|
on Risk Management |
| By: | Lukas Bauer |
| Abstract: | This paper provides comprehensive simulation results on the finite sample properties of the Diebold-Mariano (DM) test by Diebold and Mariano (1995) and the model confidence set (MCS) testing procedure by Hansen et al. (2011) applied to the asymmetric loss functions specific to financial tail risk forecasts, such as Value-at-Risk (VaR) and Expected Shortfall (ES). We focus on statistical loss functions that are strictly consistent in the sense of Gneiting (2011a). We find that the tests show little power against models that underestimate the tail risk at the most extreme quantile levels, while the finite sample properties generally improve with the quantile level and the out-of-sample size. For the small quantile levels and out-of-sample sizes of up to two years, we observe heavily skewed test statistics and non-negligible type III errors, which implies that researchers should be cautious about using standard normal or bootstrapped critical values. We demonstrate both empirically and theoretically how these unfavorable finite sample results relate to the asymmetric loss functions and the time varying volatility inherent in financial return data. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.23333 |
| By: | Vasily Melnikov |
| Abstract: | We introduce a new paradigm for risk sharing that generalizes earlier models based on discrete agents and extends them to allow for sharing risk within a continuum of agents. Agents are represented by points of a measure space and have potentially heterogeneous risk preferences modeled by risk measures. The existence of risk minimizing allocations is proved when constrained to satisfy economically convincing conditions. In the unconstrained case, we derive the dual representation of the value function using a Strassen-type theorem for the weak-star topology. These results are illustrated by explicit formulas when risk preferences are within the family of entropic and expected shortfall risk measures. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.19276 |
| By: | Torben G. Andersen (Finance Department, Kellogg School of Management, Northwestern University); Yi Ding (Faculty of Business Administration, University of Macau); Viktor Todorov (Finance Department, Kellogg School of Management, Northwestern University) |
| Abstract: | We develop nonparametric estimates for tail risk in the cross-section of asset prices at high frequencies. We show that the tail behavior of the crosssectional return distribution depends on whether the time interval contains a systematic jump event. If so, the cross-sectional return tail is governed by the assets’ exposures to the systematic event while, otherwise, it is determined by the idiosyncratic jump tails of the stocks. We develop an estimator for the tail shape of the cross-sectional return distribution that display distinct properties with and without systematic jumps. Empirically, we provide evidence for symmetric cross-sectional return tails at high-frequency that exhibit nontrivial and persistent time series variation. A hypothesis of equal cross-sectional return tail shapes during periods with and without systematic jump events is strongly rejected by the data. |
| Keywords: | Cross-sectional return distribution, extreme value theory, highfrequency data, tail risk |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202530 |
| By: | Miguel Inverneiro; Tiago Pinheiro |
| Abstract: | Are lenders in the Portuguese financial system more likely to have large losses now than in the past? If a lender has large losses, is it more likely that another one will as well? How has that likelihood changed over time? We address these and other questions using granular credit exposure data in the period between 2009 and 2023. Our findings indicate that the risk of large losses is lower in 2023 than in 2012. Behind this result is a reduction in the borrowers’ default probabilities, a decline in the share of credit to firms accompanied by a rise in the share of credit to households and, to a more limited extent, an increase in loan recoveries. Additionally, we find that the risk of multiple lenders experiencing large losses simultaneously has decreased during the period of analysis. But, if one lender has large losses, the risk that another one will also face large losses has been rising since 2019. This result is driven by an increase in credit to sectors that have high default risk correlation. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202509 |
| By: | Carolina Nunes; Tiago Pinheiro |
