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on Risk Management |
| By: | Sourish Das |
| Abstract: | This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for effective risk management. Traditional Generalised Pareto Distribution (GPD) models often ignore market conditions; in contrast, our framework links the scale parameter to covariates using a log-linear function, allowing tail risk to respond dynamically to market volatility. We examine four prior choices for Bayesian regularisation of regression coefficients: Cauchy, Lasso (Laplace), Ridge (Gaussian), and Zellner's g-prior. Simulation results suggest that the Cauchy prior delivers the best trade-off between predictive accuracy and model simplicity, achieving the lowest RMSE, AIC, and BIC values. Empirically, we apply the model to large negative returns (exceeding 5%) in the Nifty 50 index. Volatility measures from the Nifty 50, S&P 500, and gold are used as covariates to capture both domestic and global risk drivers. Our findings show that tail risk increases significantly with higher market volatility. In particular, both S&P 500 and gold volatilities contribute meaningfully to crash prediction, highlighting global spillover and flight-to-safety effects. The proposed GPR model offers a robust and interpretable approach for tail risk forecasting in emerging markets. It improves upon traditional EVT-based models by incorporating real-time financial indicators, making it useful for practitioners, policymakers, and financial regulators concerned with systemic risk and stress testing. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.17549 |
| By: | Ziyao Wang; Svetlozar T Rachev |
| Abstract: | Financial returns are known to exhibit heavy tails, volatility clustering and abrupt jumps that are poorly captured by classical diffusion models. Advances in machine learning have enabled highly flexible functional forms for conditional means and volatilities, yet few models deliver interpretable state--dependent tail risk, capture multiple forecast horizons and yield distributions amenable to backtesting and execution. This paper proposes a neural L\'evy jump--diffusion framework that jointly learns, as functions of observable state variables, the conditional drift, diffusion, jump intensity and jump size distribution. We show how a single shared encoder yields multiple forecasting heads corresponding to distinct horizons (daily, weekly, etc.), facilitating multi--horizon density forecasts and risk measures. The state vector includes conventional price and volume features as well as novel complexity measures such as permutation entropy and recurrence quantification analysis determinism, which quantify predictability in the underlying process. Estimation is based on a quasi--maximum likelihood approach that separates diffusion and jump contributions via bipower variation weights and incorporates monotonicity and smoothness regularisation to ensure identifiability. A cost--aware portfolio optimiser translates the model's conditional densities into implementable trading strategies under leverage, turnover and no--trade--band constraints. Extensive empirical analyses on cross--sectional equity data demonstrate improved calibration, sharper tail control and economically significant risk reduction relative to baseline diffusive and GARCH benchmarks. The proposed framework is therefore an interpretable, testable and practically deployable method for state--dependent risk and density forecasting. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01041 |
| By: | Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Abeeb Olaniran (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This paper explores the role of mining activity, proxied by growth rates of electricity consumption and cost of mining, as a driver of pricing inefficiencies in cryptocurrencies. Utilizing alternative measures of crash risk proxied by the realized negative coefficient of skewness and realized down-to-up volatility, derived based on 5-minute intraday Bitcoin data, nonparametric causality-in-quantiles tests, along with sign analysis, captured by the estimates of partial average derivatives, provide evidence that mining activity can, in general, predict an increase in the entire conditional distribution of crash risk, with the strongest impact associated over the normal (median) to moderately high (upper quantiles) levels of risk. Despite the emergence of these assets in international transactions and as an investment vehicle, our results suggest that decentralized mining process can contribute to inefficiencies in the pricing of cryptocurrencies, putting further doubt into the role of these assets as a medium of exchange, alternative to conventional assets. |
| Keywords: | Crypto currencies, Crash risk, Mining activity, Nonparametric causality-in-quantiles |
| JEL: | C22 C53 G10 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202530 |
| By: | Fabrizio Lillo; Piero Mazzarisi; Ioanna-Yvonni Tsaknaki |
