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on Risk Management |
| By: | Yuming Ma; Shintaro Sengoku; Kazuhide Nakata |
| Abstract: | For quantitative trading risk management purposes, we present a novel idea: the realized local volatility surface. Concisely, it stands for the conditional expected volatility when sudden market behaviors of the underlying occur. One is able to explore risk management usages by following the orthotical Delta-Gamma dynamic hedging framework. The realized local volatility surface is, mathematically, a generalized Wiener measure from historical prices. It is reconstructed via employing high-frequency trading market data. A Stick-Breaking Gaussian Mixture Model is fitted via Hamiltonian Monte Carlo, producing a local volatility surface with 95% credible intervals. A practically validated Bayesian nonparametric estimation workflow. Empirical results on TSLA high-frequency data illustrate its ability to capture counterfactual volatility. We also discuss its application in improving volatility-based risk management. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15626 |
| By: | Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Komotini, Greece); Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); 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) |
| Abstract: | This paper forecasts monthly cross-sectional realized variance (RV) for U.S. equities across 49 industries and all 50 states. We exploit information in both own-market and cross-market (oil) realized moments (semi-variance, leverage, skewness, kurtosis, and upside and downside tail risk) as predictors. To accommodate cross-sectional dependence, we compare standard econometric panel models with machine-learning approaches and introduce a new machine-learning technique tailored specifically to panel data. Using observations from April 1994 through April 2023, the panel-dedicated machine-learning model consistently outperforms all other methods, while oil-related moments add little incremental predictive power beyond own-market moments. Short-horizon forecasts successfully capture immediate shocks, whereas longer-horizon forecasts reflect broader structural economic changes. These results carry important implications for portfolio allocation and risk management. |
| Keywords: | Cross-sectional realized variance, Realized moments, Machine learning, Forecasting |
| JEL: | C33 C53 G10 G17 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202518 |
| By: | Jingyi Wei; Steve Yang; Zhenyu Cui |
| Abstract: | In this study, we propose a novel integrated Generalized Autoregressive Conditional Heteroskedasticity-Gated Recurrent Unit (GARCH-GRU) model for financial volatility modeling and forecasting. The model embeds the GARCH(1, 1) formulation directly into the GRU cell architecture, yielding a unified recurrent unit that jointly captures both traditional econometric properties and complex temporal dynamics. This hybrid structure leverages the strengths of GARCH in modeling key stylized facts of financial volatility, such as clustering and persistence, while utilizing the GRU's capacity to learn nonlinear dependencies from sequential data. Compared to the GARCH-LSTM counterpart, the GARCH-GRU model demonstrates superior computational efficiency, requiring significantly less training time, while maintaining and improving forecasting accuracy. Empirical evaluation across multiple financial datasets confirms the model's robust outperformance in terms of mean squared error (MSE) and mean absolute error (MAE) relative to a range of benchmarks, including standard neural networks, alternative hybrid architectures, and classical GARCH-type models. As an application, we compute Value-at-Risk (VaR) using the model's volatility forecasts and observe lower violation ratios, further validating the predictive reliability of the proposed framework in practical risk management settings. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.09380 |
| By: | Bonesini, Ofelia; Jacquier, Antoine; Lacombe, Chloé |
| Abstract: | We provide a thorough analysis of the path-dependent volatility model introduced by Guyon [30], proving existence and uniqueness of a strong solution, characterising its behaviour at boundary points, providing asymptotic closed-form option prices as well as deriving small-time behaviour estimates. |
| Keywords: | path-dependent volatility; large deviations; boundary classification; ergodicity; implied volatility |
| JEL: | F3 G3 |
| Date: | 2025–06–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127342 |
| By: | Fabienne Schmid; Daniel Oeltz |
| Abstract: | We present a robust Deep Hedging framework for the pricing and hedging of option portfolios that significantly improves training efficiency and model robustness. In particular, we propose a neural model for training model embeddings which utilizes the paths of several advanced equity option models with stochastic volatility in order to learn the relationships that exist between hedging strategies. A key advantage of the proposed method is its ability to rapidly and reliably adapt to new market regimes through the recalibration of a low-dimensional embedding vector, rather than retraining the entire network. Moreover, we examine the observed Profit and Loss distributions on the parameter space of the models used to learn the embeddings. The results show that the proposed framework works well with data generated by complex models and can serve as a construction basis for an efficient and robust simulation tool for the systematic development of an entirely model-independent hedging strategy. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16436 |