| Abstract: | This paper estimates econometric models of default risk for individuals obtaining credit in Portugal using data from Banco de Portugal’s Credit Register. We estimate monthly default probabilities for mortgage and consumer loans over three, six, and twelve-month horizons. The models combine cross-sectional and time series components. The cross-sectional component captures default risk heterogeneity across individuals by relating default risk to loan and borrower characteristics. The time series component captures time variation in aggregate default risk by linking it with macroeconomic variables. Our findings indicate that the model’s performance in distinguishing between defaulting and non-defaulting borrowers is on par with or superior to existing literature. The results also show a close alignment between average default probabilities and actual default rates across various borrower characteristics and lending institutions. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202510 |
| By: | Dirk Tasche |
| Abstract: | Recalibration of binary probabilistic classifiers to a target prior probability is an important task in areas like credit risk management. We analyse methods for recalibration from a distribution shift perspective. Distribution shift assumptions linked to the area under the curve (AUC) of a probabilistic classifier are found to be useful for the design of meaningful recalibration methods. Two new methods called parametric covariate shift with posterior drift (CSPD) and ROC-based quasi moment matching (QMM) are proposed and tested together with some other methods in an example setting. The outcomes of the test suggest that the QMM methods discussed in the paper can provide appropriately conservative results in evaluations with concave functionals like for instance risk weights functions for credit risk. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.19068 |
| By: | Ruixue Jing; Ryota Kobayashi; Luis Enrique Correa Rocha |
| Abstract: | The emerging cryptocurrency market presents unique challenges for investment due to its unregulated nature and inherent volatility. However, collective price movements can be explored to maximise profits with minimal risk using investment portfolios. In this paper, we develop a technical framework that utilises historical data on daily closing prices and integrates network analysis, price forecasting, and portfolio theory to identify cryptocurrencies for building profitable portfolios under uncertainty. Our method utilises the Louvain network community algorithm and consensus clustering to detect robust and temporally stable clusters of highly correlated cryptocurrencies, from which the chosen cryptocurrencies are selected. A price prediction step using the ARIMA model guarantees that the portfolio performs well for up to 14 days in the investment horizon. Empirical analysis over a 5-year period shows that despite the high volatility in the crypto market, hidden price patterns can be effectively utilised to generate consistently profitable, time-agnostic cryptocurrency portfolios. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.24831 |
| By: | Tobias Adrian; Hongqi Chen; Max-Sebastian Dov\`i; Ji Hyung Lee |
| Abstract: | We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors, thereby isolating the downside risks emanating from each sector. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.00572 |
| By: | Frédéric Vinas |
| Abstract: | I study the impact of oil price shocks to non-financial firms over two decades using a highly granular firm-level dataset. I show the impact of these price shocks to key financial and operational metrics, including value added, employment, real wages, labor share, profit margins, dividend payments, productivity, and credit risk. I highlight the asymmetric effects of oil price increases and decreases. A one standard deviation increase in the weighted oil price shocks leads to a €396 decrease in per capita productivity (in 2024 euros), and a 0.30 percentage point increase in the probability of default, while there is no significant effect in the case of oil price decreases, leading to persistent effects of oil price increases in the medium term. I also show heterogeneous effects of oil price increases across firm size and energy intensity. This paper has implications for policymakers, especially those concerned with financial stability (bank stress-testing, climate stress-testing, macro-financial modeling), and competitiveness, and more generally for those studying climate transition risks. |
| Keywords: | Oil Shock, Oil Price, Raw Materials, Value Added, Wage Bill, Labor Share, Profit Margin, Default, Productivity, Climate Risk, Transition Risk, Physical Risk, Credit Risk |
| JEL: | D33 E32 G3 G33 G35 Q41 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:989 |
| By: | Giovanni Bonaccolto; Nicola Borri; Andrea Consiglio; Giorgio Di Giorgio |
