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on Risk Management |
| By: | Sicheng Fu |
| Abstract: | This paper proposes a semiparametric joint VaRES framework driven by realized information, mo tivated by the economic mechanisms underlying tail risk generation. Building on the CAViaR quantile recursion, the model introduces a dynamic ESVaR gap to capture time-varying tail sever ity, while measurement equations transform multiple realized measures into high-frequency risk innovations.These innovations are further aggregated through a dynamic factor model, extracting common high-frequency tail risk factors that affect the quantile level and tail thickness through dis tinct risk channels. This structure explicitly separates changes in risk levels from the intensification of tail risk.Empirical evidence shows that the proposed model consistently outperforms quantile regression, EVT-based, and GARCH-type benchmarks across multiple loss functions, highlighting the importance of embedding high-frequency information directly into the tail risk generation layer |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01142 |
| By: | Albores, Isaac |
| Abstract: | This study examines the return and volatility spillovers, as well as tail-risk dynamics, between energy and agricultural commodity markets by analyzing the quantile connectedness of a system comprising key agricultural and energy commodities under extreme market conditions. We utilize a quantile vector autoregression (QVAR) model to show differences in the total connectedness index across varying market conditions and across time. Our findings show asymmetric returns spillovers between the commodities of interest, showing distinct risk transmission effects. In extreme market conditions, both bullish and bearish, we found the network connectivity of returns to be significantly stronger than under the median quantile, which represents normal market conditions. We also find under extreme scenarios, energy commodity markets tend to be more net transmitters, while the energy markets are net receivers of shocks. Our findings have implications for investors in risk management and portfolio diversification, as well as policymakers looking to manage commodity risk. |
| Keywords: | Risk and Uncertainty |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360695 |
| By: | Tim J. Boonen; Xia Han; Peng Liu; Jiacong Wang |
| Abstract: | This paper studies Pareto-optimal reinsurance design in a monopolistic market with multiple primary insurers and a single reinsurer, all with heterogeneous risk preferences. The risk preferences are characterized by a family of risk measures, called Range Value-at-Risk (RVaR), which includes both Value-at-Risk (VaR) and Expected Shortfall (ES) as special cases. Recognizing the practical difficulty of accurately estimating the dependence structure among the insurers' losses, we adopt a robust optimization approach that assumes the marginal distributions are known while leaving the dependence structure unspecified. We provide a complete characterization of optimal indemnity schedules under the worst-case scenario, showing that the infinite-dimensional optimization problem can be reduced to a tractable finite-dimensional problem involving only two or three parameters for each indemnity function. Additionally, for independent and identically distributed risks, we exploit the argument of asymptotic normality to derive optimal two-parameter layer contracts. Finally, numerical applications are considered in a two-insurer setting to illustrate the influence of the dependence structures and heterogeneous risk tolerances on optimal strategies and the corresponding risk evaluation. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.11430 |
| By: | Tsiboe, Francis; Turner, Dylan; Aglasan, Serkan; Rejesus, Roderick M. |
| Keywords: | Risk and Uncertainty |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360707 |
| By: | Ziheng Chen; Minxuan Hu; Jiayu Yi; Wenxi Sun |
| Abstract: | We extend the Q-learner in Black-Scholes (QLBS) framework by incorporating risk aversion and trading costs, and propose a novel Replication Learning of Option Pricing (RLOP) approach. Both methods are fully compatible with standard reinforcement learning algorithms and operate under market frictions. Using SPY and XOP option data, we evaluate performance along static and dynamic dimensions. Adaptive-QLBS achieves higher static pricing accuracy in implied volatility space, while RLOP delivers superior dynamic hedging performance by reducing shortfall probability. These results highlight the importance of evaluating option pricing models beyond static fit, emphasizing realized hedging outcomes. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01709 |
| By: | Julei Iuliana (Université Babeş-Bolyai de Cluj-Napoca) |