| By: | Giacomo Zelbi; Leonardo Niccol\`o Ialongo; Stefan Thurner |
| Abstract: | The networked nature of supply chains makes them susceptible to systemic risk, where local firm failures can propagate through firm interdependencies that can lead to cascading supply chain disruptions. The systemic risk of supply chains can be quantified and is closely related to the topology and dynamics of supply chain networks (SCN). How different network properties contribute to this risk remains unclear. Here, we ask whether systemic risk can be significantly reduced by strategically rewiring supplier-customer links. In doing so, we understand the role of specific endogenously emerged network structures and to what extent the observed systemic risk is a result of fundamental properties of the dynamical system. We minimize systemic risk through rewiring by employing a method from statistical physics that respects firm-level constraints to production. Analyzing six specific subnetworks of the national SCNs of Ecuador and Hungary, we demonstrate that systemic risk can be considerably mitigated by 16-50% without reducing the production output of firms. A comparison of network properties before and after rewiring reveals that this risk reduction is achieved by changing the connectivity in non-trivial ways. These results suggest that actual SCN topologies carry unnecessarily high levels of systemic risk. We discuss the possibility of devising policies to reduce systemic risk through minimal, targeted interventions in supply chain networks through market-based incentives. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.12955 |
| By: | Xiyue Han; Alexander Schied |
| Abstract: | We provide a simple and straightforward approach to a continuous-time version of Cover's universal portfolio strategies within the model-free context of F\"ollmer's pathwise It\^o calculus. We establish the existence of the universal portfolio strategy and prove that its portfolio value process is the average of all values of constant rebalanced strategies. This result relies on a systematic comparison between two alternative descriptions of self-financing trading strategies within pathwise It\^o calculus. We moreover provide a comparison result for the performance and the realized volatility and variance of constant rebalanced portfolio strategies. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.11881 |
| By: | Matt Darst; Sotirios Kokas; Alexandros Kontonikas; José-Luis Peydró; Alexandros Vardoulakis |
| Abstract: | We show that the effectiveness of unconventional monetary policy is limited by how banks adjust credit supply and manage liquidity risk in response to fragile non-bank funding. For identification, we use granular U.S. administrative data on deposit accounts and loan-level commitments, matched with bank-firm supervisory balance sheets. Quantitative easing increases bank fragility by triggering a large inflow of uninsured deposits from non-bank financial institutions. In response, banks that are more exposed to this fragility actively manage their liquidity risk by offering better rates to insured deposits, while cutting uninsured rates. Doing so, they shift away from uninsured to insured deposits. Importantly, on the asset side, these banks also reduce the supply of contingent credit lines to corporate clients. This tightening of liquidity provision has real effects, as firms reliant on more exposed banks experience a reduction in liquidity insurance stemming from credit lines, leading to lower investment. Our analysis reveals that the fragility of deposit funding can disrupt the complementarity between deposit-taking and the provision of credit lines. |
| Keywords: | Bank fragility; Liquidity risk; Liquidity Insurance; Deposits; Credit lines; Quantitative Easing; Quantitative Tightening; Non-banks |
| Date: | 2025–04–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-30 |
| By: | Yingli Wang; Zhenyu Cui |
| Abstract: | This paper establishes a novel link between nearly unstable heavy-tailed integer-valued autoregressive (INAR) processes and the rough Heston model via discrete scaling limits. We prove that a sequence of bivariate cumulative INAR($\infty$) processes converge in law to the rough Heston model under appropriate scaling conditions, providing a rigorous mathematical foundation for understanding how microstructural order flow drives rough volatility dynamics. Our theoretical framework extends the scaling limit techniques from Hawkes processes to the INAR($\infty$) setting. Thus we can carry out Monte Carlo simulation of the rough Heston model through simulating the corresponding approximating INAR($\infty$) processes. Extensive numerical experiments illustrate the improved accuracy and efficiency of the proposed simulation method as compared to the literature, in the pricing of not only European options, but also path-dependent options such as arithmetic Asian options, lookback options and barrier options. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.18259 |
| By: | Savita Pareek; Sujit K. Ghosh |