| Abstract: | The Kelly criterion provides a general framework for optimizing the growth rate of an investment portfolio over time by maximizing the expected logarithmic utility of wealth. However, the optimality condition of the Kelly criterion is highly sensitive to accurate estimates of the probabilities and investment payoffs. Estimation risk can lead to greatly suboptimal portfolios. In a simple binomial model, we show that the introduction of a European option in the Kelly framework can be used to construct a class of growth optimal portfolios that are robust to estimation risk. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.18868 |
| By: | Sergio Bianchi; Daniele Angelini; Massimiliano Frezza; Augusto Pianese |
| Abstract: | Volatility, as a primary indicator of financial risk, forms the foundation of classical frameworks such as Markowitz's Portfolio Theory and the Efficient Market Hypothesis (EMH). However, its conventional use rests on assumptions-most notably, the Markovian nature of price dynamics-that often fail to reflect key empirical characteristics of financial markets. Fractional stochastic volatility models expose these limitations by demonstrating that volatility alone is insufficient to capture the full structure of return dispersion. In this context, we propose pointwise regularity, measured via the Hurst-Holder exponent, as a complementary metric of financial risk. This measure quantifies local deviations from martingale behavior, enabling a more nuanced assessment of market inefficiencies and the mechanisms by which equilibrium is restored. By accounting not only for the magnitude but also for the nature of randomness, this framework bridges the conceptual divide between efficient market theory and behavioral finance. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11649 |
| By: | Maciej Wysocki |
| Abstract: | This paper examines systematic put-writing strategies applied to S&P 500 Index options, with a focus on position sizing as a key determinant of long-term performance. Despite the well-documented volatility risk premium, where implied volatility exceeds realized volatility, the practical implementation of short-dated volatility-selling strategies remains underdeveloped in the literature. This study evaluates three position sizing approaches: the Kelly criterion, VIX-based volatility regime scaling, and a novel hybrid method combining both. Using SPXW options with expirations from 0 to 5 days, the analysis explores a broad design space, including moneyness levels, volatility estimators, and memory horizons. Results show that ultra-short-dated, far out-of-the-money options deliver superior risk-adjusted returns. The hybrid sizing method consistently balances return generation with robust drawdown control, particularly under low-volatility conditions such as those seen in 2024. The study offers new insights into volatility harvesting, introducing a dynamic sizing framework that adapts to shifting market regimes. It also contributes practical guidance for constructing short-dated option strategies that are robust across market environments. These findings have direct applications for institutional investors seeking to enhance portfolio efficiency through systematic exposure to volatility premia. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.16598 |
| By: | Jakub Micha\'nk\'ow |
| Abstract: | This study evaluates deep neural networks for forecasting probability distributions of financial returns. 1D convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) architectures are used to forecast parameters of three probability distributions: Normal, Student's t, and skewed Student's t. Using custom negative log-likelihood loss functions, distribution parameters are optimized directly. The models are tested on six major equity indices (S\&P 500, BOVESPA, DAX, WIG, Nikkei 225, and KOSPI) using probabilistic evaluation metrics including Log Predictive Score (LPS), Continuous Ranked Probability Score (CRPS), and Probability Integral Transform (PIT). Results show that deep learning models provide accurate distributional forecasts and perform competitively with classical GARCH models for Value-at-Risk estimation. The LSTM with skewed Student's t distribution performs best across multiple evaluation criteria, capturing both heavy tails and asymmetry in financial returns. This work shows that deep neural networks are viable alternatives to traditional econometric models for financial risk assessment and portfolio management. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.18921 |
| By: | Nektarios Aslanidis; Aurelio Bariviera; George Kapetanios; Vasilis Sarafidis |
