arXiv subjects

Tenghan Zhong

Publications and source records attributed to Tenghan Zhong.

7 recordsLinked to original sources

arXiv2026

Marking-Aware Sequential VaR Recalibration for Standardized Option Books

Daily Value-at-Risk (VaR) for option books requires more than an accurate quantile forecast. It first requires a precise definition of the loss target. Before any model is evaluated, the protocol must fix the book construction rule, the marking rule for the next day, the loss scale, and the information set available at forecast time. Common pipelines instead apply VaR methods to underlying returns or preconstructed book loss series, leaving these operational choices outside the statistical target. We propose a marking-aware sequential VaR recalibration framework that targets normalized book-level loss directly, restricts the forecast state to information available at forecast time, and recalibrates an upper tail VaR using only past forecast residuals. In out-of-sample evaluation on S\&P 500 index (SPX) and QQQ exchange-traded fund (ETF) options, the reference VaR undercovers all three books in both markets. Sequential VaR recalibration moves exceedance rates close to the target and delivers the best aggregate performance across books, with the lowest average violation, lowest pinball loss, and smallest maximum exceedance over rolling 50 trading day windows among the evaluated methods. Robustness checks preserve the same conclusion under strict direct marking, stricter book selection screens, and removal of the VaR floor. The result is also stable across alternative quantile learners, residual recalibration windows, and decay rates. These findings support marking-aware sequential VaR recalibration as a leakage-safe risk control layer for option-book VaR under realistic quote and marking frictions.

q-fin.RM↗

arXiv2026

Reliability-Aware ETF Tail-Risk Monitoring

Daily ETF risk monitoring can become unreliable when market data quality degrades, market conditions shift, or predictive performance becomes unstable. This paper develops a reliability-aware risk monitoring service for next-day tail-risk surveillance. The proposed framework combines service-time quality checks, lower-tail prediction, uncertainty scoring, and risk-aware adjustment of the tail-risk estimate. We evaluate the system on a daily panel of multiple ETFs augmented with VIX and yield-curve information under a rolling walk-forward design. Empirically, the framework improves tail-risk monitoring, especially during stressed periods, while remaining reliable under simulated input degradation.

q-fin.RM↗

arXiv2026

Risk-Sensitive Specialist Routing for Volatility Forecasting

Volatility forecasting becomes challenging when market conditions shift and model performance varies across market states. Motivated by this instability, we develop a risk-sensitive specialist routing framework for ETF volatility forecasting. The framework uses online risk-sensitive evaluation and state-dependent gating to combine different forecasting specialists across calm and stressed market states. Using a daily panel of six ETFs under a rolling walk-forward design, we find that the strongest forecaster is regime-dependent rather than stable across all states. Relative to the rolling-best baseline, the proposed routing framework reduces high-volatility forecast loss by about 24% and underprediction loss by about 22%. These results suggest that specialist routing provides a practical forecasting architecture that adapts to changing market conditions.

q-fin.ST↗

arXiv2026

Non-Spanning Identification of Scheduled Event Risk in Option Pricing

Short-dated index options make scheduled macro-announcement risk visible in market prices, but visibility does not imply identification: a flexible no-event surface fitted to event-spanning quotes can absorb event premia, while a jump calibrated without event-spanning quotes is unidentified. To separate the continuous surface from the scheduled jump, we 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.

q-fin.PR↗

arXiv2026

Entropy-Regularized Certainty-Equivalent Bellman Policies for Risk-Sensitive Market Making

We study a finite-inventory risk-sensitive market making problem in which a dealer controls bid and ask quotes, faces Brownian midprice risk, and receives liquidity-taking orders through point processes with quote-dependent intensities. The objective is the certainty equivalent induced by exponential utility with terminal and running inventory penalties. We introduce an exact discrete entropy-regularized Bellman operator that applies log-sum-exp regularization to deterministic-action certainty-equivalent scores, rather than to a risk-neutral one-step reward. This distinction is essential because the exponential certainty equivalent does not commute with quote randomization. For time step \(h\) and entropy parameter \(λ\), we prove uniform convergence to the unregularized continuous-time risk-sensitive value at rate \[ O\bigl(h+λ(1+|\logλ|)\bigr). \] We also prove certainty-equivalent performance bounds for the induced Gibbs policies under a fresh-sampling relaxed implementation, in which quote marks are sampled at potential fill events rather than frozen over a time step. Under a quadratic growth condition on the Hamiltonian in the relevant quote coordinates, these policies concentrate around the unregularized optimal quote set. Finally, we show that a lower-cost Hamiltonian-Gibbs proxy satisfies a certainty-equivalent performance bound of the same order as the exact Bellman Gibbs policy. Numerical experiments in an Avellaneda--Stoikov specification support the predicted scaling for discretization error, entropy bias, policy gap, quote concentration, and exact-versus-proxy consistency.

q-fin.TR↗

arXiv2026

Proxy-Reliance Control in Conformal Recalibration of One-Sided Value-at-Risk

We introduce a proxy-reliance-controlled conformal recalibration framework for one-sided Value-at-Risk (VaR), and study a question that existing state-aware methods do not usually isolate: how strongly should the recalibration adjustment depend on an imperfect volatility proxy? We formalize this through a proxy-reliance parameter that continuously interpolates between an approximately constant-shift correction and a fully proxy-scaled correction. This makes proxy reliance a distinct and practically interpretable design choice in one-sided VaR recalibration. We show theoretically that larger proxy reliance increases the responsiveness of the tail adjustment to proxy scale, but also increases stressed-state fragility when the proxy underreacts. Empirically, in rolling out-of-sample tests on a six-ETF panel with VIX-linked state variables, and with supporting evidence from SPY, we find that the empirical value of proxy-reliance control lies in improved stressed-state robustness rather than uniform overall dominance. In particular, when the baseline forecast remains exposed to proxy imperfection in stressed states, lower or intermediate proxy reliance can outperform fully proxy-scaled recalibration in stressed left-tail VaR control.

q-fin.RM↗

arXiv2025

An Efficient Calibration Framework for Volatility Derivatives under Rough Volatility with Jumps

We present a fast and robust calibration method for stochastic volatility models that admit Fourier-analytic transform-based pricing via characteristic functions. The design is structure-preserving: we keep the original pricing transform and (i) split the pricing formula into data-independent inte- grals and a market-dependent remainder; (ii) precompute those data-independent integrals with GPU acceleration; and (iii) approximate only the remaining, market-dependent pricing map with a small neural network. We instantiate the workflow on a rough volatility model with tempered-stable jumps tailored to power-type volatility derivatives and calibrate it to VIX options with a global-to-local search. We verify that a pure-jump rough volatility model adequately captures the VIX dynamics, consistent with prior empirical findings, and demonstrate that our calibration method achieves high accuracy and speed.

q-fin.CP↗