| Abstract: | The mean-variance portfolio model, based on the risk-return trade-off for optimal asset allocation, remains foundational in portfolio optimization. However, its reliance on restrictive assumptions about asset return distributions limits its applicability to real-world data. Parametric copula structures provide a novel way to overcome these limitations by accounting for asymmetry, heavy tails, and time-varying dependencies. Existing methods have been shown to rely on fixed or static dependence structures, thus overlooking the dynamic nature of the financial market. In this study, a semiparametric model is proposed that combines non-parametrically estimated copulas with parametrically estimated marginals to allow all parameters to dynamically evolve over time. A novel framework was developed that integrates time-varying dependence modeling with flexible empirical beta copula structures. Marginal distributions were modeled using the Skewed Generalized T family. This effectively captures asymmetry and heavy tails and makes the model suitable for predictive inferences in real world scenarios. Furthermore, the model was applied to rolling windows of financial returns from the USA, India and Hong Kong economies to understand the influence of dynamic market conditions. The approach addresses the limitations of models that rely on parametric assumptions. By accounting for asymmetry, heavy tails, and cross-correlated asset prices, the proposed method offers a robust solution for optimizing diverse portfolios in an interconnected financial market. Through adaptive modeling, it allows for better management of risk and return across varying economic conditions, leading to more efficient asset allocation and improved portfolio performance. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.12266 |
| By: | Markus Bibinger; Jun Yu; Chen Zhang |
| Abstract: | A multivariate fractional Brownian motion (mfBm) with component-wise Hurst exponents is used to model and forecast realized volatility. We investigate the interplay between correlation coefficients and Hurst exponents and propose a novel estimation method for all model parameters, establishing consistency and asymptotic normality of the estimators. Additionally, we develop a time-reversibility test, which is typically not rejected by real volatility data. When the data-generating process is a time-reversible mfBm, we derive optimal forecasting formulae and analyze their properties. A key insight is that an mfBm with different Hurst exponents and non-zero correlations can reduce forecasting errors compared to a one-dimensional model. Consistent with optimal forecasting theory, out-of-sample forecasts using the time-reversible mfBm show improvements over univariate fBm, particularly when the estimated Hurst exponents differ significantly. Empirical results demonstrate that mfBm-based forecasts outperform the (vector) HAR model. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15985 |
| By: | Fang, Dawei (Department of Economics, School of Business, Economics and Law, Göteborg University); Ke, Changxia (School of Economics and Finance, Queensland University of Technology); Kubitz, Greg (School of Economics and Finance, Queensland University of Technology); Liu, Yang (Faculty of Business and Economics, University of Melbourne); Noe, Thomas (Saıd Business School & Balliol College, University of Oxford); Page, Lionel (School of Economics, University of Queensland) |
| Abstract: | Risk-taking spurred by rank-based contest rewards can have enormous consequences, from breakthrough innovations in research competitions to hedge fund collapses engendered by risky bets aimed at raising league-table rankings. This paper provides a novel theoretical and experimental framework of rank-motivated risk-taking that both allows for complex prize structures and permits participants to make arbitrary mean-preserving changes to their random performance. As predicted by our theory, participants choose positively skewed performance under highly convex prize schedules and negatively skewed performance under concave ones. Convexifying the prize schedule or increasing competition for identical winner prizes induces riskier and more skewed performance. |
| Keywords: | rank incentives; risk taking; skewness; contest structure |
| JEL: | C72 C91 D74 D81 |
| Date: | 2025–05–07 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:gunwpe:0855 |
| By: | Matteo Orlandini (Université Côte d'Azur, CNRS, GREDEG, France; Institute of Economics, Scuola Superiore Sant'Anna, Italy); Sebastiano Michele Zema (Scuola Normale Superiore, Italy); Mauro Napoletano (Université Côte d'Azur, CNRS, GREDEG, France; Sciences Po, OFCE, France; Institute of Economics, Scuola Superiore Sant'Anna, Italy); Giorgio Fagiolo (Institute of Economics, Scuola Superiore Sant'Anna, Italy) |
| Abstract: | The node degree distribution of an inferred financial network is often characterized by a small number of nodes with a large number of connections and many nodes with few connections. To date, there is no empirical evidence on how this stylized statistical fact can be useful in predicting fluctuations of financial assets. In this paper, we explore this possibility by modifying well-known time-series models and augmenting them with covariates from a reconstructed network, selecting nodes that are identified as the most connected to the index of interest. We then analyze the out-of-sample performance of these models across different volatility proxies. The results show that nodes belonging to the right tail of the degree distribution possess high predictive power over financial aggregates, independently of the volatility measure used. Our findings suggest that incorporating the topological information that arises from this statistical regularity in financial networks can enhance the accuracy of traditional predictive models. |
