Quantitative Finance

Zovko, Ilija I

doi: 10.48550/arxiv.2606.13419pmid: N/A

Abstract:An important question for an algo trader working an order is to understand if their actions are moving the market against them -- i.e., causing market impact. The conventional answer usually is one of two: (i) monitor price slippage in real-time, potentially reducing adverse activity with increased slippage, or (ii) do away with dynamic trading adjustments and rely on semi-static rules based on ex-post estimates of slippage over a large sample of events. Realtime monitoring fails because reliably estimating slippage is statistically expensive -- it requires hundreds of fills before it can be told apart from background volatility. More fundamentally however, it does not establish causality. Observed adverse price moves may be caused by the trader's own actions, or by an unrelated participant competing for the same liquidity and capturing the same alpha. The optimal response (say, slow down vs.\ speed up) is opposite in the two cases. We propose a method that detects price impact, on a per-action basis, by measuring the timing synchronicity between a trader's actions and subsequent adverse market events. The method at heart is a test for statistical \emph{surprise} in the timing of adverse events post trader action. We must be clear in that we do make a leap of faith here and assume that surprisingly fast adverse market events are evidence of causation and that the action triggered them -- a direct signature of impact and information leakage. Validating it requires real execution data; we set out the empirical tests that would do so.

Jin, Tao; Florescu, Stuart

doi: 10.48550/arxiv.2606.04235pmid: N/A

Abstract:Collateral allocation for uncleared derivatives is a legally constrained and operationally discrete optimization problem. Institutions must satisfy margin requirements while respecting CSA eligibility rules, valuation percentages, rounding, transfer thresholds, concentration limits, custody conditions, inventory, and VM, IM, or IA side constraints. This manuscript develops CR-HO-QAOA, a certified higher-order quantum candidate-generation framework for margin- and CSA-aware collateral allocation. The framework is adapter-first: official SIMM, proxy SIMM, legacy IA, VM-only, RQV, or hybrid margin sources are normalized into a common MarginRequirement, so the optimizer does not calculate or replace official SIMM. Given the requirement, CSA terms, and inventory, the optimizer builds a bounded active neighborhood of pledge, recall, substitution, batch, and slack actions. These actions define a higher-order binary model whose hyperedges capture concentration pressure, custody batches, substitution tickets, chunky lots, liquidity effects, overshoot, and side-specific requirements. The quantum layer maps hyperedges into a Pauli-Z cost Hamiltonian and uses collateral-specific feasible-subspace mixers to preserve one-hot choices, movement budgets, side assignments, and substitution structure. Candidates are decoded, repaired if needed, evaluated under an eight-term production objective, and certified by a deterministic CP-SAT master solver before any recommendation is reported. Synthetic benchmarks show that higher-order, constraint-preserving candidate generation can improve certified sample quality relative to QUBO-style and generic-mixer baselines, while CP-SAT remains the feasibility and governance arbiter. These results are synthetic workflow-validation evidence only, not evidence of hardware quantum advantage or production bank savings.

Radke, Silvian M.; Verpoort, Philipp C.; Ueckerdt, Falko; Müsgens, Felix

doi: 10.48550/arxiv.2606.09190pmid: N/A

Abstract:Despite growing concerns over energy security, infrastructure planning and modelling for emerging green fuel supply chains often neglect risks from supply disruptions. Using a stochastic optimisation model of EU hydrogen imports, we show that 'naive' infrastructure planning results in welfare losses of 12 % (24 billion EUR) compared to risk-aware planning that anticipates supply disruptions. Despite requiring higher upfront investments, anticipatory planning achieves welfare levels close to those of an idealised system without disruptions, but entails a markedly different infrastructure configuration. Two complementary resilience strategies emerge: diversification across import corridors and strategic over-investment. This leads to increased intra-European transport capacity, a broader set of import pipelines, and investments in costly shipping terminals for hydrogen carriers. Our results show that incorporating supply risk considerations into infrastructure planning helps prevent the structural vulnerabilities seen in fossil fuel systems when designing future hydrogen supply chains.

Campbell, Steven; Capponi, Agostino; Parashar, Ananya

doi: 10.48550/arxiv.2606.03158pmid: N/A

Abstract:We study optimal portfolio choice for a household simultaneously managing a random-deadline goal, such as a medical emergency or job loss, and a fixed-deadline goal such as retirement or college tuition. Under a forced funding rule, in which each goal is paid in full whenever affordable, the household maximizes a weighted sum of the probabilities of fully funding both goals in a Black--Scholes market. We identify two novel effects absent from single-goal models: a growth crowding-out effect, in which precautionary saving for the random goal distorts investment toward the fixed goal, and a deadline pressure effect, in which a compressed saving horizon forces excess risk-taking. A striking implication is that the value function need not be monotone in wealth: a household just above the random-goal threshold is forced to pay it when the shock arrives, depleting its wealth for the fixed goal, and ends up worse off than a slightly poorer household that missed the random goal but kept its wealth intact. This non-monotonicity is absent from all single-goal benchmarks and arises purely from the interaction between the two goal types under forced funding. We further study an optional funding variant in which the household may decline the fixed-deadline goal at time $T$ rather than being required to fund it. We characterize the ex ante option value, i.e., the full time-$0$ value of this flexibility and the terminal option value, i.e., its value at the funding decision node. We find that both options are most valuable at intermediate wealth levels where paying the fixed-deadline goal would substantially reduce the continuation value of the random-deadline problem.