| Abstract: | This paper investigates the dynamic interdependencies between the European insurance sector and key financial markets-equity, bond, and banking-by extending the Generalized Forecast Error Variance Decomposition framework to a broad set of performance and risk indicators. Our empirical analysis, based on a comprehensive dataset spanning January 2000 to October 2024, shows that the insurance market is not a passive receiver of external shocks but an active contributor in the propagation of systemic risk, particularly during periods of financial stress such as the subprime crisis, the European sovereign debt crisis, and the COVID-19 pandemic. Significant heterogeneity is observed across subsectors, with diversified multiline insurers and reinsurance playing key roles in shock transmission. Moreover, our granular company-level analysis reveals clusters of systemically central insurance companies, underscoring the presence of a core group that consistently exhibits high interconnectivity and influence in risk propagation. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.02635 |
| By: | Pingping Zeng; Gongqiu Zhang; Weinan Zhang |
| Abstract: | Drawdown risk, an important metric in financial risk management, poses significant computational challenges due to its highly path-dependent nature. This paper proposes a unified framework for computing five important drawdown quantities introduced in Landriault et al. (2015) and Zhang (2015) under general Markov models. We first establish linear systems and develop efficient algorithms for such problems under continuous-time Markov chains (CTMCs), and then establish their theoretical convergence to target quantities under general Markov models. Notably, the proposed algorithms for most quantities achieve the same complexity order as those for path-independent problems: cubic in the number of CTMC states for general Markov models and linear when applied to diffusion models. Rigorous convergence analysis is conducted under weak regularity conditions, and extensive numerical experiments validate the accuracy and efficiency of the proposed algorithms. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.00552 |
| By: | Lijie Ding; Egang Lu; Kin Cheung |
| Abstract: | We introduce a fast and flexible Machine Learning (ML) framework for pricing derivative products whose valuation depends on volatility surfaces. By parameterizing volatility surfaces with the 5-parameter stochastic volatility inspired (SVI) model augmented by a one-factor term structure adjustment, we first generate numerous volatility surfaces over realistic ranges for these parameters. From these synthetic market scenarios, we then compute high-accuracy valuations using conventional methodologies for two representative products: the fair strike of a variance swap and the price and Greeks of an American put. We then train the Gaussian Process Regressor (GPR) to learn the nonlinear mapping from the input risk factors, which are the volatility surface parameters, strike and interest rate, to the valuation outputs. Once trained, We use the GPR to perform out-of-sample valuations and compare the results against valuations using conventional methodologies. Our ML model achieves very accurate results of $0.5\%$ relative error for the fair strike of variance swap and $1.7\% \sim 3.5\%$ relative error for American put prices and first-order Greeks. More importantly, after training, the model computes valuations almost instantly, yielding a three to four orders of magnitude speedup over Crank-Nicolson finite-difference method for American puts, enabling real-time risk analytics, dynamic hedging and large-scale scenario analysis. Our approach is general and can be extended to other path-dependent derivative products with early-exercise features, paving the way for hybrid quantitative engines for modern financial systems. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.22957 |
| By: | Viraj Nadkarni; Pramod Viswanath |
| Abstract: | We present the first formal treatment of \emph{yield tokenization}, a mechanism that decomposes yield-bearing assets into principal and yield components to facilitate risk transfer and price discovery in decentralized finance (DeFi). We propose a model that characterizes yield token dynamics using stochastic differential equations. We derive a no-arbitrage pricing framework for yield tokens, enabling their use in hedging future yield volatility and managing interest rate risk in decentralized lending pools. Taking DeFi lending as our focus, we show how both borrowers and lenders can use yield tokens to achieve optimal hedging outcomes and mitigate exposure to adversarial interest rate manipulation. Furthermore, we design automated market makers (AMMs) that incorporate a menu of bonding curves to aggregate liquidity from participants with heterogeneous risk preferences. This leads to an efficient and incentive-compatible mechanism for trading yield tokens and yield futures. Building on these foundations, we propose a modular \textit{fixed-rate} lending protocol that synthesizes on-chain yield token markets and lending pools, enabling robust interest rate discovery and enhancing capital efficiency. Our work provides the theoretical underpinnings for risk management and fixed-income infrastructure in DeFi, offering practical mechanisms for stable and sustainable yield markets. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.22784 |