| Abstract: | This paper provides a review of the existing literature on the accounting profession and its response to uncertainty, risk, and responsibilities. The study adopts a bibliometric and systematic approach, based on a sample of 104 articles published between 2016 and 2025 in the Web of Science database. The analysis sheds light on the dynamics of the accounting profession, highlighting its growing role in risk management and strategic decision-making. The results also reveal significant gaps in the literature, particularly regarding risk culture and professional responsibilities, paving the way for future interdisciplinary research. |
| Keywords: | risk culture, responsibilities, risk, uncertainty, accounting profession, accounting profession uncertainty risk responsibilities risk culture |
| Date: | 2025–11–20 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05455624 |
| By: | Mindy L. Mallory |
| Abstract: | We identify volatility spillovers across commodities, equities, and treasuries using a hybrid HAR-ElasticNet framework on daily realized volatility for six futures markets over 2002--2025. Our two step procedure estimates own-volatility dynamics via OLS to preserve persistence, then applies ElasticNet regularization to cross-market spillovers. The sparse network structure that emerges shows equity markets (ES, NQ) act as the primary volatility transmitters, while crude oil (CL) ends up being the largest receiver of cross-market shocks. Agricultural commodities stay isolated from the larger network. A simple univariate HAR model achieves equally performing point forecasts as our model, but our approach reveals network structure that univariate models cannot. Joint Impulse Response Functions trace how shocks propagate through the network. Our contribution is to demonstrate that hybrid estimation methods can identify meaningful spillover pathways while preserving forecast performance. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.03146 |
| By: | Anna Perekhodko; Robert \'Slepaczuk |
| Abstract: | Accurate volatility forecasting is essential in banking, investment, and risk management, because expectations about future market movements directly influence current decisions. This study proposes a hybrid modelling framework that integrates a Stochastic Volatility model with a Long Short Term Memory neural network. The SV model improves statistical precision and captures latent volatility dynamics, especially in response to unforeseen events, while the LSTM network enhances the model's ability to detect complex nonlinear patterns in financial time series. The forecasting is conducted using daily data from the S and P 500 index, covering the period from January 1 1998 to December 31 2024. A rolling window approach is employed to train the model and generate one step ahead volatility forecasts. The performance of the hybrid SV-LSTM model is evaluated through both statistical testing and investment simulations. The results show that the hybrid approach outperforms both the standalone SV and LSTM models and contributes to the development of volatility modelling techniques, providing a foundation for improving risk assessment and strategic investment planning in the context of the S and P 500. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.12250 |
| By: | Walter Farkas (University of Zurich - Department Finance; Swiss Finance Institute; ETH Zürich); Fabian Sandmeier (University of Zurich - Department of Finance; Swiss Finance Institute) |
| Abstract: | We analyze solvency and liquidity implications of Credit Default Swaps (CDS) in banking networks. We emphasize that one can neither isolate them, nor just analyze them in parallel, but needs to consider their complex interplay. By calibrating our model to the largest banks in the Euro area, we are able to run a large-scale stress test and isolate the effect of different network configurations, as well as different overall coverages of CDS, on systemic risk. An increase in CDS notional always leads to an increase in liquidity risk. The impact on solvency risk is conditional on the topology of the network. We provide a robust network configuration for which an increase in CDS notional leads to a decrease in solvency risk. |
| Keywords: | Systemic Risk, Financial Networks, Credit Default Swaps, Solvency Stress Testing |
| JEL: | C63 D85 G01 G21 G28 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp25107 |
| By: | Iñaki Aldasoro; Andreas Barth; Laura Comino Suarez; Riccardo Reale |
