account which opens a path for directly, and event-driven understanding of the dynamical interaction between the real economy and financial systems. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17044 |
| By: | Isaak, Niklas; Jessen, Robin |
| Abstract: | Women born later experience greater earnings growth volatility at given ages than the next older cohort. This alone would imply a welfare loss due to increased earnings risk. However, using German registry data for the years 2001-2016, we document a moderation in higher-order earnings risk: Both men and women born later face higher skewness in earnings changes, indicating fewer large decreases than increases, and lower kurtosis at younger ages, implying fewer large earnings changes. The changes in these higher-order moments point at a welfare increase. These patterns persist for 5-year earnings changes, which are more reflective of persistent changes. Notably, during the Great Recession, all male cohorts' skewness dropped sharply, driven by greater lower-tail earnings risk, whereas younger women were much less affected. |
| Abstract: | Später geborene Frauen erfahren in einem bestimmten Alter eine größere Volatilität des Einkommenswachstums als die nächst ältere Kohorte. Dies allein würde einen Wohlfahrtsverlust aufgrund des erhöhten Einkommensrisikos bedeuten. Anhand deutscher Registerdaten für die Jahre 2001-2016 zeigen wir jedoch eine Abschwächung des Lohnrisikos in höheren Momenten: Sowohl bei Männern als auch bei Frauen, die später geboren wurden, ist die Schiefe der Verdienstveränderungen höher, was auf weniger große Rückgänge als Zuwächse hindeutet, und die Wölbung ist in jüngeren Jahren geringer, was weniger große Verdienstveränderungen impliziert. Die Veränderungen in diesen höheren Momenten deuten auf einen Anstieg der Wohlfahrt hin. Diese Muster bleiben bei den 5-Jahres-Verdienständerungen bestehen, die eher auf persistente Veränderungen hindeuten. Bemerkenswert ist, dass während der Finanzkrise die Schiefe bei allen männlichen Kohorten stark abnahm, was auf ein größeres Risiko im unteren Teil des Verdienstes zurückzuführen ist, während jüngere Frauen weit weniger betroffen waren. |
| Keywords: | Wage risk, income dynamics, life cycle, business cycle |
| JEL: | D31 J31 E24 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:rwirep:311187 |
| By: | Xiangdong Liu; Sicheng Fu; Shaopeng Hong |
| Abstract: | Volatility forecasting in financial markets is a topic that has received more attention from scholars. In this paper, we propose a new volatility forecasting model that combines the heterogeneous autoregressive (HAR) model with a family of path-dependent volatility models (HAR-PD). The model utilizes the long- and short-term memory properties of price data to capture volatility features and trend features. By integrating the features of path-dependent volatility into the HAR model family framework, we develop a new set of volatility forecasting models. And, we propose a HAR-REQ model based on the empirical quartile as a threshold, which exhibits stronger forecasting ability compared to the HAR-REX model. Subsequently, the predictive performance of the HAR-PD model family is evaluated by statistical tests using data from the Chinese stock market and compared with the basic HAR model family. The empirical results show that the HAR-PD model family has higher forecasting accuracy compared to the underlying HAR model family. In addition, robustness tests confirm the significant predictive power of the HAR-PD model family. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.00851 |
| By: | Pierre Azoulay; Wesley H. Greenblatt |
| Abstract: | Scientific projects that carry a high degree of risk may be more likely to lead to breakthroughs yet also face challenges in winning the support necessary to be carried out. We analyze the determinants of renewal for more than 100, 000 R01 grants from the National Institutes of Health between 1980 and 2015. We use four distinct proxies to measure risk taking: extreme tail outcomes, disruptiveness, pivoting from an investigator’s prior work, and standing out from the crowd in one’s field. After carefully controlling for investigator, grant, and institution characteristics, we measure the association between risk taking and grant renewal. Across each of these measures, we find that risky grants are renewed at markedly lower rates than less risky ones. We also provide evidence that the magnitude of the risk penalty is magnified for more novel areas of research and novice investigators, consistent with the academic community’s perception that current scientific institutions do not motivate exploratory research adequately. |
| JEL: | H51 O32 O38 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33495 |
| By: | de Castro, Luciano; Frischtak, Claudio R.; Rodrigues, Arthur |
| Abstract: | Most developing economies rely on foreign capital to finance their infrastructure needs. These projects are usually structured as long-term (25–35 years) franchises that pay in local currency. If investors evaluate their returns in terms of foreign currency, exchange rate volatility introduces risk that may reduce the level of investment below what would be socially optimal. This paper proposes a mechanism with very general features that hedges exchange rate fluctuation by adjusting the concession period. Such mechanism does not imply additional costs to the government and could be offered as a zero-cost option to lenders and investors exposed to currency fluctuations. This general mechanism is illustrated with three alternative specifications and data from a 25-year highway franchise is used to simulate how they would play out in eight different countries that exhibit diverse exchange rate trajectories. |
| Date: | 2023–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10568 |
