Quantitative Researcher

Arghya Ghosh

I develop quantitative frameworks for financial markets, applying stochastic modelling, statistical inference, and computational methods to pricing, valuation, risk measurement, and stress testing of complex financial products.

Mumbai, India

Email Curriculum Vitae (PDF) Research & working papers
Arghya Ghosh at his desk, giving a thumbs up.

About

I'm a quantitative researcher with a strong mathematical foundation and over a decade of experience developing, validating, and stress-testing statistical and stochastic models for complex financial systems, including derivative pricing and securitized product valuation.

My research interests lie at the intersection of stochastic modeling, risk dynamics, macro–financial systems, and climate-linked uncertainty, with an emphasis on interpretable, theoretically grounded models that remain deployable at scale. More recently I've been working at the boundary of quantum computing and quantitative finance — quantum amplitude estimation for portfolio loss computation, and quantum-inspired combinatorial optimization for portfolio construction.

Professional & Research Experience

Vice President, Quantitative Research — JPMorgan Chase & Co.

August 2021 – Present
  • Lead end-to-end development of stochastic pricing and risk models for CRE, CMBS, and structured products used in regulatory stress testing (CCAR, CECL) and internal risk frameworks.
  • Built option-adjusted spread (OAS) and cashflow-waterfall / tranche pricing models for CMBS securitized products, translating collateral-level cashflow and prepayment dynamics into structural tranche valuations.
  • Designed the model-monitoring, back-testing, and obligor / collateral grading frameworks across the CRE, CMBS, and SPG domains, including performance thresholds and escalation triggers.
  • Built climate risk models integrating physical, transition, and market risk channels across 10,000+ collateral records; proposed a novel NOI volatility framework using a two-barrier Merton-type model.
  • Developed a scalable Python computational pipeline for the CRE Loss Engine, cutting runtime by ~35% and enabling cross-portfolio experimentation.
  • Member of the Quantum-Inspired Algorithm (QIA) working group of the JPMorgan Quantitative Research team.

Senior Associate, Model Risk Management — Morgan Stanley

October 2019 – August 2021
  • Validated AIRB / IRB, CCAR, and CECL credit risk models for large-scale portfolios, including residential mortgage, CRE, and structured products.
  • Created a benchmark model using fractional Brownian motion to capture the loss impact where independent-increment assumptions were failing.
  • Led performance monitoring, backtesting, and challenger-model development; automated validation workflows and analytical tooling to improve reproducibility.

Associate, Model Risk Management — Credit Suisse

July 2016 – October 2019
  • Validated stress-testing and scenario-generation models (Brownian Bridge frameworks, PPNR) and market risk models (IRC, VaR, expected shortfall).
  • Independently re-implemented and benchmarked derivative pricing models — credit derivatives (CDS, synthetic CDO / index tranches) and equity derivatives (vanilla and exotic options, structured notes) — against desk valuations.
  • Applied explainable ML techniques to improve transparency in complex forecasting and credit risk models.

Statistical Programmer — inVentiv Health

April 2011 – March 2012
  • Constructed clinical-trial datasets integrating longitudinal patient indicators and treatment histories; performed exploratory and inferential analyses supporting regulatory submissions.

Assistant System Engineer — Tata Consultancy Services

May 2010 – April 2011
  • Developed automation and configuration-management tooling and Unix-based scripting workflows to improve operational reliability of enterprise systems.

Research

My current focus is an independent research project, Kelly-type portfolio optimisation for pension-fund welfare under mean-reversion uncertainty, regime collapse, and downside constraints — a unified robust Kelly framework for long-horizon allocation when a predictive state variable may stop mean-reverting and a hard welfare floor must hold. Work in progress.

Alongside it: a methodology note from my model-risk work — a variance-adjusted monitoring band for LGD model performance — an implementation study adapting deep time-inconsistent portfolio optimisation (D-TIPO) to NSE equities, and a regime-conditioned hybrid allocation framework that switches between QUBO, HRP, and CVaR sleeves on a hidden-Markov market-regime signal.

Read about the research →

Publications

Quantum Amplitude Estimation for Expected Loss Computation under a Discretized Latent-Factor Model

Sole-authored · SSRN, January 2026. Presented as my final project for the Quantum Computing certification at IIT Delhi.

A quantum algorithm for credit-portfolio expected-loss estimation using a discretized latent-factor model. Loss functions are embedded via controlled rotations and evaluated with Grover-based amplitude amplification, giving a quadratic speedup over classical Monte Carlo.

PDF SSRN DOI: 10.2139/ssrn.6334018

A Unified Risk-Neutral Stochastic Simulation Framework for Securities-Backed (Lombard) Lending

With Dr. Ramesh Bhavisetti · International Journal for Multidisciplinary Research, Vol. 8, Issue 1 (Feb 2026).

A risk-neutral stochastic framework for the market–credit interaction in securities-backed lending. Structural wrong-way risk is modeled via an LTV-dependent Cox default intensity, with recovery and LGD endogenized through collateral dynamics and liquidation haircuts.

Structured lending · WWR

Projects

ALGO LAB — Quantitative Portfolio Management Platform · 2025

An institutional-grade quantitative portfolio pipeline for NSE-listed equities and ETFs, built from scratch and deployed as a live web application. A four-step pipeline — Universe Screener → Parameter Tuning → Walk-Forward OOS Backtest → Final Portfolio Weights — runs over ~115 instruments across 11 asset classes.

  • Portfolio construction via QUBO (quantum-inspired combinatorial optimization with simulated annealing + Max-Sharpe MVO), Hierarchical Risk Parity, and Robust CVaR with an ellipsoidal robustness penalty.
  • Ledoit-Wolf covariance shrinkage and James-Stein mean shrinkage to stabilize parameter estimates on limited history.
  • Walk-forward backtesting engine with configurable train / validation / test splits, plus Beta-Neutral, Drawdown-Triggered, and Put-Protection hedge overlays.

Education

Quantum Computing Certification — Indian Institute of Technology (IIT) Delhi

June 2025 – February 2026

Quantum mechanics for computing · qubit systems and quantum gates · quantum algorithms (Grover, phase estimation, amplitude estimation) · quantum machine learning · variational algorithms (VQE, QAOA) · Qiskit.

Final project: Quantum Amplitude Estimation for Expected Loss Computation under a Discretized Latent-Factor Model (SSRN, 2026).

M.Sc. in Applied Mathematics — Chennai Mathematical Institute (CMI)

July 2014 – April 2016

Measure theory · real analysis · topology · probability theory · stochastic processes · stochastic calculus · time series analysis.

Bachelor of Engineering — National Institute of Technology (NIT)

July 2006 – April 2010

Engineering mathematics · numerical methods · control systems · fluid mechanics · thermodynamics · strength of materials.

Research & Technical Skills

Mathematical & statistical foundations Stochastic calculus · probability & measure theory · time series analysis · econometrics · optimization · numerical methods · Monte Carlo simulation
Modeling & machine learning Statistical learning · forecasting models · PD / LGD modeling · cashflow modeling · scenario design · explainable ML (SHAP, LIME)
Quantitative finance & risk Derivative pricing (IR, FX, credit, equity) · OAS & tranche valuation · market, credit & liquidity risk · CCAR & CECL stress testing · structured products · CRE / CMBS modeling
Programming & scientific computing Python · R · MATLAB · SAS · SQL · VBA
Emerging domains Climate & ESG risk modeling · quantum computing

Talks

Contact

Best reached by email at arghya.ghosh130@gmail.com. A full CV is available as a PDF.