| By: | Ayush Jha; Abootaleb Shirvani; Ali Jaffri; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and solving a finite-horizon Bellman equation, we construct long-short momentum portfolios that adjust to changing ESG sentiment regimes. Unlike traditional momentum strategies based on historical returns, our approach incorporates the Stable Tail Adjusted Return ratio and Rachev ratio to better capture downside risk in turbulent markets. We apply this framework across three asset classes, Russell 3000 equities, Dow Jones~30 stocks, and cryptocurrencies, under both pro- and anti-ESG market regimes. We find that ESG-loser portfolios significantly outperform ESG-winner portfolios in pro-ESG regimes, a counterintuitive result suggesting that market overreaction to ESG sentiment creates short-term pricing inefficiencies. This pattern is robust across tail-sensitive performance metrics and is most pronounced under a two-week formation and holding period. Our framework highlights how ESG considerations and sentiment regimes alter return dynamics, offering practical guidance for investors seeking to implement responsive momentum strategies under sustainability constraints. These findings challenge conventional assumptions about ESG investing and underscore the importance of dynamic, regime-aware portfolio construction in environments shaped by regulatory signals, investor flows, and behavioral biases. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.24250 |
| By: | Shanyu Han; Yang Liu; Xiang Yu |
| Abstract: | We propose a reinforcement learning (RL) framework under a broad class of risk objectives, characterized by convex scoring functions. This class covers many common risk measures, such as variance, Expected Shortfall, entropic Value-at-Risk, and mean-risk utility. To resolve the time-inconsistency issue, we consider an augmented state space and an auxiliary variable and recast the problem as a two-state optimization problem. We propose a customized Actor-Critic algorithm and establish some theoretical approximation guarantees. A key theoretical contribution is that our results do not require the Markov decision process to be continuous. Additionally, we propose an auxiliary variable sampling method inspired by the alternating minimization algorithm, which is convergent under certain conditions. We validate our approach in simulation experiments with a financial application in statistical arbitrage trading, demonstrating the effectiveness of the algorithm. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.04553 |
| By: | Rodney Garratt; David Murphy; Travis D. Nesmith; Xiaopeng Wu |
| Abstract: | Central counterparties’ ability to hold successful default auctions is critically important to financial stability. However, due to the unique features of these auctions, standard auction theory results do not apply. We present a model of CCP default auctions that incorporates both the vital, but non-standard, objective of minimizing the likelihood it suffers reputationally damaging losses and the potential for information leakage to affect CCP members’ private portfolio valuations. This gives insight into the key question of how CCPs should select auction participants. In particular, we prove that an entry fee, by appropriately incentivizing some members not to enter the auction, can maximize the probability of auction success. The result is novel, both in auction theory and as a mechanism for CCP auction design. |
| Keywords: | Auctions; Central counterparties; CCPs; Default; Derivatives; Entry mechanism; Financial stablity; Systemic risk |
| JEL: | D44 D47 G13 G23 |
| Date: | 2024–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:100033 |
| By: | Stelios Arvanitis (Department of Economics, AUEB) |
| Abstract: | We develop and implement methods for determining whether relaxing sparsity constraints on portfolios improves the investment opportunity set for risk-averse investors.We formulate a new estimation procedure for sparse second-order stochastic spanning based on a greedy algorithm and Linear Programming. We show the optimal recovery of the sparse solution asymptotically whether spanning holds or not. From large equity datasets, we estimate the expected utility loss due to possible under-diversification, and find that there is no benefit from expanding a sparse opportunity set beyond 45 assets. The optimal sparse portfolio invests in 10 industry sectors and cuts tail risk when compared to a sparse mean-variance portfolio. On a rolling-window basis, the number of assets shrinks to 25 assets in crisis periods, while standard factor models cannot explain the performance of the sparse portfolios. |