| Keywords: | Volatility forecasting, Network-augmented models, Cross-border volatility spillovers, Equity indexes |
| JEL: | G17 G11 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-19 |
| By: | Charles I. Jones |
| Abstract: | During the Covid-19 pandemic, the United States effectively “spent” about 4 percent of GDP — via reduced economic activity — to address a mortality risk of roughly 0.3 percent. Many experts believe that catastrophic risks from advanced A.I. over the next decade are at least this large, suggesting that a comparable mitigation investment could be worthwhile. Existing lives are valued by policymakers at around $10 million each in the United States. To avoid a 1% mortality risk, this value implies a willingness to pay of $100, 000 per person — more than 100% of per capita GDP. If the risk is realized over the next two decades, an annual investment of 5% of GDP toward mitigating catastrophic risk could be justified, depending on the effectiveness of such investment. This back-of-the-envelope intuition is supported by the model developed here. In the model, for most of the scenarios and parameter combinations considered, spending at least 1% of GDP annually to mitigate AI risk can be justified even without placing any value on the welfare of future generations. |
| JEL: | O40 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33602 |
| By: | Marco Migueis; Sydney Peirce |
| Abstract: | This study assesses whether the introduction of the GSIB surcharge requirement resulted in GSIBs reducing the systemic risk posed by their activities. We find limited evidence of GSIBs managing their activities to avoid increases in their surcharges. For a sample of international banks, proximity to surcharge thresholds is associated to a decrease in the growth of intra-financial system liabilities, underwriting activities, and holdings of trading and available-for-sale securities. In the case of US GSIBs and the method 2 GSIB surcharge, we find some association between proximity to surcharge thresholds and a decrease in the growth of trading and available-for-sale securities and short-term wholesale funding. |
| Keywords: | Bank capital requirements; Banking regulation; GSIB surcharge; Systemic risk |
| JEL: | G01 G18 G21 |
| Date: | 2025–04–16 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-29 |
| By: | Xiyue Han; Alexander Schied |
| Abstract: | In [8], easily computable scale-invariant estimator $\widehat{\mathscr{R}}^s_n$ was constructed to estimate the Hurst parameter of the drifted fractional Brownian motion $X$ from its antiderivative. This paper extends this convergence result by proving that $\widehat{\mathscr{R}}^s_n$ also consistently estimates the Hurst parameter when applied to the antiderivative of $g \circ X$ for a general nonlinear function $g$. We also establish an almost sure rate of convergence in this general setting. Our result applies, in particular, to the estimation of the Hurst parameter of a wide class of rough stochastic volatility models from discrete observations of the integrated variance, including the fractional stochastic volatility model. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.09276 |
| By: | Brignone, Davide (Bank of England); Gambetti, Luca (Universitat Autònoma de Barcelona, BSE, Università di Torino and CCA); Ricci, Martino (European Central Bank) |
| Abstract: | In this paper, we investigate the economic effects of geopolitical risk (GPR) shocks, with a focus on non‑linear transmission mechanisms. Using a VARX framework, we show that larger positive shocks have a disproportionately greater impact, pointing to the existence of an amplification channel driven by rising uncertainty. Large GPR shocks trigger precautionary behaviours, leading to sharp declines in consumption and equity prices. In contrast, prices react positively but the responses are overall muted due to offsetting forces from reduced demand and heightened uncertainty. We further show that GPR shocks linked to anticipated geopolitical threats exhibit pronounced non‑linearities, significantly increasing oil prices and inflation expectations, thereby exerting upward pressure on domestic prices. |
| Keywords: | Geopolitical risk; non-linearities; inflation; vector autoregressions; uncertainty |
| JEL: | C30 D80 E32 F44 H56 |
| Date: | 2025–02–21 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1118 |
| By: | Dror, David Mark |
| Abstract: | This paper develops a rigorous mathematical framework for analyzing trust dynamics and statistical properties in small-scale risk-sharing communities. We establish that small pools with interdependent risks exhibit fundamentally different mathematical properties than large insurance systems, with volatility exceeding stability thresholds by a factor of √(N/Nc) and correlation structures reducing effective pool size by up to 89%. We formalize trust as a mathematically tractable variable with threshold stability properties, proving the existence of critical values TRcritical ∈ [0.65, 0.75] that create bifurcation points in system behavior. Our mathematical analysis demonstrates that network density directly determines trust propagation speed according to precise mathematical relationships. We prove that trust response exhibits asymmetric properties, with negative experiences having 1.5-2.5 times stronger impact than positive experiences of equal magnitude, creating hysteresis effects in system stability. By developing differential equations governing trust evolution and applying network diffusion models, we establish exact conditions for system stability and characterize phase transitions under parameter variation. The mathematical framework enables precise quantification of correlation penalties, network effects, and trust thresholds with applications to community-based risk-sharing systems where conventional statistical approaches fail. Our results transform qualitative concepts of trust and social capital into quantifiable mathematical variables with specific dynamics and stability properties. |
