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on Risk Management |
| By: | Kyle Sung; Traian A. Pirvu |
| Abstract: | We provide an explicit solution for optimal option portfolios under variance and Value at Risk (VaR) minimization when the underlying returns follow a Student t-distribution. The novelty of our paper is the departure from the traditional normal returns setting. Our main contribution is the methodology for obtaining optimal portfolios. Numerical experiments reveal that, as expected, the optimal variance and VaR portfolio compositions differ by a significant amount, suggesting that more realistic tail risk settings can lead to potentially more realistic portfolio allocations. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.07991 |
| By: | Zaniar Ahmadi; Fr\'ed\'eric Godin |
| Abstract: | This paper investigates the deep hedging framework, based on reinforcement learning (RL), for the dynamic hedging of swaptions, contrasting its performance with traditional sensitivity-based rho-hedging. We design agents under three distinct objective functions (mean squared error, downside risk, and Conditional Value-at-Risk) to capture alternative risk preferences and evaluate how these objectives shape hedging styles. Relying on a three-factor arbitrage-free dynamic Nelson-Siegel model for our simulation experiments, our findings show that near-optimal hedging effectiveness is achieved when using two swaps as hedging instruments. Deep hedging strategies dynamically adapt the hedging portfolio's exposure to risk factors across states of the market. In our experiments, their out-performance over rho-hedging strategies persists even in the presence some of model misspecification. These results highlight RL's potential to deliver more efficient and resilient swaption hedging strategies. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.06639 |
| By: | Marek Folprecht |
| Abstract: | The study presents a holistic approach to modeling flood risk of real estate properties. The method combines the hydrological flow simulation model and a model of financial losses. Two use cases of the model are discussed. First, a stress testing method, based on historical scenario simulations, is presented. Next, a Value at Risk approach using the Generalized extreme value distribution and the Gumbel copula is discussed. Both methods are then tested on a large sample of Czech house data. The results show that the model can replicate the order of historical flood magnitudes under the historical scenarios. Moreover, the Value at Risk approach can generate scenarios unseen in recent history. The model could be a useful flood losses modeling tool for banks, insurance companies, real estate investment companies or state agencies. A special case for stressing credit risk parameters for mortgage portfolios is discussed in more detail. |
| Keywords: | Flood risk, Generalized extreme value, Gumbel copula, Value at Risk, Monte Carlo, Czech Republic, Stress Testing |
| Date: | 2025–12–14 |
| URL: | https://d.repec.org/n?u=RePEc:prg:jnlwps:v:6:y:2026:id:6.001 |
| By: | Ekleen Kaur |
| Abstract: | The integration of cryptocurrencies into institutional portfolios necessitates the adoption of robust risk modeling frameworks. This study is a part of a series of subsequent works to fine-tune model risk analysis for cryptocurrencies. Through this first research work, we establish a foundational benchmark by applying the traditional industry-standard Geometric Brownian Motion (GBM) model. Popularly used for non-crypto financial assets, GBM assumes Lognormal return distributions for a multi-asset cryptocurrency portfolio (XRP, SOL, ADA). This work utilizes Maximum Likelihood Estimation and a correlated Monte Carlo Simulation incorporating the Cholesky decomposition of historical covariance. We present our stock portfolio model as a Minimum Variance Portfolio (MVP). We observe the model's structural shift within the heavy-tailed, non-Gaussian cryptocurrency environment. The results reveal limitations of the Lognormal assumption: the calculated Value-at-Risk at the 5% confidence level over the one-year horizon. For baselining our results, we also present a holistic comparative analysis with an equity portfolio (AAPL, TSLA, NVDA), demonstrating a significantly lower failure rate. This performance provides conclusive evidence that the GBM model is fundamentally the perfect benchmark for our subsequent works. Results from this novel work will be an indicator for the success criteria in our future model for crypto risk management, rigorously motivating the development and application of advanced models. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.14272 |
| By: | Giuseppe Brandi; Tiziana Di Matteo |