| Keywords: | Nonparametric estimation, stochastic dominance, spanning, under-diversification, greedy algorithm, Linear Programming |
| JEL: | C13 C14 C44 C58 C61 D81 G11 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:qed:wpaper:1532 |
| By: | Laurent Clerc; Sandrine Lecarpentier; Cyril Pouvelle |
| Abstract: | This paper examines the impact of multiple regulatory constraints on the financing of the economy in the context of the implementation of the Basel III regulation on capital and liquidity. We propose a simple theoretical model of bank lending decision to analyse the interactions between these various regulatory requirements and the conditions under which some constraints may bind while others may not. Building on the predictions of this theoretical model, we estimate the impact of these different regulatory requirements on lending growth, on a panel of 54 French banks since 2014. Our results indicate that four pairwise interactions, most of them involving the leverage ratio, have a significant effect on lending growth. We also emphasize that the regulatory ratios interact more for banks with lower regulatory ratios and in periods of financial stress. More specifically, our results highlight a significant relationship of partial substitutability between the leverage ratio, the LCR and the NSFR for such banks in such periods, resulting from the positive effect of bank own funds on liquidity. |
| Keywords: | Bank Capital Regulation, Bank Liquidity Regulation, Basel III, Stress Tests |
| JEL: | G28 G21 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:988 |
| By: | Pierre Brugi\`ere; Gabriel Turinici |
| Abstract: | Option pricing theory, such as the Black and Scholes (1973) model, provides an explicit solution to construct a strategy that perfectly hedges an option in a continuous-time setting. In practice, however, trading occurs in discrete time and often involves transaction costs, making the direct application of continuous-time solutions potentially suboptimal. Previous studies, such as those by Buehler et al. (2018), Buehler et al. (2019) and Cao et al. (2019), have shown that deep learning or reinforcement learning can be used to derive better hedging strategies than those based on continuous-time models. However, these approaches typically rely on a large number of trajectories (of the order of $10^5$ or $10^6$) to train the model. In this work, we show that using as few as 256 trajectories is sufficient to train a neural network that significantly outperforms, in the Geometric Brownian Motion framework, both the classical Black & Scholes formula and the Leland model, which is arguably one of the most effective explicit alternatives for incorporating transaction costs. The ability to train neural networks with such a small number of trajectories suggests the potential for more practical and simple implementation on real-time financial series. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.22836 |
| By: | Makoto WATANABE; Yu Awaya; Jihwan Do |
| Abstract: | We construct a model of bubbles where an asset can be used as collateral primarily due to higher-order uncertainty: while both a lender and a borrower know that the intrinsic value of the asset is low, they may still believe that a greater fool exists who will purchase it at a much higher price. We show that such bubbles can lead to inefficient overinvestment under certain conditions. Using this framework, we also examine the impacts of macroprudential policies, as well as other regulatory measures such as interest rate hikes and the resolution of uncertainty. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cnn:wpaper:25-013e |
| By: | Stelios Arvanitis (Department of Economics, AUEB) |
| Abstract: | This paper utilizes a Banach-type fixed point theorem in a functorial context to develop Universal Choice Spaces for addressing decision problems, focusing on expected utility and preference uncertainty. This generates an infinite sequence of optimal selection problems involving probability measures on utility sets. Each solution at a given stage addresses the preference ambiguity from the previous stage, enabling optimal choices at that level. The Universal Choice Space is characterized as a collection of finite-dimensional vectors of probability distributions, with the mth component being an arbitrary probability measure relevant to the mth stage of the problem. The space is derived as the canonical fixed point of a suitable endofunctor on an enriched category and simultaneously as the colimit of the sequence of iterations of this functor, starting from a suitable object. |
| Keywords: | Expected utility, ambiguity of preferences, infinite regress, enriched category, endofunctor, canonical fixed point, initial algebra, colimit, universal choice space |