| Abstract: | Diese Arbeit entwickelt ein rigoroses mathematisches Rahmenwerk zur Analyse von Vertrauensdynamiken und statistischen Eigenschaften in kleinskaligen Risikoausgleichsgemeinschaften. Wir zeigen, dass kleine Risikopools mit interdependenten Risiken grundlegend andere mathematische Eigenschaften aufweisen als groß angelegte Versicherungssysteme: Die Volatilität überschreitet die Stabilitätsschwellen um den Faktor √(N/Nc), und Korrelationsstrukturen reduzieren die effektive Poolgröße um bis zu 89 %. Wir formalisieren Vertrauen als mathematisch behandelbare Variable mit stabilitätstheoretischen Schwellenwerten und weisen die Existenz kritischer Werte TR_critical ∈ [0, 65; 0, 75] nach, die als Bifurkationspunkte im Systemverhalten fungieren. Unsere mathematische Analyse zeigt, dass die Netzwerkkonnektivität die Geschwindigkeit der Vertrauensausbreitung direkt bestimmt, gemäß exakt ableitbaren mathematischen Beziehungen. Es wird nachgewiesen, dass die Vertrauensreaktion asymmetrisch verläuft: Negative Erfahrungen wirken sich 1, 5- bis 2, 5-mal stärker aus als gleichwertige positive Erlebnisse, was Hystereseeffekte in der Systemstabilität verursacht. Durch die Entwicklung von Differentialgleichungen zur Beschreibung der Vertrauensentwicklung sowie die Anwendung von Netzwerkdiffusionsmodellen definieren wir exakte Stabilitätsbedingungen und charakterisieren Phasenübergänge bei Parameterveränderungen. Das mathematische Rahmenwerk ermöglicht eine präzise Quantifizierung von Korrelationsverlusten, Netzwerkeffekten und Vertrauensschwellen – insbesondere in gemeinschaftsbasierten Risikoausgleichssystemen, in denen konventionelle statistische Verfahren versagen. Unsere Ergebnisse überführen qualitative Konzepte wie Vertrauen und Sozialkapital in quantifizierbare mathematische Variablen mit spezifischen Dynamiken und Stabilitätseigenschaften. |
| Keywords: | trust dynamics, small risk pools, network diffusion, copula theory, correlation structures, threshold stability |
| JEL: | Z13 G22 D85 C46 C58 C22 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:316140 |
| By: | Youngbin Lee; Yejin Kim; Suin Kim; Yongjae Lee |
| Abstract: | Portfolio optimization faces challenges due to the sensitivity in traditional mean-variance models. The Black-Litterman model mitigates this by integrating investor views, but defining these views remains difficult. This study explores the integration of large language models (LLMs) generated views into portfolio optimization using the Black-Litterman framework. Our method leverages LLMs to estimate expected stock returns from historical prices and company metadata, incorporating uncertainty through the variance in predictions. We conduct a backtest of the LLM-optimized portfolios from June 2024 to February 2025, rebalancing biweekly using the previous two weeks of price data. As baselines, we compare against the S&P 500, an equal-weighted portfolio, and a traditional mean-variance optimized portfolio constructed using the same set of stocks. Empirical results suggest that different LLMs exhibit varying levels of predictive optimism and confidence stability, which impact portfolio performance. The source code and data are available at https://github.com/youngandbin/LLM-MVO-B LM. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.14345 |
| By: | Felix Fie{\ss}inger; Mitja Stadje |
| Abstract: | We adapt Leland's dynamic capital structure model to the context of an insurance company selling participating life insurance contracts explaining the existence of life insurance contracts which provide both a guaranteed payment and surplus participation to the policyholders. Our derivation of the optimal participation rate reveals its pronounced sensitivity to the contract duration and the associated tax rate. Moreover, the asset substitution effect, which describes the tendency of equity holders to increase the riskiness of a company's investment decisions, decreases when adding surplus participation. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.12851 |
| By: | Morando, Alberto |
| Abstract: | As new automated features enter the automotive market, we need methods to assess their safety in a rapid, proactive, and iterative way. The traditional way of relying on crash statistics does not meet these needs. An alternative is to use extrapolation techniques designed to deal with rare events, such as extreme value theory (EVT). In this paper, we applied EVT to estimate the risk of collision with and without adaptive cruise control (ACC) during steady-state car following. We defined a Bayesian regression model to estimate the parameters of the Weibull distribution for block maxima (BM) of the brake threat number (BTN). We used a small, open-access dataset collected during a platooning experiment on a test track, with and without ACC. We found that ACC has extremely low probability to end up in a rear-end crash under normal car following circumstances. Although there is a expectation that ACC is generally safer than manual driving, we found that the relative risk of ACC was higher than the human control baseline in the dataset. The reason is that the manual control baseline represented a cautious driving style, which may not be typical in real traffic. Nonetheless, EVT can be used to measure the expected safety benefit of a vehicle system without requiring a large dataset. BTN was the appropriate safety metric to compare automated and manual driving mode as it accounts for specific brake behavior and performance. |