| Abstract: | The rough Bergomi (rBergomi) model, characterised by its roughness parameter $H$, has been shown to exhibit multiscaling behaviour as $H$ approaches zero. Multiscaling has profound implications for financial modelling: it affects extreme risk estimation, influences optimal portfolio allocation across different time horizons, and challenges traditional option pricing approaches that assume uniscaling behaviours. Understanding whether multiscaling arises primarily from the roughness of volatility paths or from the resulting fat-tailed returns has important implications for financial modelling, option pricing, and risk management. This paper investigates the real source of this multiscaling behaviour by introducing a novel two-stage statistical testing procedure. In the first stage, we establish the presence of multiscaling in the rBergomi model against an uniscaling fractional Brownian motion process. We quantify multiscaling by using weighted least squares regression that accounts for heteroscedastic estimation errors across moments. In the second stage, we apply shuffled surrogates that preserve return distributions while destroying temporal correlations. This is done by using distance-based permutation tests robust to asymmetric null distributions. In order to validate our procedure, we check the robustness of the results by using synthetic processes with known multifractal properties, namely the Multifractal Random Walk (MRW) and the Fractional L\'evy Stable Motion (FLSM). We provide compelling evidence that multiscaling in the rBergomi model arises primarily from fat-tailed return distributions rather than memory effects. Our findings suggest that the apparent multiscaling in rough volatility models is largely attributable to distributional properties rather than genuine temporal scaling behaviour. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11305 |
| By: | Xia Han; Bin Li; Yao Luo |
| Abstract: | We propose a new framework for studying optimal insurance under information asymmetry within the Stackelberg game framework. In this setting, a monopolistic insurer faces uncertainty regarding a customer's loss distribution or risk attitude. The customer is assumed to follow a mean–variance preference in continuous time, while the insurer sets premiums through a risk loading based on the expected loss. An optimal menu is explicitly derived for a general class of aggregate loss models. Our approach connects with the extensive literature on optimal insurance demand, stemming from the seminal work of Arrow (1963), and leads to an interesting finding: a nonlinear pricing structure for risk-type uncertainty versus a linear pricing structure for risk-attitude uncertainty. Specifically, if an insurer is uncertain about a customer's risk type and seeks to elicit this information, the risk loading (premium minus expected loss) is set lower for high-risk individuals to encourage them to select the corresponding contract. In contrast, if the insurer is only uncertain about the customer's risk attitude, no such discounts---in terms of risk loading---are provided. This reveals that information about customers' risk types is more valuable than information about their risk attitudes. Additionally, we compare our optimal menu with the worst-case contract derived from the maxmin expected utility, we find that our optimal menu increases the insurer's expected profit and enhances the likelihood of trading. |
| Keywords: | Optimal insurance; Information asymmetry; Stackelberg game framework; Risk loading; Nonlinear pricing; Linear pricing; Ambiguity |
| JEL: | D82 G22 D81 |
| Date: | 2026–01–22 |
| URL: | https://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-815 |
| By: | Christophe Hurlin; Quentin Lajaunie; Yoann Pull |
| Abstract: | This paper proposes a formal framework for reverse stress testing geopolitical risk in corporate credit portfolios. A joint macro-financial scenario vector, augmented with an explicit geopolitical risk factor, is mapped into stressed probabilities of default and losses given default. These stresses are then propagated to portfolio tail losses through a latent factor structure and translated into a stressed CET1 ratio, jointly accounting for capital depletion and risk-weighted asset dynamics. Reverse stress testing is formulated as a constrained maximum likelihood problem over the scenario space. This yields a geopolitical point reverse stress test, or design point, defined as the most probable scenario that breaches a prescribed capital adequacy constraint under a reference distribution. The framework further characterises neighbourhoods and near optimal sets of reverse stress scenarios, allowing for sensitivity analysis and governance oriented interpretation. The approach is compatible with internal rating based models and supports implementation at the exposure or sector level. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.03983 |
| By: | O. Didkovskyi; A. Vidali; N. Jean; G. Le Pera |