| Abstract: | This paper analyzes realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader economy-wide risk sources: macro-financial proxies are unavailable at high-frequencies, while the abundance of low-frequency candidates offers limited guidance on empirical relevance. To address this, we develop a two-stage ``divide-and-conquer'' approach. The first stage estimates exposures to high-frequency idiosyncratic and market risk only, using asset-level IV regressions. The second stage identifies latent economy-wide factors by extracting the leading principal component from the model residuals and mapping it to lower-frequency macro-financial uncertainty and sentiment-based indicators via high-dimensional variable selection. Structured patterns of heterogeneity in exposures are uncovered using Mean Group estimators across asset categories. The method is applied to a broad sample of crypto assets, covering more than 80% of total market capitalization. We document short-term mean reversion and significant average exposures to idiosyncratic volatility and illiquidity. Green and DeFi assets are, on average, more exposed to market-level and economy-wide risk than their non-Green and non-DeFi counterparts. By contrast, stablecoins are less exposed to idiosyncratic, market-level, and economy-wide risk factors relative to non-stablecoins. At a conceptual level, our study develops a coherent framework for isolating distinct layers of risk in crypto markets. Empirically, it sheds light on how return sensitivities vary across digital asset categories -- insights that are important for both portfolio design and regulatory oversight. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.21100 |
| By: | Rupendra Yadav; Aparna Mehra |
| Abstract: | This study introduces a portfolio optimization framework to minimize mixed conditional value at risk (MCVaR), incorporating a chance constraint on expected returns and limiting the number of assets via cardinality constraints. A robust MCVaR model is presented, which presumes ellipsoidal support for random returns without assuming any distribution. The model utilizes an uncertainty set grounded in a reproducing kernel Hilbert space (RKHS) to manage the chance constraint, resulting in a simplified second-order cone programming (SOCP) formulation. The performance of the robust model is tested on datasets from six distinct financial markets. The outcomes of comprehensive experiments indicate that the robust model surpasses the nominal model, market portfolio, and equal-weight portfolio with higher expected returns, lower risk metrics, enhanced reward-risk ratios, and a better value of Jensen's alpha in many cases. Furthermore, we aim to validate the robust models in different market phases (bullish, bearish, and neutral). The robust model shows a distinct advantage in bear markets, providing better risk protection against adverse conditions. In contrast, its performance in bullish and neutral phases is somewhat similar to that of the nominal model. The robust model appears effective in volatile markets, although further research is necessary to comprehend its performance across different market conditions. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.00447 |
| By: | Michael Schmutz; Eckhard Platen; Thorsten Schmidt |
| Abstract: | In this paper we study the pricing and hedging of nonreplicable contingent claims, such as long-term insurance contracts like variable annuities. Our approach is based on the benchmark-neutral pricing framework of Platen (2024), which differs from the classical benchmark approach by using the stock growth optimal portfolio as the num\'eraire. In typical settings, this choice leads to an equivalent martingale measure, the benchmark-neutral measure. The resulting prices can be significantly lower than the respective risk-neutral ones, making this approach attractive for long-term risk-management. We derive the associated risk-minimizing hedging strategy under the assumption that the contingent claim possesses a martingale decomposition. For a set of nonreplicable contingent claims, these strategies allow monitoring the working capital required to generate their payoffs and enable an assessment of the resulting diversification effects. Furthermore, an algorithmic refinancing strategy is proposed that allows modeling the working capital. Finally, insurance-finance arbitrages of the first kind are introduced and it is demonstrated that benchmark-neutral pricing effectively avoids such arbitrages. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.19494 |
| By: | Zixing Chen; Yihan Qi; Shanlan Que; Julian Sester; Xiao Zhang |
| Abstract: | In this paper we study the quality of model-free valuation approaches for financial derivatives by systematically evaluating the difference between model-free super-hedging strategies and the realized payoff of financial derivatives using historical option prices from several constituents of the S&P 500 between 2018 and 2022. Our study allows in particular to describe the realized gap between payoff and model-free hedging strategy empirically so that we can quantify to which degree model-free approaches are overly conservative. Our results imply that the model-free hedging approach is only marginally more conservative than industry-standard models such as the Heston-model while being model-free at the same time. This finding, its statistical description and the model-independence of the hedging approach enable us to construct an explicit trading strategy which, as we demonstrate, can be profitably applied in financial markets, and additionally possesses the desirable feature with an explicit control of its downside risk due to its model-free construction preventing losses pathwise. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.16595 |
