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Quantitative Markets & Institutions Lab

Research on financial models, market regimes, institutions, and investor decisions.

Research by · Quantitative Markets & Institutions Lab

Examine the researchExplore the models
Executed investigations
06
Empirical studies
04
Numerical validations
02
Interactive models
05
Out-of-sample equity curves, frozen 2021 weightsgrowth of $1 · 2022-01 = 1
MVO max Sharpe
1.39
Robust MVO
1.74
Risk parity
1.25
HRP
1.12
Drawdown from the running peak% · daily · one mark per day beyond forecast VaR
−0.4%
SPY realised volatility% annualised · rolling 21-day
18.0%
SPY–TLT correlationrolling 126-day correlation of daily returns · full daily series
+0.31
Rolling 252-day QQQ betasβ · trailing 252 observations, month-end
Mkt-RF
1.16
HML
−0.05
Mom
0.03

: Out-of-sample equity curves, frozen 2021 weights · 6 Jul 2026 · MVO max Sharpe 1.39, Robust MVO 1.74, Risk parity 1.25, HRP 1.12; Drawdown from the running peak · 6 Jul 2026 · −0.4%; SPY realised volatility · 6 Jul 2026 · 18.0%; SPY–TLT correlation · 6 Jul 2026 · +0.31; Rolling 252-day QQQ betas · 29 May 2026 · Mkt-RF 1.16, HML −0.05, Mom 0.03

Commit 4806df9Data – Inspect the research record

01 Investigations

Published research

  • Portfolio construction · Estimation risk

    Portfolio optimization under estimation error

    The fitted max-Sharpe portfolio weakened materially on later data.

    1.12MVO Sharpe · fitSharpe 1.12 → 0.44

  • Market risk · Forecast calibration

    Historical VaR under coverage and independence tests

    Breaches arrived too often and in clusters; the forecast failed its calibration tests.

    135Observed breaches135 breaches · 107 expected

  • Derivatives · Stochastic assumptions

    Constant-volatility dynamics under empirical tests

    Observed volatility and option prices contradicted the tested constant-volatility dynamics.

    ≈3%–93%Realized vol range≈3% → 93% realized volatility

  • Empirical dossier · Measured relationship

    Stock–bond correlation regimes and their portfolio consequences

    One sign change, three portfolio consequences.

    −0.35SPY–TLT · 2015–19−0.35 → +0.37 correlation

  • Factor models · Product inference

    ETF factor exposure and alpha after multiple testing

    Advertised exposures appeared; no tested alpha survived multiple-testing correction.

    0.34MTUM momentum β0 alphas at 5% FDR

  • Numerical methods · Simulation error

    Monte Carlo convergence, bias, and variance reduction

    Sampling error followed the N⁻¹ᐟ² law; enough paths is defined by the decision's tolerance.

    N⁻¹ᐟ²Error rateError ∝ N⁻¹ᐟ²

02 Instruments

Working instruments

  • Quantitative model · Derivatives

    Option Valuation Models

    The Black-Scholes-Merton closed form and the Cox-Ross-Rubinstein lattice priced side by side, with every Greek computed live in the browser.

  • Quantitative model · Stochastic processes

    Stochastic-Process Simulation

    Five stochastic processes — GBM, jumps, Heston, mean reversion, regime switching — simulated live as 50 seeded one-year paths.

03 Methods and record

Every result ships with the evidence needed to challenge it.

  • Research design and validation

    Methods & Evidence Standards

    Model validation, evidence classification, uncertainty, falsification, and reproducibility standards for quantitative financial research.

  • Versioned research ledger

    Research Record & Ledger

    A public ledger of six quantitative investigations — questions, claims, evidence, failures, and commit-pinned reproduction steps.

Interpretation standard

A quantitative conclusion is inseparable from its model, data vintage, evaluation design, and error estimate. Confidence is warranted only within those boundaries.