| Abstract: | This paper presents a meta-learning framework for credit risk assessment of Italian Small and Medium Enterprises (SMEs) that explicitly addresses the temporal misalignment of credit scoring models. The approach aligns financial statement reference dates with evaluation dates, mitigating bias arising from publication delays and asynchronous data sources. It is based on a two-step temporal decomposition that at first estimates annual probabilities of default (PDs) anchored to balance-sheet reference dates (December 31st) through a static model. Then it models the monthly evolution of PDs using higher-frequency behavioral data. Finally, we employ stacking-based architecture to aggregate multiple scoring systems, each capturing complementary aspects of default risk, into a unified predictive model. In this way, first level model outputs are treated as learned representations that encode non-linear relationships in financial and behavioral indicators, allowing integration of new expert-based features without retraining base models. This design provides a coherent and interpretable solution to challenges typical of low-default environments, including heterogeneous default definitions and reporting delays. Empirical validation shows that the framework effectively captures credit risk evolution over time, improving temporal consistency and predictive stability relative to standard ensemble methods. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.07588 |
| By: | Shiyu Zhang; Zining Wang; Jin Zheng; John Cartlidge |
| Abstract: | Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing decentralized finance (DeFi) ecosystem, numerous studies have analyzed systemic risk through specific channels such as liquidity pressures, leverage mechanisms, smart contract risks, and historical risk events. However, these studies are mostly event-driven or focused on isolated risk channels, paying limited attention to the structural dimension of systemic risk. Overall, this study provides a unified quantitative framework for ecosystem-level analysis and continuous monitoring of systemic risk in DeFi. From a network-based perspective, this paper proposes the DeFi Correlation Fragility Indicator (CFI), constructed from time-varying correlation networks at the protocol category level. The CFI captures ecosystem-wide structural fragility associated with correlation concentration and increasing synchronicity. Furthermore, we define a Risk Contribution Score (RCS) to quantify the marginal contribution of different protocol types to overall systemic risk. By combining the CFI and RCS, the framework enables both the tracking of time-varying systemic risk and identification of structurally important functional modules in risk accumulation and amplification. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.08540 |
| By: | Jianwei Peng (Humboldt-Universit\"at zu Berlin); Stefan Lessmann (Humboldt-Universit\"at zu Berlin; Bucharest University of Economic Studies) |
| Abstract: | Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing methods that implicitly assume a stationary data-generating process, in practise, mortgage portfolios are exposed to various forms of data drift caused by changing borrower behaviour, macroeconomic conditions, policy regimes and so on. This study investigates the impact of data drift on survival-based credit risk models and proposes a dynamic joint modelling framework to improve robustness under non-stationary environments. The proposed model integrates a longitudinal behavioural marker derived from balance dynamics with a discrete-time hazard formulation, combined with landmark one-hot encoding and isotonic calibration. Three types of data drift (sudden, incremental and recurring) are simulated and analysed on mortgage loan datasets from Freddie Mac. Experiments and corresponding evidence show that the proposed landmark-based joint model consistently outperforms classical survival models, tree-based drift-adaptive learners and gradient boosting methods in terms of discrimination and calibration across all drift scenarios, which confirms the superiority of our model design. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.20533 |
| By: | Yimeng Qiu |
| Abstract: | The classical mean-variance framework characterizes portfolio risk solely through return variance and the covariance matrix, implicitly assuming that all relevant sources of risk are captured by second moments. In modern financial markets, however, shocks often propagate through complex networks of interconnections, giving rise to systemic and spillover risks that variance alone does not reflect. This paper develops a unified portfolio optimization framework that incorporates connectedness risk alongside expected return and variance. Using a quadratic measure of network spillovers derived from a connectedness matrix, we formulate a three-objective optimization problem and characterize the resulting three-dimensional efficient surface. We establish existence, uniqueness, and continuity of optimal portfolios under mild regularity conditions and derive closed-form solutions when short-selling is allowed. The trade-off between variance and connectedness is shown to be strictly monotone except in degenerate cases, yielding a well-defined risk-risk frontier. Under simultaneous diagonalizability of the covariance and connectedness matrices, we prove a three-fund separation theorem: all efficient portfolios can be expressed as affine combinations of a minimum-variance portfolio, a minimum-connectedness portfolio, and the tangency portfolio. The framework clarifies how network-based risk alters classical diversification results and provides a transparent theoretical foundation for incorporating systemic connectedness into portfolio choice. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.06271 |