| JEL: | D81 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:qed:wpaper:1534 |
| By: | Christos Axioglou; Marco Ratto; Josselin Roman |
| Abstract: | The European Economic Forecasts (EEFs) are an integral part of the European Commission's Treatybased economic and budgetary surveillance framework. To increase the transparency and credibility of its forecasts, the Commission regularly evaluates forecast performance, focusing on point estimates of three prominent variables in the Commission's economic surveillance: GDP growth, inflation, and the general government budget balance. This paper updates the previous regular report, covering the additional period 2018-2023. The analysis evaluates the quality of the forecasts – in terms of unbiasedness, efficient use of the information available, and correction of past errors. To this purpose, a number of basic metrics are calculated and econometric methodologies/tests are run, as in past exercises, but with the additional challenge posed by the large volatility in the economic variables due to the pandemic and energy crises. The study also explores the potential sources of forecast inaccuracies, including the role played by the assumptions underpinning the forecasts and economic uncertainty. The analysis is reinforced by model-based decompositions of forecast errors using the Commission's Global Multi-Country Model. Lastly, the report updates the comparison of the Commission's forecast performance with that of other international institutions. Overall, this updated exercise confirms that the Commission’s forecasts provide a largely unbiased picture of the near-term economic outlook, accurately foresee the trends in its key variables and tend to perform better than ‘naïve’ forecasts that utilise no other information than the most recent reading for the target variable. The accuracy of the Commission’s GDP growth forecasts was found broadly similar to that of other major international institutions. |
| JEL: | C1 E60 E66 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:euf:dispap:222 |
| By: | Danilo Stojanovic; Veljko Bojovic |
| Abstract: | This paper examines how profit volatility has influenced firms’ decisions over the past four decades. Using Compustat data, we document that: (1) high-investing firms cut their investment rate more sharply than other firms, implying that extensive margin investment decisions - whether to invest in new projects or not - are important for the uncertainty effects; (2) the interaction between firms’ financial and real conditions amplifies the negative impact of increased uncertainty on the investment rates. We also develop and calibrate a heterogeneous-firm model that incorporates both real and financial costs. In the model, higher capital adjustment costs increase the investment inaction rate by 31%, while higher financial costs reduce the investment spike rate by 46%. Incorporating irreversible capital into the collateral constraint reduces firms’ debt capacity, leading to an increase in the investment inaction rate, cash holdings, and net dividends. |
| Keywords: | Capital Investment, Adjustment Costs, Extensive Margin |
| JEL: | C31 E22 G31 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:cer:papers:wp793 |
| By: | Yucheng Guo; Qinxin Yan |
| Abstract: | We study particle systems interacting via hitting times on sparsely connected graphs, following the framework of Lacker, Ramanan and Wu (2023). We provide general robustness conditions that guarantee the well-posedness of physical solutions to the dynamics, and demonstrate their connections to the dynamic percolation theory. We then study the limiting behavior of the particle systems, establishing the continuous dependence of the joint law of the physical solution on the underlying graph structure with respect to local convergence and showing the convergence of the global empirical measure, which extends the general results by Lacker et al. to systems with singular interaction. The model proposed provides a general framework for analyzing systemic risks in large sparsely connected financial networks with a focus on local interactions, featuring instantaneous default cascades. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.18448 |
| By: | Margherita Borella; Mariacristina De Nardi; Fang Yang; Johanna P. Torres Chain |