| Date: | 2025–01–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:hnzpw_v1 |
| By: | Ravi Jagannathan; Iwan Meier; Valeri Sokolovski |
| Abstract: | Between 2016 and 2023, the top 10% of carbon-emission-intensive firms (heavy emitters) accounted for over 90% of all Scope 1 emissions from U.S. public companies. We observe that about 35% of the market capitalization of ‘Value’ portfolios, compared to 5% of ‘Growth’ portfolios, regardless of how Value and Growth are defined, was comprised of heavy emitters. When we split the Big Value portfolio into heavy- and light-emitter stocks, we find that these two portfolios had similar realized (raw and risk-adjusted) returns and expected returns, as measured by Implied Cost of Capital, suggesting limited incremental compensation for transition risk. We also find that Big Growth low-emitter stocks consistently had lower expected returns than Big Value low-emitter stocks, with the spread widening in recent years, despite similar emission levels. This indicates that factors beyond climate concerns are necessary to fully explain the superior performance of Growth stocks relative to Value stocks over the past decade. |
| JEL: | G1 G11 G12 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33535 |
| By: | Brewer, Mike; Cominetti, Nye; Jenkins, Stephen P. |
| Abstract: | This report uses a newly available dataset – payroll data held by HM Revenue and Customs on over 250, 000 working-age people covering April 2014 to March 2019 – to look at monthly and weekly volatility in employee pre-tax earnings. It is one of a very few UK studies to look at high-frequency earnings volatility on a large scale, and the first do so on a sample that is representative of the population of employees in the UK. Earnings volatility will not pose problems for all workers (for example, if erratic earnings are the minority of a household’s income, or if they are the side effect of being able to take shifts that fit around other parts of a worker’s life). But unpredictable earnings can mean financial stress, difficulty planning for the future, and increased reliance on credit or social support. So understanding earnings volatility is crucial for building fairer labour markets, effective social policies, and financial security in an uncertain world. |
| JEL: | R14 J01 |
| Date: | 2025–03–04 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127596 |
| By: | Katsuhito Nohara; Akira Hibiki; Shinsuke Uchida; Jun Yoshida |
| Abstract: | In recent years, the intensification of disasters associated with climate change has posed an increasing threat to agricultural production. Especially, damage to agricultural products caused by disasters can be a significant source of perceived management risk, potentially affecting future farm operations. Meanwhile, adaptation strategies may offer farmers a viable approach to mitigating the management risks associated with the intensification of climate-related disasters. Accordingly, this study employs structural equation modeling to clarify how experiences with past disasters and the resulting perception of management risks affect farmers’ decisions to adopt adaptation strategies. Furthermore, the characteristics of farmers who have adoptedadaptation measures are analyzed using the New Ecological Paradigm scale. The results of this study suggest that it is important to understand in detail the factors that influence farmers' decisions, as well as their attributes and regional characteristics, in order to promote the adoption of adaptation measures. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:toh:tupdaa:70 |
| By: | Zhe Fei; Scott Robertson |
| Abstract: | We consider an equity market subject to risk from both unhedgeable shocks and default. The novelty of our work is that to partially offset default risk, investors may dynamically trade in a credit default swap (CDS) market. Assuming investment opportunities are driven by functions of an underlying diffusive factor process, we identify the certainty equivalent for a constant absolute risk aversion investor with a semi-linear partial differential equation (PDE) which has quadratic growth in both the function and gradient coefficients. For general model specifications, we prove existence of a solution to the PDE which is also the certainty equivalent. We show the optimal policy in the CDS market covers not only equity losses upon default (as one would expect), but also losses due to restricted future trading opportunities. We use our results to price default dependent claims though the principal of utility indifference, and we show that provided the underlying equity market is complete absent the possibility of default, the equity-CDS market is complete accounting for default. Lastly, through a numerical application, we show the optimal CDS policies are essentially static (and hence easily implementable) and that investing in CDS dramatically increases investor indirect utility. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.08085 |