| By: | Xiangxin He; Fangda Liu; Ruodu Wang |
| Abstract: | Portfolio diversification is a cornerstone of modern finance, while risk aversion is central to decision theory; both concepts are long-standing and foundational. We investigate their connections by studying how different forms of diversification correspond to notions of risk aversion. We focus on the classical distinctions between weak and strong risk aversion, and consider diversification preferences for pairs of risks that are identically distributed, comonotonic, antimonotonic, independent, or exchangeable, as well as their intersections. Under a weak continuity condition and without assuming completeness of preferences, diversification for antimonotonic and identically distributed pairs implies weak risk aversion, and diversification for exchangeable pairs is equivalent to strong risk aversion. The implication from diversification for independent pairs to weak risk aversion requires a stronger continuity. We further provide results and examples that clarify the relationships between various diversification preferences and risk attitudes, in particular justifying the one-directional nature of many implications. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04067 |
| By: | Anubha Goel; Amita Sharma; Juho Kanniainen |
| Abstract: | Topological Data Analysis (TDA), an emerging field in investment sciences, harnesses mathematical methods to extract data features based on shape, offering a promising alternative to classical portfolio selection methodologies. We utilize persistence landscapes, a type of summary statistics for persistent homology, to capture the topological variation of returns, blossoming a novel concept of ``Topological Risk". Our proposed topological risk then quantifies portfolio risk by tracking time-varying topological properties of assets through the $L_p$ norm of the persistence landscape. Through optimization, we derive an optimal portfolio that minimizes this topological risk. Numerical experiments conducted using nearly a decade long S\&P 500 data demonstrate the superior performance of our TDA-based portfolios in comparison to the seven popular portfolio optimization models and two benchmark portfolio strategies, the naive $1/N$ portfolio and the S\&P 500 market index, in terms of excess mean return, and several financial ratios. The outcome remains consistent through out the computational analysis conducted for the varying size of holding and investment time horizon. These results underscore the potential of our TDA-based topological risk metric in providing a more comprehensive understanding of portfolio dynamics than traditional statistical measures. As such, it holds significant relevance for modern portfolio management practices. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.03974 |
| By: | Enrique G. Mendoza; Vincenzo Quadrini |
| Abstract: | We study how the increased cross-country ownership of financial assets between advanced and emerging economies impacted their financial and macroeconomic volatility. While cross-country ownership improved risk-sharing and reduced volatility associated with financial crises, it also increased the exposure of countries to foreign crises, leading to higher international co-movement. Through quantitative applications of a two-region model representative of advanced and emerging economies, we find that financial globalization reduced volatility worldwide, but significantly more in emerging economies. |
| JEL: | F40 F41 G01 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34689 |
| By: | Timo Dimitriadis; Yannick Hoga |
| Abstract: | Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk forecasts in an "online" fashion. This enables daily monitoring of the forecasts while controlling for the accumulation of false test rejections. Such online schemes are vital in taking timely countermeasures to avoid financial distress. Our monitoring procedures allow multiple series at once to be monitored, thus increasing the likelihood and the speed at which early signs of trouble may be picked up. The tests hold size by construction, such that the null of correct systemic risk assessments is only rejected during the monitoring period with (at most) a pre-specified probability. Monte Carlo simulations illustrate the good finite-sample properties of our procedures. An empirical application to US banks during multiple crises demonstrates the usefulness of our surveillance schemes for both regulators and financial institutions. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.08598 |
| By: | Belgacem, Wajdi; Parsons, Jay |
| Keywords: | Financial Economics |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361151 |
| By: | Daniele Caratelli; Jacob Lockwood; Robert Mann; Kevin Zhao |
| Abstract: | Countercyclical capital constraints allow banks to provide additional credit to consumers during recessions, smoothing consumption volatility. |
| Date: | 2026–01–22 |
| URL: | https://d.repec.org/n?u=RePEc:ofr:ofrblg:26-01 |
