We propose a semi-structural DSGE model for the Israeli economy, as a small open economy, which contains a financial friction in the household sector credit market. Such a friction is reflected in a positive relationship between households' leverage ratio and their interest rate (credit spread) on debt, as evident in the Israeli data. Our main purpose is to evaluate the implications of such a friction on the implementation of monetary policy and macroprudential policy. Our two main findings are: First, it is important that the monetary policy will react also to developments in the credit market, such as credit spread widening, to increase effectiveness in achieving its main goals of stabilizing inflation and real activity. Second, macroprudential policy may increase the sensitivity of households' credit spread to their leverage. Thus, this policy can mitigate or even prevent over-borrowing and reduce the risk of a debt deleveraging crisis. Moreover, in a case of demand weakness and debt deleveraging, in addition to accommodative monetary policy, the macroprudential policy may contribute to stimulating demand due to a corresponding reduction in credit spread.

The rapid adoption of large language models (LLMs) in financial services introduces new operational, regulatory, and security risks. Yet most red-teaming benchmarks remain domain-agnostic and fail to capture failure modes specific to regulated BFSI settings, where harmful behavior can be elicited through legally or professionally plausible framing. We propose a risk-aware evaluation framework for LLM security failures in Banking, Financial Services, and Insurance (BFSI), combining a domain-specific taxonomy of financial harms, an automated multi-round red-teaming pipeline, and an ensemble-based judging protocol. We introduce the Risk-Adjusted Harm Score (RAHS), a risk-sensitive metric that goes beyond success rates by quantifying the operational severity of disclosures, accounting for mitigation signals, and leveraging inter-judge agreement. Across diverse models, we find that higher decoding stochasticity and sustained adaptive interaction not only increase jailbreak success, but also drive systematic escalation toward more severe and operationally actionable financial disclosures. These results expose limitations of single-turn, domain-agnostic security evaluation and motivate risk-sensitive assessment under prolonged adversarial pressure for real-world BFSI deployment.

This paper studies the impact of funding market frictions on bond prices and market-wide liquidity. Using proprietary transaction-level data on all gilt-backed repo and reverse-repo trades, we demonstrate how the market power of individual dealers and their linkages generate frictions. Specifically, we show that frictions related to market power account for between 0.5 and 1.3 percentage points of bond yield deviation, while the transmission of heterogeneously persistent shocks between dealers accounts for between 2 and 4 percentage points of yield deviation.

We study market making in aggregator-routed RFQ markets where platform routing depends on slowly varying dealer performance scores. We propose a two-tier stochastic control model that separates RFQ-level price competition from a macro routing layer: tier A represents aggregator flow whose opportunity intensity is multiplied by a promotion gate driven by the dealer's win score, while tier B captures background flow that is not gated and does not update the score. RFQs arrive in multiple sizes and the dealer chooses a size-ladder of bid/ask offsets; conditional on winning, trades earn spread minus an adverse selection correction and contribute to inventory risk. The resulting Hamilton-Jacobi-Bellman equation admits a reduced Bergault-Guéant operator form with explicit win/lose branches for the score on tier A. Using the envelope-theorem argument, we express optimal controls through derivatives of the one-dimensional reduced Hamiltonians, yielding an interpretable mapping from optimal win probabilities to optimal offsets. In the long-memory regime, we derive an adiabatic approximation that separates fast inventory dynamics from slow score dynamics. A quadratic inventory ansatz and quadratic Hamiltonian expansion lead to a quasi-stationarity inventory-curvature scaling and a one-dimensional score drift field. For steep (logistic) promotion gates, the score dynamics can exhibit fold bifurcations, bistability, and hysteresis, producing an endogenous "campaign vs. harvest" pattern in optimal quoting. Numerical experiments confirm this behaviour and highlight the stabilizing role of background flow in maintaining inventory-mixing capacity even when the dealer is weakly promoted.