| By: | Shaofeng Kang; Zeying Tian |
| Abstract: | We propose a two-level, learning-based portfolio method (RL-BHRP) that spreads risk across sectors and stocks, and adjusts exposures as market conditions change. Using U.S. Equities from 2012 to mid-2025, we design the model using 2012 to 2019 data, and evaluate it out-of-sample from 2020 to 2025 against a sector index built from exchange-traded funds and a static risk-balanced portfolio. Over the test window, the adaptive portfolio compounds wealth by approximately 120 percent, compared with 101 percent for the static comparator and 91 percent for the sector benchmark. The average annual growth is roughly 15 percent, compared to 13 percent and 12 percent, respectively. Gains are achieved without significant deviations from the benchmark and with peak-to-trough losses comparable to those of the alternatives, indicating that the method adds value while remaining diversified and investable. Weight charts show gradual shifts rather than abrupt swings, reflecting disciplined rebalancing and the cost-aware design. Overall, the results support risk-balanced, adaptive allocation as a practical approach to achieving stronger and more stable long-term performance. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11856 |
| By: | Umberto Collodel |
| Abstract: | This paper develops a novel method to simulate financial market reactions to European Central Bank (ECB) press conferences using a Large Language Model (LLM). We create a behavioral, agent-based simulation of 30 synthetic traders, each with distinct risk preferences, cognitive biases, and interpretive styles. These agents forecast Euro interest rate swap levels at 3-month, 2-year, and 10-year maturities, with the variation across forecasts serving as a measure of market uncertainty or disagreement. We evaluate three prompting strategies, naive, few-shot (enriched with historical data), and an advanced iterative 'LLM-as-a-Judge' framework, to assess the effect of prompt design on predictive performance. Even the naive approach generates a strong correlation (roughly 0.5) between synthetic disagreement and actual market outcomes, particularly for longer-term maturities. The LLM-as-a-Judge framework further improves accuracy at the first iteration. These results demonstrate that LLM-driven simulations can capture interpretive uncertainty beyond traditional measures, providing central banks with a practical tool to anticipate market reactions, refine communication strategies, and enhance financial stability. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.13635 |
| By: | Reitmeier, Lea; Dookie, Denyse; Rozer, Viktor |
| Abstract: | This report aims to enhance understanding of sovereign catastrophe bonds, a type of insurance-linked security, as a tool in comprehensive disaster risk reduction. Traditional disaster risk finance tools, such as insurance and reserve funds, remain important, but catastrophe bonds are gaining attention as a specialised option. Interest in their use is particularly strong in developing countries, where multilateral development banks are expanding support for catastrophe bonds. |
| JEL: | E6 R14 J01 |
| Date: | 2025–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129330 |
| By: | Hellenkamp, Detlef |
| Abstract: | The European, and in particular the German, banking sector is in a phase of profound structural transformation that is characterised by the simultaneous impact and interaction of several macro-structural drivers. Advancing digitalisation - particularly through artificial intelligence (AI) and distributed ledger technology (DLT) - ESG integration as a strategic and regulatory imperative, a tightening regulatory framework (including Basel IV, DORA, EU AI Act, MiCA), demographic changes and intensified competition from digital players and changing customer behaviour are presenting banks with profound challenges. This discussion paper explains the impact of these drivers on business models, risk management, operational resilience, regulatory adjustment requirements and the strategic positioning of banks in the German and European context. It shows that the simultaneous management of these transformations - under conditions of increased complexity and rising demands on capital, technology and personnel - requires integrated management approaches and far-reaching organisational adjustments.In particular, the focus is on: the strategic use of AI, taking into account ethical and regulatory limits, the anchoring of ESG in risk management and product strategy, the impact of Basel IV regulations on the capital structure, and the relevance of demographic shifts for customer interfaces, HR strategies and sales models. The work concludes with the formulation of strategic imperatives for banks as an approach to a future-oriented, resilient and competitive realignment. |
