This week the pipeline scored 101 new papers. Here are the 10 strongest by our rigor-weighted score (how scoring works).
1. Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks
Holy Grail · Math 8.5/10 · Rigor 9.0/10 · Machine Learning, Volatility
Compiling implied volatility (IV) surfaces and forecasting derivative risk from high-frequency equity and options feeds requires addressing contract heterogeneity, schema shifts, volatility clustering, and non-stationary trading activity. Using a split-adjusted NVIDIA (NVDA) equity and options panel…
2. Long-term yields of short-rate models as Feynman-Kac ground states: a variational quantum eigensolver with single-setting energy estimation on two NISQ platforms
Holy Grail · Math 9.0/10 · Rigor 8.0/10 · Fixed Income
We show that the long-term yield of a short-rate model is the ground-state energy of a Schrodinger operator obtained from the Feynman-Kac bond-pricing equation, so that it can be computed by a variational quantum eigensolver (VQE). We treat the Vasicek, Cox-Ingersoll-Ross and Black-Karasinski models…
3. Are LLM Research Agents Reliable Judges of Predictability? Evidence from Planted-Signal Worlds
Holy Grail · Math 7.5/10 · Rigor 9.0/10 · NLP & LLMs, Machine Learning
Researchers ask large language model (LLM) research agents whether a forecasting model beats a static benchmark, a verdict that real data cannot check. We treat the agent as a statistical test and score pre-specified runs of three frontier models and one open-weight model in simulated worlds with a…
4. Decomposing Rank Reversals under Temporal Nonstationarity: Alignment, Aggressiveness, and Instability
Holy Grail · Math 9.0/10 · Rigor 8.0/10
Under temporal nonstationarity, model performance rankings reverse across training horizons due to heterogeneous sensitivities to memory. While existing literature addresses adaptive lookbacks, effective model complexity, and empirical forecast evaluation in isolation, no unified framework explains…
5. A Rough Instantaneous Squared VIX Model for Term Structure and Option Pricing
Holy Grail · Math 9.0/10 · Rigor 8.0/10 · Volatility, Options & Derivatives
The VIX term structure provides forward-looking measures of market uncertainty across horizons, while VIX options allow investors to hedge volatility risk. This paper proposes a rough instantaneous squared VIX (RISVIX) model by introducing fractional memory into the conventional Markovian framework.…
6. Security and Derivative Pricing with Fast Regime Switches
Holy Grail · Math 9.0/10 · Rigor 8.0/10 · Options & Derivatives
A regime-switching model lets any parameter of a standard model take a different value in each state of a hidden Markov chain. For the affine and exponential-Lévy models in common use the pricing problem reduces to one linear system, a’ = (Q + diag g(t)) a, with the chain’s generator Q and a model-s…
7. Symbol-based residual certification of reduced and neural surrogates for tempered-fractional CGMY pricing
Holy Grail · Math 9.0/10 · Rigor 8.0/10
We certify reduced and neural approximations of European-put price trajectories under the Carr–Geman–Madan–Yor model by residual bounds for the spline Galerkin discretisation of the tempered-fractional pricing equation. The Galerkin matrices are Toeplitz sections of an explicit periodised Fourier…
8. Functional Tensor Trains for High-Order Greeks and Exposure Tails of Multi-Asset Structured Products
Holy Grail · Math 9.0/10 · Rigor 8.0/10 · Risk Management, Options & Derivatives
Risk management of worst-of autocallables needs price sensitivities (Greeks) up to third order and the potential future exposure, a tail quantile of the note’s future value. In Monte Carlo, each order of differentiation multiplies the sampling noise by a factor that grows with the observation freque…
9. Calibration of Passive Market Impact with a Queue-Sensitive Propagator Model
Holy Grail · Math 8.5/10 · Rigor 8.0/10 · Market Microstructure
We develop a framework for calibrating passive market impact, defined as the price response to liquidity-providing limit orders, in contrast to the impact of liquidity-consuming market orders. We build on the model of Ouazzani Chahdi, Rosenbaum, and Szymanski (arxiv:2412.07461), which combines Hawke…
10. PanelGP: A Probabilistic Factor Model for Asset-Return Panels
Holy Grail · Math 8.5/10 · Rigor 8.0/10 · Factor Investing
We develop PanelGP, a conditional factor model for large panels of stock returns in which firm characteristics determine the factor loadings and economic conditions determine the expected factors. Both relations are estimated nonparametrically with Gaussian-process priors. The kernel choice nests in…
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