| By: | Camilo Gómez; Carlos Andrés Quicazán-Moreno; Hernando Vargas-Herrera |
| Abstract: | Following the pandemic, the Colombia’s financial system experienced a pronounced credit cycle, with significant real growth in consumer loans followed by a deceleration from late 2022. This paper uses granular loan-level data to analyse how financial intermediaries adjusted the credit risk composition of new loans throughout this cycle. It examines the implications of these shifts for loan supply dynamics and financial conditions. Additionally, the study explores the interaction between credit risk composition and monetary policy transmission during the 2021–24 period. As monetary tightening led to rising lending rates, changes in loan composition—particularly the increased share of riskier borrowers—amplified the observed transmission of policy rates to average lending costs, especially in the consumer credit segment. The findings highlight the importance of credit risk dynamics in assessing monetary policy effectiveness and demonstrate the value of disaggregated data in understanding macro-financial conditions. *****RESUMEN: Tras la pandemia, el sistema financiero de Colombia experimentó un marcado ciclo de crédito, con un significativo crecimiento real en los préstamos de consumo, seguido de una desaceleración a partir de finales de 2022. Este estudio utiliza datos granulares a nivel de préstamo para analizar cómo los intermediarios financieros ajustaron la composición del riesgo crediticio en los nuevos préstamos a lo largo de este ciclo. Se examinan las implicaciones de estos cambios en la dinámica de la oferta de crédito y las condiciones financieras. Además, el estudio explora la interacción entre la composición del crédito y la transmisión de la política monetaria durante el período 2021–24. Dado que el endurecimiento monetario elevó las tasas de interés de los préstamos, los cambios en la composición del crédito—particularmente el aumento en la participación de prestatarios más riesgosos—amplificaron la transmisión de las tasas de política a los costos de financiamiento promedio, especialmente en el segmento de crédito de consumo. Los resultados destacan la importancia de la dinámica del riesgo crediticio en la evaluación de la efectividad de la política monetaria y demuestran el valor de los datos desagregados para comprender las condiciones macrofinancieras de la economía. |
| Keywords: | Monetary Policy Transmission, Credit Cycle, Loan Composition, Risk Taking, Transmisión de la política monetaria, Ciclo de crédito, Composición de la cartera, Toma de riesgo |
| JEL: | E43 E52 G21 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bdr:borrec:1313 |
| By: | Afees A. Salisu (Centre for Econometrics and Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Ahamuefula E. Ogbonna (Centre for Econometrics and Applied Research, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | We evaluate the predictive ability of the newly developed climate-related migration uncertainty index (CMUI) and its two components, the climate uncertainty index (CUI) and the migration uncertainty index (MUI), for the return volatility of agricultural commodity prices in both futures and spot markets. Employing a GARCH-MIDAS model, based on mixed data frequencies covering the period from 1977Q4 (with the earliest daily observation on October 3, 1977) to 2024Q1 (with the latest daily observation on March 29, 2024), we conduct both statistical and economic evaluations, including the Modified Diebold-Mariano test, Model Confidence Set procedure, and risk-adjusted performance metrics. The results demonstrate that integrating CUI, MUI, and CMUI into the predictive model of the return volatility of agricultural commodity prices significantly improves forecast accuracy relative to the conventional GARCH-MIDAS-RV benchmark. These findings suggest that the climate and migration related uncertainty indices are both statistically significant and economically relevant, offering enhanced predictive power and investment performance. |
| Keywords: | Climate-related Migration Uncertainty Index, Climate Uncertainty Index, Migration Uncertainty Index, Agricultural commodity prices, GARCH-MIDAS, Forecast evaluation, Economic Significance |
| JEL: | C53 D8 F22 Q02 Q13 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202516 |
| By: | Martina Halouskov\'a; \v{S}tefan Ly\'ocsa |
| Abstract: | Macroeconomic variables are known to significantly impact equity markets, but their predictive power for price fluctuations has been underexplored due to challenges such as infrequency and variability in timing of announcements, changing market expectations, and the gradual pricing in of news. To address these concerns, we estimate the public's attention and sentiment towards ten scheduled macroeconomic variables using social media, news articles, information consumption data, and a search engine. We use standard and machine-learning methods and show that we are able to improve volatility forecasts for almost all 404 major U.S. stocks in our sample. Models that use sentiment to macroeconomic announcements consistently improve volatility forecasts across all economic sectors, with the greatest improvement of 14.99% on average against the benchmark method - on days of extreme price variation. The magnitude of improvements varies with the data source used to estimate attention and sentiment, and is found within machine-learning models. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.19767 |