| Keywords: | Strukturwandel Bankwesen; Digitalisierung Banken; KI-Bankwesen; ESG-Banken; Regulierung Banken; DORA (Digital Operational Resilience Act; Tokenisierung Finanzsektor; Risikomanagement Banken; DLT-Banken; Cybersicherheit Banken |
| JEL: | G21 G28 Q33 |
| Date: | 2025–05–07 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125913 |
| By: | Zbigniew Palmowski; Pawe{\l} St\c{e}pniak |
| Abstract: | This paper presents a derivation of the explicit price for the perpetual American put option in the Black-Scholes model, time-capped by the first drawdown epoch beyond a predefined level. We demonstrate that the optimal exercise strategy involves executing the option when the asset price first falls below a specified threshold. The proof relies on martingale arguments and the fluctuation theory of L\'evy processes. To complement the theoretical findings, we provide numerical analysis. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.00999 |
| By: | Sabrina Aufiero; Silvia Bartolucci; Fabio Caccioli; Pierpaolo Vivo |
| Abstract: | This work explores the formation and propagation of systemic risks across traditional finance (TradFi) and decentralized finance (DeFi), offering a comparative framework that bridges these two increasingly interconnected ecosystems. We propose a conceptual model for systemic risk formation in TradFi, grounded in well-established mechanisms such as leverage cycles, liquidity crises, and interconnected institutional exposures. Extending this analysis to DeFi, we identify unique structural and technological characteristics - such as composability, smart contract vulnerabilities, and algorithm-driven mechanisms - that shape the emergence and transmission of risks within decentralized systems. Through a conceptual mapping, we highlight risks with similar foundations (e.g., trading vulnerabilities, liquidity shocks), while emphasizing how these risks manifest and propagate differently due to the contrasting architectures of TradFi and DeFi. Furthermore, we introduce the concept of crosstagion, a bidirectional process where instability in DeFi can spill over into TradFi, and vice versa. We illustrate how disruptions such as liquidity crises, regulatory actions, or political developments can cascade across these systems, leveraging their growing interdependence. By analyzing this mutual dynamics, we highlight the importance of understanding systemic risks not only within TradFi and DeFi individually, but also at their intersection. Our findings contribute to the evolving discourse on risk management in a hybrid financial ecosystem, offering insights for policymakers, regulators, and financial stakeholders navigating this complex landscape. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.12007 |
| By: | Hamza Hanbali |
| Abstract: | This paper investigates the benefits of incorporating diversification effects into the pricing process of insurance policies from two different business lines. The paper shows that, for the same risk reduction, insurers pricing policies jointly can have a competitive advantage over those pricing them separately. However, the choice of competitiveness constrains the underwriting flexibility of joint pricers. The paper goes a step further by modeling explicitly the relationship between premiums and the number of customers in each line. Using the total collected premiums as a criterion to compare the competing strategies, the paper provides conditions for the optimal pricing decision based on policyholders' sensitivity to price discounts. The results are illustrated for a portfolio of annuities and assurances. Further, using non-life data from the Brazilian insurance market, an empirical exploration shows that most pairs satisfy the condition for being priced jointly, even when pairwise correlations are high. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.13409 |
| By: | Zhi Chen; Zachary Feinstein; Ionut Florescu |
| Abstract: | Environmental, Social, and Governance (ESG) factors aim to provide non-financial insights into corporations. In this study, we investigate whether we can extract relevant ESG variables to assess corporate risk, as measured by logarithmic volatility. We propose a novel Hierarchical Variable Selection (HVS) algorithm to identify a parsimonious set of variables from raw data that are most relevant to risk. HVS is specifically designed for ESG datasets characterized by a tree structure with significantly more variables than observations. Our findings demonstrate that HVS achieves significantly higher performance than models using pre-aggregated ESG scores. Furthermore, when compared with traditional variable selection methods, HVS achieves superior explanatory power using a more parsimonious set of ESG variables. We illustrate the methodology using company data from various sectors of the US economy. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.18679 |
