Things worth keeping.
A running collection in three shelves: the research papers I keep returning to (many from my master's thesis on sparse regression at BNP Paribas AM), general resources, and the interview-prep material that actually helped. Updated whenever something deserves a place.
Volatility is rough
Volatility is rough: the H=0.1 finding behind the rough-volatility deep dive on this site.
Deep Hedging
Reframes hedging as an optimisation problem solvable with deep learning. Opens up an entire research direction.
Transfer Learning via L1 Regularization
The transfer-Lasso idea behind the fund risk-exposure pipeline I built at BNP Paribas AM.
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
Asymptotics unifying the adaptive and transfer Lasso estimators.
The Adaptive Lasso and Its Oracle Properties
Weighted L1 penalties recover the oracle: the reference point for every Lasso variant that followed.
Least Angle Regression
On Model Selection Consistency of Lasso
The irrepresentable condition: when the Lasso can and cannot find the true support.
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
SCAD: the nonconvex penalty that fixes the Lasso's bias on large coefficients.
Near-ideal Model Selection by L1 Minimization
On the Degrees of Freedom of the Lasso
Relaxed Lasso
The Sparsity and Bias of the Lasso Selection in High-Dimensional Linear Regression
Thresholding-based Iterative Selection Procedures for Model Selection and Shrinkage
Gene Selection for Cancer Classification using Support Vector Machines
SVM-RFE: recursive feature elimination, still a workhorse for feature selection.
Wavelet Methods in Statistics: Some Recent Developments and Their Applications
Comparison of Model Selection for Regression
Model Selection: Two Fundamental Measures of Coherence and Their Algorithmic Significance
A Feature Selection Method for Multi-Dimension Time-Series Data
Dynamic Filtering of Time-Varying Sparse Signals via L1 Minimization
Where sparse recovery meets Kalman filtering: tracking a moving support.
A Step by Step Mathematical Derivation and Tutorial on Kalman Filters
The cleanest self-contained derivation of the Kalman filter I know.
Return-Based Style Analysis with Time-Varying Exposures
Kalman-filtered style exposures: the dynamic version of Sharpe's style analysis.
Extreme Risk Analysis
Advances in Financial Machine Learning
A foundational reference for ML in finance. The chapters on cross-validation pitfalls and meta-labelling are particularly worth re-reading.
The Elements of Statistical Learning
The statistical learning bible. Free from the authors; the chapters on shrinkage and model selection underpin half the papers on this shelf.
vectorbt
Vectorised backtesting in Python, fast enough to scan thousands of parameter combinations in seconds.
Robert Carver - Systematic Trading
A practitioner's blog on portfolio construction, position sizing, and instrument diversification. The pyramid model in his book remains useful.
AQR - Library of research
Free repository of well-written research papers on factor investing, risk parity, and managed futures.
A Practical Guide to Quantitative Finance Interviews
The classic. Brainteasers, probability, stochastic calculus and options questions with worked solutions. Do it cover to cover once.
Heard on the Street
Quantitative questions from real interviews, updated for decades. Good coverage of the finance-intuition questions the green book skips.
QuantGuide
LeetCode-style bank of quant interview problems (probability, brainteasers, stats), with difficulty ratings and firm tags.
150 Most Frequently Asked Questions on Quant Interviews
Compact drill set from the Baruch MFE program. Ideal for a final pass the week before interviews.