This week the pipeline scored 57 new papers. Here are the 10 strongest by our rigor-weighted score (how scoring works).
1. Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams
Holy Grail · Math 9.0/10 · Rigor 8.0/10 · Market Microstructure
Temporal point processes are used both to measure market mechanisms, for example how fast prices absorb news and how much of the reaction is the market responding to itself, and to predict the next event. We show that one algebraic object, the phase-type (Erlang-chain) state space, serves both purpo…
2. GROMM: Generative Risk-neutral Option Market Models
Holy Grail · Math 9.0/10 · Rigor 8.0/10 · Options & Derivatives, Volatility
We present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that its dynamics admit a unique strong solution. In spirit, it is a stochastic model for local volatility when the latter is u…
3. Data-Driven Dynamic Portfolio Selection with Vine Copula Simulation and Reinforcement Learning
Holy Grail · Math 8.5/10 · Rigor 8.0/10 · Risk Management, Portfolio Optimization
We develop a data-driven approach to dynamic portfolio selection that combines vine-copula simulation with reinforcement learning. The investor has constant relative risk aversion and maximises expected terminal utility with a running penalty on the conditional value-at-risk (CVaR) of one-period los…
4. Designing Independent Variation: A Guided Iteration Methodology for Price Experiments in Short, Correlated Panels
Holy Grail · Math 8.5/10 · Rigor 8.0/10
Estimation error in short, correlated pricing panels is dominated by trajectory dispersion rather than sampling noise, creating an identification shortfall that only independently assigned variation can resolve. We propose a guided iteration methodology to decide how much variation to generate, wher…
5. Structured SDEs for Realized GARCH: Continuous-Time Volatility Modelling and Component-Level Analysis
Holy Grail · Math 8.0/10 · Rigor 8.0/10 · Volatility, Machine Learning
We propose a structured continuous-time stochastic volatility model for one-day-ahead integrated-variance forecasting. The model introduces a jointly evolving realized-measure state that feeds directly into latent variance dynamics, while correlated Brownian innovations capture return-linked depende…
6. CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope
Holy Grail · Math 8.0/10 · Rigor 8.0/10
Correlation matrices combine nonlinear positive-definiteness constraints with a simultaneous row-column permutation symmetry. Their generation therefore involves two distinct problems. First, every output must remain on the correlation elliptope, irrespective of how its variables are ordered. Second…
7. StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction
Holy Grail · Math 8.0/10 · Rigor 8.0/10 · Market Microstructure
Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equation (SDE). For market time series this SDE can be a poor abstraction: limit order book (LOB) order flow is heavy-tailed, bursty, and occasionally jump…
8. Pricing American Commodity Options Under the Schwartz Two-Factor Model Using the Alternating Direction Explicit (ADE) Method
Holy Grail · Math 8.5/10 · Rigor 7.5/10 · Factor Investing, Commodities & Energy
This project investigates the valuation of American commodity options using the Schwartz two-factor model. We employ the double-sweep Alternating Direction Explicit (ADE) method, an unconditionally stable (for diffusion) finite difference scheme that provides the efficiency of explicit marching with…
9. E-Backtesting Expected Shortfall without Value-at-Risk Forecasts under Time-series Models
Holy Grail · Math 8.5/10 · Rigor 7.5/10 · Risk Management, Volatility
Expected Shortfall (ES) measures the average size of tail losses. Its sequential backtests typically also require a Value-at-Risk (VaR) forecast. We develop two e-backtesting methods using only observed losses and ES forecasts: one for iid losses only and the other for time series under an AR-GARCH…
10. AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery
Holy Grail · Math 7.5/10 · Rigor 8.0/10 · NLP & LLMs, Reinforcement Learning
Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space. Recent LLM-based approaches use prompting, iterative search, or seed-factor evolution, leaving direct factor construction through policy learning less explored. To ad…
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