Zhong, Tenghan

doi: 10.48550/arxiv.2606.12872pmid: N/A

Abstract:Short-dated index options make scheduled macro-announcement risk visible in market prices, but identification is nontrivial: a flexible no-event surface fitted to event-spanning quotes can absorb event premia, while a jump calibrated without event-spanning quotes is unidentified. We therefore model Federal Open Market Committee (FOMC) decisions, Consumer Price Index (CPI) releases, and nonfarm payroll (NFP) reports as deterministic-time jumps in risk-neutral option pricing and propose a non-spanning identification protocol. Non-spanning expiries identify the no-event volatility surface, event-spanning training quotes calibrate the scheduled jump, and held-out event-spanning quotes are used only for pricing evaluation. On PM-settled S\&P 500 index (SPX) options from May 2022 to August 2025, Gaussian and two-component mixture jumps improve held-out event-spanning pricing, with the clearest gains in robust median pricing errors and in event-volatility option combinations (straddles and strangles) rather than directional risk reversals. A contaminated-surface stress test confirms the identification concern: allowing event-spanning training quotes into the no-event surface fit produces strong held-out performance by absorbing event premia rather than identifying scheduled jump risk. An amortized mixture density network (MDN) benchmark shows limited cross-event transfer: pure leave-one-event-out amortization reduces implied-volatility errors but not mean dollar or mean spread-normalized pricing errors, while the scale-calibrated variant restores Gaussian-level performance yet remains below event-specific mixture calibration. Scheduled-jump identification is strongest for CPI and FOMC and weaker for NFP.

Press, William H.; Dannenberg, Alex

doi: 10.48550/arxiv.2606.00800pmid: N/A

Abstract:Q-variance (so-called) posits a statistical relationship $\mathbf{E}(\sigma^2 | z) = \sigma_0^2 + \tfrac{1}{2}z^2$ between an asset's volatility $\sigma^2$, as observed in a time interval $T$, and its (suitably scaled) return $z$ in the same interval. We here show that this relationship is {\em exactly equivalent} to to positing an Inverse Gamma probability distribution for $\sigma^2$ itself. We then show that such a distribution is exactly generated by a multiplicative Langevin process with an arbitrary, settable coherence time $\tau_c$, so that very nearly the same Q-variance relationship will hold for all $T \ll \tau_c$.

Atchadé, Mintodê Nicodème; Christiansen, Marcus C.; Hubalek, Friedrich; Junike, Gero

doi: 10.48550/arxiv.2606.04715pmid: N/A

Abstract:The surplus of a life insurance policy depends on both systematic changes in mortality risk and financial changes. We propose to decompose the surplus by the axiomatically justified IASU decomposition, which is a continuous time version of the Shapley value. However, life tables are not updated continuously, but rather, only once per year. In this yearly update cycle of the life tables, we apply different interpolation methods to perform the IASU decomposition and analyze the effects of these methods on the surplus decomposition. Our results show that Lee-Carter and linear interpolation yield almost identical decompositions, whereas constant approximations results in substantially different decompositions. As a consequence, reporting standards and regulators should clarify how to interpolate mortality risks.

Ikeda, Kiyohiro; Kogure, Yosuke; Aizawa, Hiroki; Takayama, Yuki

doi: 10.48550/arxiv.2606.09472pmid: N/A

Abstract:This paper presents a systematic framework for analyzing the economic parameter sensitivity of a hierarchical spatial economy within economic geography models. Through the hierarchical reduction approach proposed in this study, the original region-level governing equation is condensed into country-level and alliance-level equations. Based on the reduced governing equation, we formulate the sensitivity of economic variables on each country's population. This approach is applied to the analysis of international trade competition -- covering both trade liberalization and protectionism around Brexit -- among the UK, France, and Germany. We find that both the UK and the EU should focus on reducing domestic transportation costs, whereas tariffs and retaliatory tariffs act as a double-edged sword that can either strengthen or weaken their trade positions.

Brou, Arsène; Luger, Richard

doi: 10.1016/j.jbankfin.2026.107716pmid: N/A

Abstract:Changes in volatility contain valuable information about the likelihood of positive versus negative returns. We propose a new approach to modeling financial returns that exploits this insight by decomposing returns into sign and magnitude (absolute value) components, with magnitude closely related to volatility. The joint distribution used to compute expected returns combines a model for the marginal distribution of magnitude with a model for the distribution of the sign, conditional on the contemporaneous magnitude. Unlike traditional linear predictive regressions, this decomposition framework captures nonlinear predictability in return dynamics. An out-of-sample forecasting evaluation using monthly U.S. stock market excess returns demonstrates substantial statistical and economic gains relative to linear regression and complete subset regression, while delivering performance that is competitive with copula-based return-decomposition methods and other nonlinear benchmarks.

Yan, Dong; Ye, Wenrui; Zong, Zhiyue; Chen, Wenting

doi: 10.48550/arxiv.2606.10805pmid: N/A

Abstract:We extend the return extrapolation framework of Atmaz (2022) to incorporate two behaviorally realistic features absent from the linear benchmark: saturation in belief updating and asymmetry between gains and losses. We introduce a smooth, nonlinear, asymmetric extrapolation function and characterize the optimal portfolio of a CRRA investor under Heston (1993) stochastic volatility as the sum of a sentiment-distorted myopic demand, a variance hedging demand, and a sentiment hedging demand. The resulting semilinear Hamilton-Jacobi-Bellman equation is solved by two independent numerical methods, a finite-difference ADI scheme with time-step policy iteration and a deep learning-driven iterative scheme. The model generates four investor-level behavioral anomalies: asymmetric responses to gains and losses, attenuated reactions at extremes, excess trading volume, and welfare loss rising with the strength of extrapolation, each of which maps onto documented empirical patterns. Its central finding is that saturation acts as an endogenous correction mechanism: at the same local slope at the origin, the asymmetric nonlinear extrapolator carries a smaller welfare loss than a linear one.