| Abstract: | When capital requirements rise, banks can raise equity or reduce risk-weighted assets, typically by cutting lending. We show they also use credit default swaps (CDS). Linking EU trade-repository CDS data to syndicated loans for November 2017 to April 2024, we document that banks significantly increase CDS hedging on loans to firms in countries that raise their countercyclical capital buffer (CCyB). Our identification exploits within-bank comparisons of hedging for similar borrowers across countries with different CCyB rates. A 1 percentage point increase in the CCyB reduces the uninsured share of a loan by about 53 percentage points, with the strongest effects for banks most exposed to the buffer-raising country. Eligible credit risk transfer via CDS thus emerges as a first-order channel through which banks accommodate tighter capital requirements, potentially attenuating macroprudential policy transmission. |
| Keywords: | bank capital requirements, CDS, countercyclical capital buffers |
| JEL: | E51 G21 G28 G32 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1323 |
| By: | Chorok Lee |
| Abstract: | We derive a specific functional form for factor alpha decay -- hyperbolic decay alpha(t) = K/(1+lambda*t) -- from a game-theoretic equilibrium model, and test it against linear and exponential alternatives. Using eight Fama-French factors (1963--2024), we find: (1) Hyperbolic decay fits mechanical factors. Momentum exhibits clear hyperbolic decay (R^2 = 0.65), outperforming linear (0.51) and exponential (0.61) baselines -- validating the equilibrium foundation. (2) Not all factors crowd equally. Mechanical factors (momentum, reversal) fit the model; judgment-based factors (value, quality) do not -- consistent with a signal-ambiguity taxonomy paralleling Hua and Sun's "barriers to entry." (3) Crowding accelerated post-2015. Out-of-sample, the model over-predicts remaining alpha (0.30 vs. 0.15), correlating with factor ETF growth (rho = -0.63). (4) Average returns are efficiently priced. Crowding-based factor selection fails to generate alpha (Sharpe: 0.22 vs. 0.39 factor momentum benchmark). (5) Crowding predicts tail risk. Out-of-sample (2001--2024), crowded reversal factors show 1.7--1.8x higher crash probability (bottom decile returns), while crowded momentum shows lower crash risk (0.38x, p = 0.006). Our findings extend equilibrium crowding models (DeMiguel et al.) to temporal dynamics and show that crowding predicts crashes, not means -- useful for risk management, not alpha generation. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.11913 |
| By: | Cevallos, Samantha; Marco, Palma; Hernán, Bejarano |
| Keywords: | Risk and Uncertainty |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360706 |
| By: | Miryana Grigorova; James Wheeldon |
| Abstract: | We study non-linear Backward Stochastic Differential Equations (BSDEs) driven by a Brownian motion and p default martingales. The driver of the BSDE with multiple default jumps can take a generalized form involving an optional finite variation process. We first show existence and uniqueness. We then establish comparison and strict comparison results for these BSDEs, under a suitable assumption on the driver. In the case of a linear driver, we derive an explicit formula for the first component of the BSDE using an adjoint exponential semimartingale. The representation depends on whether the finite variation process is predictable or only optional. We apply our results to the problem of pricing and hedging a European option in a linear complete market with two defaultable assets and in a non-linear complete market with p defaultable assets. Two examples of the latter market model are provided: an example where the seller of the option is a large investor influencing the probability of default of a single asset and an example where the large seller's strategy affects the default probabilities of all p assets. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01250 |
| By: | Marco Ioffredi; Stefano Marmi; Matteo Tanzi |
| Abstract: | Systemic financial risk refers to the simultaneous failure or destabilization of multiple financial institutions, often triggered by contagion mechanisms or common exposures to shocks. In this paper, we present a dynamical model of bank leverage (the ratio of asset holdings to equity) a quantity that both reflects and drives risk dynamics. We model how banks, constrained by Value-at-Risk (VaR) regulations, adjust their leverage in response to changes in the price of a single asset, assumed to be held in fixed proportion across banks. This leverage-targeting behavior introduces a procyclical feedback loop between asset prices and leverage. In the dynamics, this can manifest as logistic-like behavior with a rich bifurcation structure across model parameters. By analyzing these coupled dynamics in both isolated and interconnected bank models, we outline a framework for understanding how systemic risk can emerge from seemingly rational micro-level behavior. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01505 |
| By: | Beatrice Acciaio; Brandon Garcia Flores; Antonio Marini; Gudmund Pammer |