| By: | Sedigheh Mahdavi (Kristin); Jiating (Kristin); Chen; Pradeep Kumar Joshi; Lina Huertas Guativa; Upmanyu Singh |
| Abstract: | Large Language Models (LLMs) have been employed in financial decision making, enhancing analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01990 |
| By: | Luca Benzoni; Marisa Wernick |
| Abstract: | We examine the 2025 U.S. debt limit episode through the lens of financial markets. First, we document an increase in trading activity in the U.S. sovereign CDS market, and we infer a probability of default from CDS premiums. We find that default risk reached 1% by the November 6 Presidential election, fell quickly after that, and progressively climbed back up in subsequent months to the current 1.1% level. Overall, these estimates are well below the default risk estimates for the debt-limit episodes of 2011, 2013, and 2023, which range from 4% to 6%. Second, so far we only find small distortions in the market for Treasury bills that mature around the “X-date, ” when Treasury is expected to extinguish its existing resources, and thus would be most affected by a hypothetical default. This is in contrast with the 2023 episode, when bills maturing around the X-date traded with a yield that was about 1% higher than those maturing in other months. Third, we discuss the broader consequences that debt-limit events can have for the level of bank reserves at the Federal Reserve, and their implications for money markets liquidity. |
| Keywords: | U.S. Default; Default probability; CDS; Debt limit |
| JEL: | G10 G12 G18 G28 E32 E43 E44 |
| Date: | 2025–05–27 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedhwp:101720 |
| By: | Anna Mancini; Bal\'azs Lengyel; Riccardo Di Clemente; Giulio Cimini |
| Abstract: | Recent crises like the COVID-19 pandemic and geopolitical tensions have exposed vulnerabilities and caused disruptions of supply chains, leading to product shortages, increased costs, and economic instability. This has prompted increasing efforts to assess systemic risk, namely the effects of firm disruptions on entire economies. However, the ability of firms to react to crises by rewiring their supply links has been largely overlooked, limiting our understanding of production networks resilience. Here we study dynamics and determinants of firm-level systemic risk in the Hungarian production network from 2015 to 2022. We use as benchmark a heuristic maximum entropy null model that generates an ensemble of production networks at equilibrium, by preserving the total input (demand) and output (supply) of each firm at the sector level. We show that the fairly stable set of firms with highest systemic risk undergoes a structural change during COVID-19, as those enabling economic exchanges become key players in the economy -- a result which is not reproduced by the null model. Although the empirical systemic risk aligns well with the null value until the onset of the pandemic, it becomes significantly smaller afterwards as the adaptive behavior of firms leads to a more resilient economy. Furthermore, firms' international trade volume (being a subject of disruption) becomes a significant predictor of their systemic risk. However, international links cannot provide an unequivocal explanation for the observed trends, as imports and exports have opposing effects on local systemic risk through the supply and demand channels. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.21426 |
| By: | Ahmet Umur \"Ozsoy |
| Abstract: | We reinterpret and propose a framework for pricing path-dependent financial derivatives by estimating the full distribution of payoffs using Distributional Reinforcement Learning (DistRL). Unlike traditional methods that focus on expected option value, our approach models the entire conditional distribution of payoffs, allowing for risk-aware pricing, tail-risk estimation, and enhanced uncertainty quantification. We demonstrate the efficacy of this method on Asian options, using quantile-based value function approximators. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.12657 |
| By: | Oytun Ha\c{c}ar{\i}z; Torsten Kleinow; Angus S. Macdonald |
| Abstract: | We review Markov models of surplus in life insurance based on a counting process following Norberg (1991), uniting probabilistic theory with elements of practice largely drawn from UK experience. First, we organize models systematically based on one and two technical bases, including a suitable descriptive notation. Extending this to three technical bases to accommodate different valuation approaches leads us: (a) to expand the definition of 'technical basis' to include non-contractual cashflows recognized in the associated Thiele equation; and (b) to add new (mainly) systematic terms to the surplus. Making these cashflows dynamic or 'quasi-contractual' covers many real applications, and we give two as examples, the paid-up valuation principle and reversionary bonus on participating contracts. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.00011 |