The theory of thermal macroeconomics (TM) analyses economic phenomena within the mathematical framework of classical thermodynamics, using a set of axioms that apply to the purely macroscopic aspects of an economy [CM]. The theory shows that the possible macro-behaviours are governed by an entropy function. In simple idealised cases, the entropy function can be calculated from the rules governing the interactions of individual agents. But where this is not possible, TM predicts that the entropy can nonetheless be measured empirically through an economic analogue of calorimetry in physics. We show using computer simulations the in-principle feasibility of this approach: an entropy function can successfully be measured for a range of simulated economies that we tested. In cases where entropy can be calculated analytically from microfoundational assumptions, the measured entropy agrees well. In more complex cases, where microfoundational analysis is infeasible, our method of measuring entropy still applies and is validated by demonstrations that entropy is a state function of an economic system, i.e., exhibits path independence. This appears to hold even for some systems to which we don't have a proof that the Axioms of TM apply. Furthermore, in all cases tested, entropy is concave, as predicted by TM. As shown in [CM], once the entropy function is established for a simulated exchange economy, it is possible to derive prices, the value of money and various other quantities, and make predictions about the effects of putting two or more economies in contact.

Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence -- a significant barrier to deployment. We introduce uncertainty quantification to the deep hedging framework by training a deep ensemble of five independent LSTM networks under Heston stochastic volatility with proportional transaction costs. The ensemble's disagreement at each time step provides a per-time-step confidence measure that is strongly predictive of hedging performance: the learned strategy outperforms the Black-Scholes delta on approximately 80% of paths when model agreement is high, but on fewer than 20% when disagreement is elevated. We propose a CVaR-optimised blending strategy that combines the ensemble's hedge with the classical Black-Scholes delta, weighted by the level of model uncertainty. The blend improves on the Black-Scholes delta by 35-80 basis points in CVaR across several Heston calibrations, and on the theoretically optimal Whalley-Wilmott strategy by 100-250 basis points, with all improvements statistically significant under paired bootstrap tests. The analysis reveals that ensemble uncertainty is driven primarily by option moneyness rather than volatility, and that the uncertainty-performance relationship inverts under weak leverage -- findings with practical implications for the deployment of machine learning in hedging systems.

In this paper, we study an intertemporal utility maximization problem in which an investor chooses consumption and portfolio strategies in the presence of a stochastic factor and a no-borrowing constraint. In the spirit of the Kim-Omberg model, the stochastic factor represents the expected excess return of the risky asset. It is perfectly negatively correlated with shocks to the risky asset, and follows an Ornstein-Uhlenbeck process, thereby capturing the mean reversion of expected excess returns-a feature well supported by empirical evidence in financial markets. The investor seeks to maximize expected utility from consumption, subject to the constraint that wealth remains nonnegative at all times. To address the dynamic no-borrowing constraint, we use Lagrange duality to transform the primal problem into a singular control problem in the dual space. We then characterize the solution to the dual singular control problem via an auxiliary two-dimensional optimal stopping problem featuring stochastic volatility, and subsequently retrieve the primal value function as well as the optimal portfolio and consumption plans. Finally, a numerical study is conducted to derive economic and financial implications.

Islamic equity screening relies on multiple binary rulebooks that often classify the same firm differently. This paper develops a Continuous Shariah Compliance Index (CSCI) on $[0,1]$ that embeds the published business-activity and financial-ratio thresholds of six leading standards in a single transparent measure. Using CRSP/Compustat U.S. equities from 1999-2024 with lagged accounting inputs and monthly portfolio formation, we document four results. First, existing binary standards map to distinct regions of a common compliance scale, so firms that receive the same pass/fail label can still differ materially in compliance strength. Second, CSCI-threshold portfolios provide a transparent way to vary compliance intensity while retaining economically meaningful diversification, although baseline risk-adjusted performance declines modestly as thresholds tighten. Third, the September 2023 DJIM/S&P methodology change admits firms with materially lower CSCI scores than firms that remained compliant under both the old and new rules. Fourth, in cross-sectional return tests, CSCI is not reliably associated with higher expected returns once standard characteristics are controlled for. The main contribution of CSCI is therefore measurement and portfolio design rather than the discovery of a new priced factor.