| Abstract: | We formulate a dynamic reinsurance problem in which the insurer seeks to control the terminal distribution of its surplus while minimizing the L2-norm of the ceded risk. Using techniques from martingale optimal transport, we show that, under suitable assumptions, the problem admits a tractable solution analogous to the Bass martingale. We first consider the case where the insurer wants to match a given terminal distribution of the surplus process, and then relax this condition by only requiring certain moment or risk-based constraints. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.10375 |
| By: | Aditri |
| Abstract: | Value-at-Risk (VaR) estimation at high confidence levels is inherently a rare-event problem and is particularly sensitive to tail behavior and model misspecification. This paper studies the performance of two simulation-based VaR estimation approaches, importance sampling and discrete moment matching, under controlled tail misspecification. The analysis separates the nominal model used for estimator construction from the true data-generating process used for evaluation, allowing the effects of heavy-tailed returns to be examined in a transparent and reproducible setting. Daily returns of a broad equity market proxy are used to calibrate a nominal Gaussian model, while true returns are generated from Student-t distributions with varying degrees of freedom to represent increasingly heavy tails. Importance sampling is implemented via exponential tilting of the Gaussian model, and VaR is estimated through likelihood-weighted root-finding. Discrete moment matching constructs deterministic lower and upper VaR bounds by enforcing a finite number of moment constraints on a discretized loss distribution. The results demonstrate a clear trade-off between efficiency and robustness. Importance sampling produces low-variance VaR estimates under the nominal model but systematically underestimates the true VaR under heavy-tailed returns, with bias increasing at higher confidence levels and for thicker tails. In contrast, discrete moment matching yields conservative VaR bracketing that remains robust under tail misspecification. These findings highlight that variance reduction alone is insufficient for reliable tail risk estimation when model uncertainty is significant. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.09927 |
| By: | Luttini, Emiliano Evaristo; Mekonnen, Dawit Kelemework; Mercer-Blackman, Valerie; Sorensen, Bent |
| Abstract: | Commodity-exporting countries face important challenges in shielding their economies from commodity price volatility. In an ideal world, a country would buy and sell foreign assets to insure itself against volatility caused by the destabilizing economic impact of gross domestic product fluctuations over time. The literature on the topic, which has mainly focused on risk sharing across advanced economies, has found a puzzlingly low amount of risk sharing. Using a sample of 110 countries between 1995 and 2019, this paper finds that commodity exporters share 46 percent of their risk as a group internationally, significantly more so than non-commodity exporters, which share about 33 percent of their risk. The greater the volatility of commodity terms of trade, the more a country shares risk internationally. Consequently, energy and metals exporters share risk more than agricultural exporters. Government saving is the main risk-sharing mechanism in commodity-exporting and non-exporting countries, although it is more important for commodity exporters. Commodity-exporting countries are also more likely to smooth gross domestic product fluctuations through net purchases of assets abroad, while non-commodity exporters tend to self-insure through procyclical domestic investment. |
| Date: | 2026–01–14 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11296 |
| By: | Nuno Silva |
| Abstract: | This paper asks how best to estimate and forecast firms’ residualized sales growth volatility, a standard measure of idiosyncratic uncertainty. Using a comprehensive dataset of Portuguese firms from 2006 to 2022, I compare the most common approaches used in the literature with a novel quantile-based method that exploits past cross-sectional information and contemporaneous macroeconomic variables and adjusts for the predictability in sales growth rates. I then estimate forecasting models and conduct a simulation exercise to assess the in-sample and out-of-sample performance of all approaches. The paper contributes to the literature by showing that quantile-based estimates and forecasts outperform traditional methods and that sales growth volatility can be measured with reasonable precision, making it suitable for wider application in empirical work. These findings support the application of quantile-based volatility measures to other low-frequency economic variables, especially those characterized by fat-tailed distributions. |