| By: | Zongxiao Wu; Yizhe Dong; Yaoyiran Li; Baofeng Shi |
| Abstract: | This study explores the integration of a representative large language model, ChatGPT, into lending decision-making with a focus on credit default prediction. Specifically, we use ChatGPT to analyse and interpret loan assessments written by loan officers and generate refined versions of these texts. Our comparative analysis reveals significant differences between generative artificial intelligence (AI)-refined and human-written texts in terms of text length, semantic similarity, and linguistic representations. Using deep learning techniques, we show that incorporating unstructured text data, particularly ChatGPT-refined texts, alongside conventional structured data significantly enhances credit default predictions. Furthermore, we demonstrate how the contents of both human-written and ChatGPT-refined assessments contribute to the models' prediction and show that the effect of essential words is highly context-dependent. Moreover, we find that ChatGPT's analysis of borrower delinquency contributes the most to improving predictive accuracy. We also evaluate the business impact of the models based on human-written and ChatGPT-refined texts, and find that, in most cases, the latter yields higher profitability than the former. This study provides valuable insights into the transformative potential of generative AI in financial services. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.18029 |
| By: | Hiroshi Oishi (Bank of Japan); Eisuke Kobayashi (Bank of Japan); Yoshihiko Sugihara (Bank of Japan) |
| Abstract: | Non-Bank Financial Intermediation (NBFI) accounts for about half of global financial assets and plays a pivotal role in financial intermediation activities. Recent observations have indicated a marked shift in the composition of NBFI, with a notable increase in the presence of investment funds, contrasting with the traditional composition of NBFI, which comprises insurance corporations and pension funds. The interconnectedness between banks and NBFI is also viewed to be increasing, mainly through increases in their cross-border transactions. Consequently, there has been a rise in cases where responses of NBFI, particularly investment funds, have been considered as contributing to the amplification of financial stresses, leading to heightened financial market volatility. In view of the potential for such stresses to propagate throughout the global financial system, including the banking sector, a series of policy recommendations have been issued by the Financial Stability Board (FSB) to contain vulnerabilities of NBFI. This article presents an overview of NBFI's financial intermediation, summarizes policy initiatives designed to enhance its resilience, and provides perspectives on issues surrounding NBFI through a review of the FSB's regular issue of the "Global Monitoring Report on Non-Bank Financial Intermediation" and its policy recommendations. |
| Keywords: | non-bank financial institutions; financial intermediation; financial risk and risk management; government policy and regulation |
| JEL: | G23 G15 G32 G28 |
| Date: | 2025–05–15 |
| URL: | https://d.repec.org/n?u=RePEc:boj:bojrev:rev25e06 |
| By: | Dario Crisci; Sebastian E. Ferrando; Konrad Gajewski |
| Abstract: | An agent-based modelling methodology for the joint price evolution of two stocks is put forward. The method models future multidimensional price trajectories reflecting how a class of agents rebalance their portfolios in an operational way by reacting to how stocks' charts unfold. Prices are expressed in units of a third stock that acts as numeraire. The methodology is robust, in particular, it does not depend on any prior probability or analytical assumptions and it is based on constructing scenarios/trajectories. A main ingredient is a superhedging interpretation that provides relative superhedging prices between the two modelled stocks. The operational nature of the methodology gives objective conditions for the validity of the model and so implies realistic risk-rewards profiles for the agent's operations. Superhedging computations are performed with a dynamic programming algorithm deployed on a graph data structure. Null subsets of the trajectory space are directly related to arbitrage opportunities (i.e. there is no need for probabilistic considerations) that may emerge during the trajectory set construction. It follows that the superhedging algorithm handles null sets in a rigorous and intuitive way. Superhedging and underhedging bounds are kept relevant to the investor by means of a worst case pruning method and, as an alternative, a theory supported pruning that relies on a new notion of small arbitrage. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.18165 |
| By: | Bruno Bouchard; Xiaolu Tan |
| Abstract: | We provide an extension of the unbiased simulation method for SDEs developed in Henry-Labordere et al. [Ann Appl Probab. 27:6 (2017) 1-37] to a class of path-dependent dynamics, pertaining for Asian options. In our setting, both the payoff and the SDE's coefficients depend on the (weighted) average of the process or, more precisely, on the integral of the solution to the SDE against a continuous function with bounded variations. In particular, this applies to the numerical resolution of the class of path-dependent PDEs whose regularity, in the sens of Dupire, is studied in Bouchard and Tan [Ann. I.H.P., to appear]. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16349 |