| By: | Hamid El-Boudaly (LERSEM - Laboratoire d’Études et de Recherches en Sciences Economique et de Management - Ecole nationale de commerce et de gestion - UCD - Université Chouaib Doukkali, UCD - Université Chouaib Doukkali); Abdelbari El Khamlichi (LERSEM - Laboratoire d’Études et de Recherches en Sciences Economique et de Management - Ecole nationale de commerce et de gestion - UCD - Université Chouaib Doukkali, UCD - Université Chouaib Doukkali) |
| Abstract: | This literature review aims to examine the evolution of articles on Islamic insurance published by leading journals. The data are sourced from the Scopus database spanning the years 1985 to 2023. The data are then processed and analyzed using the bibliometric application R to establish the bibliometric map of Takaful development. The results showed that the number of publications on the subject of Islamic insurance has increased. The most popular author is Hassan Mk, and the keywords most used in this search are Takaful, Islamic Insurance, Insurance, Islamic Finance. Trend analysis reveals new challenges faced by Takaful companies, particularly in terms of governance and risk management. Further research on the topic of Islamic insurance, focusing not only in Malaysia but also in other Muslim countries, is therefore needed to foster the development of Islamic insurance companies. |
| Keywords: | Takaful, Islamic insurance, Shariah insurance, bibliometric, R, biblioshiny, Takaful Islamic insurance Shariah insurance bibliometric R biblioshiny |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05068197 |
| By: | Masoud Soleimani |
| Abstract: | We develop a transparent and fully auditable LLM-based pipeline for macro-financial stress testing, combining structured prompting with optional retrieval of country fundamentals and news. The system generates machine-readable macroeconomic scenarios for the G7, which cover GDP growth, inflation, and policy rates, and are translated into portfolio losses through a factor-based mapping that enables Value-at-Risk and Expected Shortfall assessment relative to classical econometric baselines. Across models, countries, and retrieval settings, the LLMs produce coherent and country-specific stress narratives, yielding stable tail-risk amplification with limited sensitivity to retrieval choices. Comprehensive plausibility checks, scenario diagnostics, and ANOVA-based variance decomposition show that risk variation is driven primarily by portfolio composition and prompt design rather than by the retrieval mechanism. The pipeline incorporates snapshotting, deterministic modes, and hash-verified artifacts to ensure reproducibility and auditability. Overall, the results demonstrate that LLM-generated macro scenarios, when paired with transparent structure and rigorous validation, can provide a scalable and interpretable complement to traditional stress-testing frameworks. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.07867 |
| By: | Rushikesh Handal; Masanori Hirano |
| Abstract: | High-dimensional option pricing and hedging present significant challenges in quantitative finance, where traditional PDE-based methods struggle with the curse of dimensionality. The BSDE framework offers a computationally efficient alternative to PDE-based methods, and recently proposed deep BSDE solvers, generally utilizing conventional Multi-Layer Perceptrons (MLPs), build upon this framework to provide a scalable alternative to numerical BSDE solvers. In this research, we show that although such MLP-based deep BSDEs demonstrate promising results in option pricing, there remains room for improvement regarding hedging performance. To address this issue, we introduce KANHedge, a novel BSDE-based hedger that leverages Kolmogorov-Arnold Networks (KANs) within the BSDE framework. Unlike conventional MLP approaches that use fixed activation functions, KANs employ learnable B-spline activation functions that provide enhanced function approximation capabilities for continuous derivatives. We comprehensively evaluate KANHedge on both European and American basket options across multiple dimensions and market conditions. Our experimental results demonstrate that while KANHedge and MLP achieve comparable pricing accuracy, KANHedge provides improved hedging performance. Specifically, KANHedge achieves considerable reductions in hedging cost metrics, demonstrating enhanced risk control capabilities. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11097 |
| By: | Agostino Capponi; Stijn Van Nieuwerburgh; Xinkai Wu |
| Abstract: | Following the Great Financial Crisis (GFC), the Credit Risk Transfer (CRT) bond market emerged as a new asset class in U.S. mortgage market. We develop an asset pricing framework for CRTs consistent with Treasury, corporate bond, and housing markets. Our analysis reveals that the Government-Sponsored Enterprises compensate investors approximately fairly on average, though they overpay for low-risk tranches and underpay for high-risk ones. Additionally, the post-GFC guarantee fee increases broadly align with underlying credit risk. We find significant reverse cross-subsidization, where high-credit-risk borrowers subsidize low-risk ones. The 2023 reform partially corrected this cross-subsidization. |