| JEL: | C53 D22 G30 L25 G32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202525 |
| By: | Valerii Kremnev |
| Abstract: | Random walk models with log-normal outcomes fit local market observations remarkably well. Yet interconnected or recursive structures - layered derivatives, leveraged positions, iterative funding rounds - periodically produce power-law distributed events. We show that the transition from log-normal to power-law dynamics requires only three conditions: randomness in the underlying process, rectification of payouts, and iterative feed-forward of expected values. Using an infinite option-on-option chain as an illustrative model, we derive a critical volatility threshold at $\sigma^* = \sqrt{2\pi} \approx 250.66\%$ for the unconditional case. With selective survival - where participants require minimum returns to continue - the critical threshold drops discontinuously to $\sigma_{\text{th}}^{*} = \sqrt{\pi/2} \approx 125.3\%$, and can decrease further with higher survival thresholds. The resulting outcomes follow what we term the Critical Volatility ($V^*$) Distribution - a power-law whose exponent admits closed-form expression in terms of survival pressure and conditional expected growth. The result suggests that fat tails may be an emergent property of iterative log-normal processes with selection rather than an exogenous feature. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01269 |
| By: | Othmane Zarhali; Emmanuel Bacry; Jean-Fran\c{c}ois Muzy |
| Abstract: | We introduce the multivariate Log S-fBM model (mLog S-fBM), extending the univariate framework proposed by Wu \textit{et al.} to the multidimensional setting. We define the multidimensional Stationary fractional Brownian motion (mS-fBM), characterized by marginals following S-fBM dynamics and a specific cross-covariance structure. It is parametrized by a correlation scale $T$, marginal-specific intermittency parameters and Hurst exponents, as well as their multidimensional counterparts: the co-intermittency matrix and the co-Hurst matrix. The mLog S-fBM is constructed by modeling volatility components as exponentials of the mS-fBM, preserving the dependence structure of the Gaussian core. We demonstrate that the model is well-defined for any co-Hurst matrix with entries in $[0, \frac{1}{2}[$, supporting vanishing co-Hurst parameters to bridge rough volatility and multifractal regimes. We generalize the small intermittency approximation technique to the multivariate setting to develop an efficient Generalized Method of Moments calibration procedure, estimating cross-covariance parameters for pairs of marginals. We validate it on synthetic data and apply it to S\&P 500 market data, modeling stock return fluctuations. Diagonal estimates of the stock Hurst matrix, corresponding to single-stock log-volatility Hurst exponents, are close to 0, indicating multifractal behavior, while co-Hurst off-diagonal entries are close to the Hurst exponent of the S\&P 500 index ($H \approx 0.12$), and co-intermittency off-diagonal entries align with univariate intermittency estimates. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.10517 |
| By: | Haibo Wang; Jun Huang; Lutfu S Sua; Jaime Ortiz; Jinshyang Roan; Bahram Alidaee |
| Abstract: | The 2023 U.S. banking crisis propagated not through direct financial linkages but through a high-frequency, information-based contagion channel. This paper moves beyond exploration analysis to test the "too-similar-to-fail" hypothesis, arguing that risk spillovers were driven by perceived similarities in bank business models under acute interest rate pressure. Employing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with 30-day rolling windows, a method uniquely suited for capturing the rapid network shifts inherent in a panic, we analyze daily stock returns for the four failed institutions and a systematically selected peer group of surviving banks vulnerable to the same risks from March 18, 2022, to March 15, 2023. Our results provide strong evidence for this contagion channel: total system connectedness surged dramatically during the crisis peak, and we identify SIVB, FRC, and WAL as primary net transmitters of risk while their perceived peers became significant net receivers, a key dynamic indicator of systemic vulnerability that cannot be captured by asset-by-asset analysis. We further demonstrate that these spillovers were significantly amplified by market sentiment (as measured by the VIX) and economic policy uncertainty (EPU). By providing a clear conceptual framework and robust empirical validation, our findings confirm the persistence of systemic risks within the banking network and highlight the importance of real-time monitoring in strengthening financial stability. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01783 |