Introduction
Adverse selection typically destabilizes voluntary insurance markets. In Russia, mandatory health insurance (MHI) dominates, but its dual structure—insurance medical organizations (IMOs) and territorial funds (TFMIFs)—shifts risk selection from insurers to providers. Cardiovascular surgery (CVS), a high-cost, high-risk field, reveals hidden mechanisms. This study examines provider-driven adverse selection in Russian MHI and high-tech medical care (HTMC), using evidence from cardiac surgery.
Methods
We analyzed Russian MHI regulations, payment models (capitation for primary care; clinical-statistical groups (CSG/DRG) for inpatient care; fixed tariffs for HTMC) using standard reporting of TFMIFs. Additionally, a quasi-experimental design was used, comparing two centers: a regional center and a single federal center. Four groups were formed based on insurance type (MHI vs. VHI) and care level (regional vs. federal). Clinical risk stratification was assessed using the EuroSCORE II and compared across groups.
Results
HTMC, fixed episode tariffs ignore age and comorbidity. To test this empirically, we employed a quasi-experimental design comparing regional (Chelyabinsk) and federal (Bakoulev, Moscow) cardiac centers. Distributions were compared via Mann–Whitney U test. Consequently, CVS centers avoid complex cases through formal refusal or informal barriers (excessive preoperative requirements). High-risk patients are redirected to a few "unavoidable" hospitals, worsening their unadjusted mortality statistics. Regional disparities push severe cases to federal centers, which further select low-risk patients to protect performance ratings.
Conclusions
Adverse selection in Russia is provider-driven, amplified by unadjusted capitation, incomplete CSG risk adjustment, and flat HTMC tariffs. In cardiovascular surgery, this results in hidden denial of care to high-risk patients. Transition to value-based healthcare (VBHC) with bundled payments, systematic risk adjustment (DCG/ACG), and outcomes measurement is necessary to align financial and clinical incentives.

This study investigates and compares dynamic hedging strategies for American put options in financial markets governed by jump diffusion dynamics. Since closed-form continuous-time hedging solutions are generally unavailable in this setting, we approximate the underlying continuous-time models by using a discrete-time lattice framework. By focusing on the challenges posed by continuous-time hedging, we utilize a discrete-time and discrete-outcome stochastic framework with backward dynamic programming to optimize hedging under jump risk. The strategies include global quadratic hedging, local risk minimization, and Delta-Gamma hedging. We also examine an internal exercise specification in which the exercise decision is linked to the current hedging portfolio value, and compare it with the standard externally imposed exercise rule. Through numerical experiments, we compare the hedging error distributions across these methodologies, particularly highlighting how the inclusion of jump risk affects hedging outcomes. Moreover, we investigate and compare the Kou and Merton jump-diffusion models calibrated to S$\&$P 500 and Bitcoin (BTC) data. Our results indicate that while all strategies effectively manage standard market risks, local risk minimization offers superior performance in extreme market conditions. Additionally, incorporating a third hedging asset does not lead to a consistent improvement in the performance of the global hedging strategy. Although the additional option enlarges the hedge set and may reduce the required initial investment, it does not deliver a robust reduction in tail-risk measures under the jump diffusion setting considered here.

Karatzas and Wang (2000) study an extension of the perpetual American put option by introducing a knock-out barrier for the price of the underlying asset that deactivates the option. We revisit the problem of pricing perpetual American barrier-type options studied by Karatzas, I., & Wang, H. (2000), but this time with the additional assumption that the holder has the opportunity to exercise the option only at random epochs occurring according to an exogenous Poisson process with constant intensity. In contrast, the barrier-triggered deactivation of the option occurs under continuous monitoring, e.g., by an automated system. By employing a constant optimal exercise boundary strategy and assuming geometric Brownian motion dynamics for the underlying asset price process, we provide closed form formulas for the expected present value of the options payoff, the probability of exercising the option and the Laplace transform of the time until exercising the option for both put and call option cases. The determination of the optimal exercise boundary and the fair price of the option is achieved by maximizing the option’s expected present value. The impact of the random exercise opportunities on the optimal expected present value, the optimal exercise threshold and the probability of the optimal exercise before the option is knocked-out is numerically investigated for the put and call option case.