| JEL: | E60 G13 G18 G22 G28 G51 R21 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34708 |
| By: | Khabbab Zakaria; Jayapaulraj Jerinsh; Andreas Maier; Patrick Krauss; Stefano Pasquali; Dhagash Mehta |
| Abstract: | Optimal Order Execution is a well-established problem in finance that pertains to the flawless execution of a trade (buy or sell) for a given volume within a specified time frame. This problem revolves around optimizing returns while minimizing risk, yet recent research predominantly focuses on addressing one aspect of this challenge. In this paper, we introduce an innovative approach to Optimal Order Execution within the US market, leveraging Deep Reinforcement Learning (DRL) to effectively address this optimization problem holistically. Our study assesses the performance of our model in comparison to two widely employed execution strategies: Volume Weighted Average Price (VWAP) and Time Weighted Average Price (TWAP). Our experimental findings clearly demonstrate that our DRL-based approach outperforms both VWAP and TWAP in terms of return on investment and risk management. The model's ability to adapt dynamically to market conditions, even during periods of market stress, underscores its promise as a robust solution. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04896 |
| By: | Fredy Pokou (MRE, CRIStAL); Jules Sadefo Kamdem (MRE); Kpante Emmanuel Gnandi (ENAC-LAB) |
| Abstract: | This paper investigates whether structural econometric models can rival machine learning in forecasting energy--macro dynamics while retaining causal interpretability. Using monthly data from 1999 to 2025, we develop a unified framework that integrates Time-Varying Parameter Structural VARs (TVP-SVAR) with advanced dependence structures, including DCC-GARCH, t-copulas, and mixed Clayton--Frank--Gumbel copulas. These models are empirically evaluated against leading machine learning techniques Gaussian Process Regression (GPR), Artificial Neural Networks, Random Forests, and Support Vector Regression across seven macro-financial and energy variables, with Brent crude oil as the central asset. The findings reveal three major insights. First, TVP-SVAR consistently outperforms standard VAR models, confirming structural instability in energy transmission channels. Second, copula-based extensions capture non-linear and tail dependence more effectively than symmetric DCC models, particularly during periods of macroeconomic stress. Third, despite their methodological differences, copula-enhanced econometric models and GPR achieve statistically equivalent predictive accuracy (t-test p = 0.8444). However, only the econometric approach provides interpretable impulse responses, regime shifts, and tail-risk diagnostics. We conclude that machine learning can replicate predictive performance but cannot substitute the explanatory power of structural econometrics. This synthesis offers a pathway where AI accuracy and economic interpretability jointly inform energy policy and risk management. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.19321 |
| By: | Haochong Xia; Simin Li; Ruixiao Xu; Zhixia Zhang; Hongxiang Wang; Zhiqian Liu; Teng Yao Long; Molei Qin; Chuqiao Zong; Bo An |
| Abstract: | Algorithmic trading relies on machine learning models to make trading decisions. Despite strong in-sample performance, these models often degrade when confronted with evolving real-world market regimes, which can shift dramatically due to macroeconomic changes-e.g., monetary policy updates or unanticipated fluctuations in participant behavior. We identify two challenges that perpetuate this mismatch: (1) insufficient robustness in existing policy against uncertainties in high-level market fluctuations, and (2) the absence of a realistic and diverse simulation environment for training, leading to policy overfitting. To address these issues, we propose a Bayesian Robust Framework that systematically integrates a macro-conditioned generative model with robust policy learning. On the data side, to generate realistic and diverse data, we propose a macro-conditioned GAN-based generator that leverages macroeconomic indicators as primary control variables, synthesizing data with faithful temporal, cross-instrument, and macro correlations. On the policy side, to learn robust policy against market fluctuations, we cast the trading process as a two-player zero-sum Bayesian Markov game, wherein an adversarial agent simulates shifting regimes by perturbing macroeconomic indicators in the macro-conditioned generator, while the trading agent-guided by a quantile belief network-maintains and updates its belief over hidden market states. The trading agent seeks a Robust Perfect Bayesian Equilibrium via Bayesian neural fictitious self-play, stabilizing learning under adversarial market perturbations. Extensive experiments on 9 financial instruments demonstrate that our framework outperforms 9 state-of-the-art baselines. In extreme events like the COVID, our method shows improved profitability and risk management, offering a reliable solution for trading under uncertain and shifting market dynamics. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.17008 |