Energy markets exhibit complex causal relationships between weather patterns, generation technologies, and price formation, with regime changes occurring continuously rather than at discrete break points. Current approaches model electricity prices without explicit causal interpretation or counterfactual reasoning capabilities. We introduce Augmented Time Series Causal Models (ATSCM) for energy markets, extending counterfactual reasoning frameworks to multivariate temporal data with learned causal structure. Our approach models energy systems through interpretable factors (weather, generation mix, demand patterns), rich grid dynamics, and observable market variables. We integrate neural causal discovery to learn time-varying causal graphs without requiring ground truth DAGs. Applied to real-world electricity price data, ATSCM enables novel counterfactual queries such as "What would prices be under different renewable generation scenarios?".

This paper explores continuous-time and state-space optimal stopping problems from a reinforcement learning perspective. We begin by formulating the stopping problem using randomized stopping times, where the decision maker's control is represented by the probability of stopping within a given time-specifically, a bounded, non-decreasing, càdlàg control process. To encourage exploration and facilitate learning, we introduce a regularized version of the problem by penalizing the performance criterion with the cumulative residual entropy of the randomized stopping time. The regularized problem takes the form of an (n+1)-dimensional degenerate singular stochastic control with finite-fuel, where the regularized free boundary becomes the graph of a function mapping the state variable of the original stopping problem into the probability of stopping. We address this singular control problem through the dynamic programming principle, which enables us to identify the unique optimal exploratory strategy. Finally, we propose both model-based and model-free reinforcement learning algorithms tailored for exploratory optimal stopping problems. We establish policy improvement guarantees for the proposed algorithms. Moreover, the model-free method is of actor-critic type and it is scalable in high-dimensions under neural network parameterization.

We investigate whether the tails of firm-level idiosyncratic return distributions are driven by common shocks. We use quantile factor analysis to extract such common idiosyncratic quantile factors with asymmetric pricing effects and we find a significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year. This premium remains significant even when controlling for standard factors, idiosyncratic volatility and tail-risk measures. The downside factor strengthens when intermediary capital is weak and market liquidity is low, and it predicts aggregate market excess returns.

Extensive empirical studies show that the long distribution tail of travel time and the corresponding unexpected delay can have much more serious consequences than expected or moderate delay. However, the unexpected delay due to the distribution tail of travel time has received limited attention in recent studies of the valuation of travel time variability. As a complement to current valuation research, this paper proposes the concept of the value of travel time distribution tail, which quantifies the value that travelers place on reducing the unexpected delay for hedging against travel time variability. Methodologically, we define the summation of all unexpected delays as the unreliability area to quantify travel time distribution tail and show that it is a key element of two well-defined measures accounting for unreliable aspects of travel time. We then formally derive the value of distribution tail, show that it is distinct from the more established value of reliability (VOR), and combine it and the VOR in an overall value of travel time variability (VOV). We prove theoretically that the VOV exhibits diminishing marginal benefit in terms of the traveler's punctuality requirements under a validity condition. This implies that it may be economically inefficient for travelers to blindly pursue a higher probability of not being late. We then proceed to develop the concept of the travel time variability ratio, which gives the implicit cost of the punctuality requirement imposed on any given trip. Numerical examples reveal that the cost of travel time distribution tail can account for more than 10% of the trip cost, such that its omission could introduce non-trivial bias into route choice models and transportation appraisal more generally.

This paper examines how shocks to currency volatilities predict exchange rates. Using option-implied volatilities, we construct a dynamic, directed network of volatility connections. Currencies that transmit more volatility shocks, which control for common correlation, earn lower excess returns. Buying the weakest and selling the strongest transmitters delivers high risk-adjusted performance, driven by spot exchange rate movements and not explained by standard factors. A general equilibrium model shows that volatility transmission related to idiosyncratic shocks proxies for priced country-specific risk. Assuming a monotonic amplification of domestic idiosyncratic risk, volatility transmission forecasts negatively future excess returns, consistent with the empirical evidence.