Catastrophe insurance, as a financial tool for disaster prevention and mitigation, provides significant support for the risk governance of China's modernization. This paper employs the difference-in-difference method and text analysis method, based on the panel data of 31 provinces in China from 2012 to 2023, to investigate the impact of catastrophe insurance policies on the direct economic losses caused by natural disasters and the pathways through which they operate. The findings indicate that catastrophe insurance policies significantly reduce the economic losses caused by natural disasters. The results of the mechanism analysis show that the attention of local governments to climate risks has a positive moderating effect on the impact of catastrophe insurance policies on direct economic losses from natural disasters. The heterogeneous analysis reveals that the policy effects are better in the eastern region than in the central and western regions, and better in the south than in the north. Accordingly, it is suggested that regional differentiated implementation strategies be combined to further explore new models of insurance policies for disaster prevention and mitigation, in order to serve the high-quality development of China's modernization. This study enriches the empirical research on the governance effect of catastrophe insurance and provides a practical reference for improving the national disaster risk prevention and control system.

We propose a novel portfolio optimization framework that integrates linear factor modeling, non-Gaussian innovations, and generative artificial intelligence to construct portfolios with superior risk-return trade-offs. The success of portfolio optimization critically depends on accurately capturing the joint dependence structure among asset returns. However, traditional covariance measures fail to describe asymmetric tail dependence, and widely used parametric copulas are often too restrictive for complex financial regimes. To overcome these limitations, we introduce a generative framework utilizing Real NVP, a subclass of normalizing flows, to flexibly learn and represent complex dependence structures among latent financial factors. Our approach builds on the tradition of CAPM and Fama–French models but departs by extracting factors statistically via Principal Component Analysis (PCA). Each factor is filtered through ARMA-GARCH dynamics, with residuals modeled using the Normal Tempered Stable (NTS) distribution to account for fat tails and asymmetry.
Empirical results from a 10-year backtest (2015–2024) demonstrate that our NTS-MLCopula method achieves superior statistical fidelity, evidenced by a lower Maximum Mean Discrepancy (MMD) as a non-parametric metric for goodness-of-fit compared to traditional multivariate models. Furthermore, the framework consistently produces portfolios with enhanced Conditional Value at Risk (CVaR) protection and higher Sharpe ratios, proving its robustness in mitigating extreme tail risks during catastrophic market events.

Introduction: The integration of ESG criteria into portfolio management has grown substantially, yet the theoretical literature remains largely static: ESG scores are treated as deterministic or single-period random variables. In practice, scores evolve as firms change their environmental practices and governance structures, exposing multi-year investors to ESG score risk. This paper addresses this gap by extending the ESG portfolio problem to continuous time.

Methods. We derive the optimal dynamic investment strategy for a CARA investor with warm-glow preferences over portfolio ESG quality, where scores follow a multivariate Ornstein–Uhlenbeck process correlated with asset returns. The solution to the associated Hamilton–Jacobi–Bellman equation can be characterized explicitly. We further extend the model to incorporate ambiguity aversion about ESG dynamics using a Hansen–Sargent robust control formulation.

Results. The optimal portfolio decomposes into four components, two of which correspond to intertemporal hedging demands against ESG score risk. Closed-form solutions are derived in the scalar case, with comparative statics showing that the hedging demand grows quadratically in ESG preference intensity, depends critically on the return–ESG correlation, and saturates with the investment horizon. We extend the model to incorporate ambiguity aversion about ESG dynamics. The model nests the static frameworks of Pástor et al. (2021), Pedersen et al. (2021), and Avramov et al. (2022) as limiting cases.

Conclusions. Dynamic ESG investing generates hedging demands absent from static frameworks, whose magnitude depends on horizon, persistence, and return–ESG correlation. Ambiguity surroundng ESG dynamics attenuates but does not eliminate these demands, providing a tractable bridge between static and fully dynamic approaches.

In an era of increasing political salience, firm-level political risk, including policy uncertainty, regulatory threats and partisan favoritism has a significant effect on asset pricing, capital structure and investment decisions. Path-breaking research by scholars attests to these effects—yet they treat risk as exogenous, ignoring its responsiveness to changes in party identification(s). We fill this gap by looking at risk changing endogenously when firms enter and exit U.S. politicians’ stock portfolios, using lawmakers’ trades as natural experiments both in the formation of alliances and their subsequent dissolution.

In our analysis, we rely on granular transaction-level openness from congressional traders as well as common firm fundamentals and known measures of political risk. This results in a monitoring of events at firm-level frequency from open diary data, recording both entry (new positions or increases) and exit (reductions or complete disposals) for public firms. We leverage intra-firm variation before and after events, as initiations or increases represent an endorsement (e.g., increased visibility to committees), while reductions or sales serve as severed connections (e.g., divestitures post-scandals).