| By: | Fiona Xiao Jingyi; Lili Liu |
| Abstract: | Forecasting central bank policy decisions remains a persistent challenge for investors, financial institutions, and policymakers due to the wide-reaching impact of monetary actions. In particular, anticipating shifts in the U.S. federal funds rate is vital for risk management and trading strategies. Traditional methods relying only on structured macroeconomic indicators often fall short in capturing the forward-looking cues embedded in central bank communications. This study examines whether predictive accuracy can be enhanced by integrating structured data with unstructured textual signals from Federal Reserve communications. We adopt a multi-modal framework, comparing traditional machine learning models, transformer-based language models, and deep learning architectures in both unimodal and hybrid settings. Our results show that hybrid models consistently outperform unimodal baselines. The best performance is achieved by combining TF-IDF features of FOMC texts with economic indicators in an XGBoost classifier, reaching a test AUC of 0.83. FinBERT-based sentiment features marginally improve ranking but perform worse in classification, especially under class imbalance. SHAP analysis reveals that sparse, interpretable features align more closely with policy-relevant signals. These findings underscore the importance of integrating textual and structured signals transparently. For monetary policy forecasting, simpler hybrid models can offer both accuracy and interpretability, delivering actionable insights for researchers and decision-makers. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.22763 |
| By: | Boris Kriuk; Logic Ng; Zarif Al Hossain |
| Abstract: | Support and resistance (SR) levels are central to technical analysis, guiding traders in entry, exit, and risk management. Despite widespread use, traditional SR identification methods often fail to adapt to the complexities of modern, volatile markets. Recent research has introduced machine learning techniques to address the following challenges, yet most focus on price prediction rather than structural level identification. This paper presents DeepSupp, a new deep learning approach for detecting financial support levels using multi-head attention mechanisms to analyze spatial correlations and market microstructure relationships. DeepSupp integrates advanced feature engineering, constructing dynamic correlation matrices that capture evolving market relationships, and employs an attention-based autoencoder for robust representation learning. The final support levels are extracted through unsupervised clustering, leveraging DBSCAN to identify significant price thresholds. Comprehensive evaluations on S&P 500 tickers demonstrate that DeepSupp outperforms six baseline methods, achieving state-of-the-art performance across six financial metrics, including essential support accuracy and market regime sensitivity. With consistent results across diverse market conditions, DeepSupp addresses critical gaps in SR level detection, offering a scalable and reliable solution for modern financial analysis. Our approach highlights the potential of attention-based architectures to uncover nuanced market patterns and improve technical trading strategies. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01971 |
| By: | Shrenik Jadhav; Birva Sevak; Srijita Das; Akhtar Hussain; Wencong Su; Van-Hai Bui |
| Abstract: | Peer-to-peer (P2P) trading is increasingly recognized as a key mechanism for decentralized market regulation, yet existing approaches often lack robust frameworks to ensure fairness. This paper presents FairMarket-RL, a novel hybrid framework that combines Large Language Models (LLMs) with Reinforcement Learning (RL) to enable fairness-aware trading agents. In a simulated P2P microgrid with multiple sellers and buyers, the LLM acts as a real-time fairness critic, evaluating each trading episode using two metrics: Fairness-To-Buyer (FTB) and Fairness-Between-Sellers (FBS). These fairness scores are integrated into agent rewards through scheduled {\lambda}-coefficients, forming an adaptive LLM-guided reward shaping loop that replaces brittle, rule-based fairness constraints. Agents are trained using Independent Proximal Policy Optimization (IPPO) and achieve equitable outcomes, fulfilling over 90% of buyer demand, maintaining fair seller margins, and consistently reaching FTB and FBS scores above 0.80. The training process demonstrates that fairness feedback improves convergence, reduces buyer shortfalls, and narrows profit disparities between sellers. With its language-based critic, the framework scales naturally, and its extension to a large power distribution system with household prosumers illustrates its practical applicability. FairMarket-RL thus offers a scalable, equity-driven solution for autonomous trading in decentralized energy systems. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.22708 |