| Abstract: | This paper develops and estimates a dynamic life-cycle model to quantify why households save and work. The model incorporates multiple sources of risk—health, marital status, wages, medical expenses, and mortality—as well as endogenous labor supply and human capital accumulation, retirement, and bequest motives at the death of the first and last household member. We estimate it using PSID and HRS data for the 1941–1945 cohort via the Method of Simulated Moments. Eliminating bequest motives reduces aggregate wealth by 23.8% and labor earnings by 1.2%; removing medical expenses lowers them by 13.1% and 0.7%. Wage risk is crucial for early-life saving: its removal reduces wealth by 10.4% but raises earnings by 2.3%. Eliminating marriage and divorce dynamics leads couples—numerous and wealthier—to save and work slightly less, and singles—fewer and poorer—to save and work considerably more. These effects largely offset in the aggregate. Removing all saving motives beyond retirement needs and lifespan uncertainty lowers wealth by 56.9% and earnings by 2.7%. These findings show that capturing multiple risks and behavioral margins jointly is essential to understanding household saving and labor supply. |
| JEL: | E20 I1 J0 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33874 |
| By: | Moreno, William |
| Abstract: | Representing and reasoning with complex, uncertain, context-dependent, and value-laden knowledge remains a fundamental challenge in Artificial Intelligence (AI) and Knowledge Representation (KR). Existing frameworks often struggle to integrate diverse knowledge types, make underlying assumptions explicit, handle normative constraints, or provide robust justifications for inferences. This preprint introduces the Conditional Reasoning Framework (CRF) and its Orthogonal Knowledge Graph (OKG) as a novel computational and conceptual architecture designed to address these limitations. The CRF operationalizes conditional necessity through a quantifiable, counterfactual test derived from a generalization of J.L. Mackie's INUS condition, enabling context-dependent reasoning within the graph-based OKG. Its design is grounded in the novel Theory of Minimal Axiom Systems (TOMAS), which posits that meaningful representation requires at least two orthogonal (conceptually independent) foundational axioms; TOMAS provides a philosophical justification for the CRF's emphasis on axiom orthogonality and explicit context (W). Furthermore, the framework incorporates expectation calculus for handling uncertainty and integrates the "ought implies can" principle as a fundamental constraint for normative reasoning. By offering a principled method for structuring knowledge, analyzing dependencies (including diagnosing model limitations by identifying failures of expected necessary conditions), and integrating descriptive and prescriptive information, the CRF/OKG provides a promising foundation for developing more robust, transparent, and ethically-aware AI systems. |
| Date: | 2025–05–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:zwpnv_v4 |
| By: | Samiha Tariq |
| Abstract: | This paper examines the impact of cognitive biases on financial decision-making through a static Bayesian game framework. While traditional economic theory assumes fully rational investors, real-world choices are often shaped by loss aversion, overconfidence, and herd behavior. Integrating psychological insights with economic game theory, the model studies strategic interactions among investors who allocate wealth between risky and risk-free assets. Solving for the Bayesian Nash Equilibrium reveals that each bias distorts optimal portfolios and alters aggregate market dynamics. The results echo Herbert Simon's notion of bounded rationality, showing how biases can generate market inefficiencies, price bubbles, and crashes. The findings highlight the importance of incorporating psychological factors into economic models to guide policies that foster market stability and more informed financial decision-making. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.18835 |
| By: | Leo Kurata; Kensei Nakamura |
| Abstract: | This paper studies preference aggregation under uncertainty in the multi-profile framework introduced by Sprumont (2018, 2019) and characterizes a new class of aggregation rules that can address classical concerns about Harsanyi's (1955) utilitarian rules. Our class of aggregation rules, which we call relative fair aggregation rules, is grounded in three key ideas: utilitarianism, egalitarianism, and the 0--1 normalization. These rules are parameterized by a set of weights over individuals. Each ambiguous alternative is evaluated by computing the minimum weighted sum of the 0--1 normalized utility levels within that weight set. For the characterization, we propose two novel key axioms -- weak preference for mixing and restricted certainty independence -- developed using a new method of objectively randomizing outcomes even within the fully uncertain Savagean framework. Furthermore, we show that relative utilitarian aggregation rules can be identified from the above class by imposing an axiom stronger than restricted certainty independence, and that the Rawlsian maximin version can be derived by considering strong preference for mixing instead. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.03232 |