We contribute to political finance by bridging ownership and risk channels systematically, thus deepening studies of congressional trading benefits and crony capitalism. Robustness to alternative specifications indicates one's ability to be applied to a broader set of contexts. Implications protect investors from this type of policy-induced volatility, assist regulators in deciding how to craft disclosure laws such as the STOCK Act, and guide researchers on how to structurally represent endogenous policy uncertainty within an asset pricing framework.

Abstract: Members of the United States Congress buy and sell stocks and options when writing laws that shape the economy. This has prompted debate over fairness and information. In this paper, I exploit transaction-level STOCK Act disclosures from 2012 to 2025 and daily market data to check whether trades by Congress members convey valuable information about asset prices. Event-study regressions indicate that negative short sales, puts, or inverse funds make between 1 to 1.5 percent more money than usual in a week after the trade. Likewise, good trades do not earn any more money. Committee assignments amplify this effect: lawmakers who oversee an industry see larger price changes when they trade, but longer reporting delays mean that the information is less valuable. These portfolios that mimic Congress members' positions generate profits while contrarian strategies do not compared with four and six factor models. A probit analysis of reactions on the market demonstrates that negative trades tend to lead to larger price shifts than positive ones. These patterns remain valid across many different event windows, return models, subsamples, and placebo tests. The results indicate political trades are a helpful if small source of information, but they also raise questions about insider knowledge and public trust.

Introduction: Since the Morris review (2004) in the UK, and the MAAA/SOA/CAS ESGs towards the turn of the century, mean-reverting stochastic processes have been widely used to model inflation, interest rates, and other macroeconomic risk drivers in actuarial cashflow projections and financial risk models. In most practical implementations, autocorrelation is seldomly looked at and assumed to be flat. However, this is only asymptotically valid and can conceal a transient implicit diversification effect at finite horizons.

Methods: Looking at the class of mean-reverting diffusions based on Ornstein–Uhlenbeck specification (Vasicek, CIR, Hull–White), where finite-horizon lag-Δ autocorrelation is available in closed form, this paper aims to prove monotonic convergence to the stationary limit and analyze sensitivity to mean-reversion speed and horizon length. For practical materiality, Monte Carlo experiments across parameter grids for mean reversion and volatility on two representative use cases have been designed: inflation-linked liability cashflows and interest-rate-driven discounting distributions, comparing simulations with two stationary-imposition implementations (assuming a deterministic start different from the reverted mean): (i) empirical copula reordering of existing simulated paths and (ii) recursive reconstruction with a fixed flat-ρ target

Results: Across mean-reverting specifications, finite-horizon autocorrelation exhibits a pre-stationary term structure that converges to the long-run lag structure. In the Ornstein–Uhlenbeck benchmark with κ>0, the finite-horizon autocorrelation is increasing in t, and is independent of the initial level and reversion direction. Relative to stationary assumptions, early-horizon paths display weaker serial dependence and therefore stronger implicit diversification, leading to potentially material differences in aggregate variance and tail-risk metrics, especially for slow mean reversion and short-to-medium horizons.

Conclusions: Pre-stationary autocorrelation should be treated as an explicit modelling assumption in mean-reverting models. For deterministic starts, practical stationary-imposition overlays described before are shown and benchmarked against the baseline. This framework makes diversification effects measurable and provides a practical decision toolkit for model developers and validators.

This paper proposes a novel dynamic extreme value theory (EVT)–Copula modelling framework with endogenous linkage to model systemic risk arising from extreme losses and evolving dependence structures. The marginal distributions of asset losses are modelled using a peak-over-threshold approach. Then, exceedances follow generalized Pareto distributions with time-varying parameters and adaptive thresholds to capture non-stationary tail behavior. The dependence structure is specified through copula modelling of non-linear and asymmetric tail dependence across multiple financial entities. The endogenizing of the dependence copula parameters evolves according to score-driven mechanism augmented by extreme loss feedback. Specifically, the dependence parameter is modelled as a function of past dependence, score information, and aggregated magnitudes. Its captures the co-evaluation between marginal extremes and systemic independence. The estimation is carried out using inference functions for margins and canonical maximum likelihood for copula parameters exposed the computational tractability and efficiency. The proposed modelling enables the computation of systemic risk measures including value at risk, expected shortfall, conditional value at risk, marginal expected shortfall, and tail dependence coefficients. The empirical and simulation analyses demonstrate that the model effectively captures contagion effects and dynamic tail risk. The model provides a robust tool for actuarial risk management and financial stability assessment under extreme conditions.