| By: | Marc-Arthur Diaye (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); André Lapidus (PHARE - Philosophie, Histoire et Analyse des Représentations Économiques - UP1 - Université Paris 1 Panthéon-Sorbonne); Christian Schmidt (PHARE - Philosophie, Histoire et Analyse des Représentations Économiques - UP1 - Université Paris 1 Panthéon-Sorbonne) |
| Abstract: | This paper aims to restate, in a decision theory framework, the results of some significant contributions of the literature on probability discounting that followed the publication of the pioneering article by Rachlin et al. We provide a restatement of probability discounting, usually limited to the case of 2-issues lotteries, in terms of rank-dependent utility, in which the utilities of the outcomes of n-issues lotteries are weighted by probabilities transformed after their transposition into time-delays. This formalism makes the typical cases of rationality in time and in risk mutually exclusive, but allows looser types of rationality. The resulting attitude toward probability and toward risk are then determined in relation to the values of the two parameters involved in the procedure of probability discounting: a parameter related to impatience and pessimism, and a parameter related to time-consistency and the separation between non-optimism and non-pessimism. A simulation illustrates these results through the characteristics of the transformation of probabilities function. |
| Keywords: | Probability discounting, Time discounting, Logarithmic time perception, Rank-dependent utility, Rationality, Attitude toward probabilities, Attitude toward risk |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-03256606 |
| By: | Michael Pfarrhofer; Anna Stelzer |
| Abstract: | We present an econometric framework that adapts tools for scenario analysis, such as variants of conditional forecasts and impulse response functions, for use with dynamic nonparametric multivariate models. We demonstrate the utility of our approach with simulated data and three real-world applications: (1) scenario-based conditional forecasts aligned with Federal Reserve stress test assumptions, measuring (2) macroeconomic risk under varying financial conditions, and (3) asymmetric effects of US-based financial shocks and their international spillovers. Our results indicate the importance of nonlinearities and asymmetries in dynamic relationships between macroeconomic and financial variables. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.08440 |
| By: | Daniele Angelini; Fabrizio Di Sciorio |
| Abstract: | Implied volatility IV is a key metric in financial markets, reflecting market expectations of future price fluctuations. Research has explored IV's relationship with moneyness, focusing on its connection to the implied Hurst exponent H. Our study reveals that H approaches 1/2 when moneyness equals 1, marking a critical point in market efficiency expectations. We developed an IV model that integrates H to capture these dynamics more effectively. This model considers the interaction between H and the underlying-to-strike price ratio S/K, crucial for capturing IV variations based on moneyness. Using Optuna optimization across multiple indexes, the model outperformed SABR and fSABR in accuracy. This approach provides a more detailed representation of market expectations and IV-H dynamics, improving options pricing and volatility forecasting while enhancing theoretical and pratcical financial analysis. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.07518 |
| By: | Andrea Di Giovan Paolo; Jose Higueras |
| Abstract: | This paper examines the equilibrium effects of insurance contracts on healthcare markets using a mechanism design framework. A population of risk-averse agents with preferences as in Yaari (1987) face the risk of developing an illness of unknown severity, which can be treated in a competitive hospital services market at the prevailing market price. After privately observing their health risk, but before learning their sickness level, agents have the option to purchase insurance from a monopolistic provider. Insurance contracts specify premiums, out-of-pocket costs (OPCs), and hospital service coverage, thus determining demand and price in the downstream hospital market through a market-clearing condition. Our first main result shows that optimal insurance contracts take a simple form: agents can choose between full hospital coverage with a high OPC or restricted coverage with a low OPC. This highlights a novel form of under-insurance (rationing or restricted access to healthcare services) emerging purely due to the insurer's attempt to control his price impact. Our second key result illustrates the nuanced effect of price impact on the amount of insurance provided. Higher healthcare prices increase insurer payouts but also worsen agents' outside options, making them more willing to pay for insurance ex ante. The net effect of these forces determines whether insurance provision exceeds or falls short of a price-taking benchmark. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.01780 |
| By: | Moustapha Pemy; Na Zhang |
| Abstract: | This paper studies the ubiquitous problem of liquidating large quantities of highly correlated stocks, a task frequently encountered by institutional investors and proprietary trading firms. Traditional methods in this setting suffer from the curse of dimensionality, making them impractical for high-dimensional problems. In this work, we propose a novel method based on stochastic optimal control to optimally tackle this complex multidimensional problem. The proposed method minimizes the overall execution shortfall of highly correlated stocks using a reinforcement learning approach. We rigorously establish the convergence of our optimal trading strategy and present an implementation of our algorithm using intra-day market data. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.07868 |