Appendix E — Further Reading and Bibliography

Annotated bibliography organised by topic area.

E.1 OCaml Language and Ecosystem

Books

• Real World OCaml (2nd ed.) — Minsky, Madhavapeddy, Hickey (O'Reilly 2022).
  The definitive practical guide. Free online at dev.realworldocaml.org. Covers Core, Async, Dune, S-expressions, and testing.

• OCaml Programming: Correct + Efficient + Beautiful — Clarkson et al. (Cornell open-access 2023).
  Excellent for foundations: type system, modules, functors, interpreters.

• More OCaml: Algorithms, Methods, and Diversions — Whitington (Coherent PDF 2014).
  Intermediate algorithms written idiomatically in OCaml.

Online Resources

• https://ocaml.org — official docs, standard library reference, tutorials
• https://opam.ocaml.org — package search and docs
• Jane Street Tech Blog — in-depth posts on Core, Async, OxCaml

E.2 Financial Mathematics Foundations

• Options, Futures, and Other Derivatives (11th ed.) — Hull (Pearson 2022).
  The standard reference. Chapters 13–20 cover Black-Scholes, trees, Greeks, and exotics.

• Paul Wilmott on Quantitative Finance (2nd ed.) — Wilmott (Wiley 2006).
  Three-volume encyclopaedia. Rigorous derivations of PDEs, stochastic calculus, model risk.

• Interest Rate Models — Theory and Practice — Brigo & Mercurio (Springer 2006).
  Authoritative text on short-rate models, HJM, LIBOR market models, CDS, CVA.

• Stochastic Calculus for Finance I & II — Shreve (Springer 2004).
  Mathematically rigorous treatment of Brownian motion, Itô calculus, risk-neutral pricing.

E.3 Numerical Methods for Finance

• Numerical Methods in Finance and Economics — Brandimarte (Wiley 2006).
  Monte Carlo, finite differences, optimisation. Excellent balance of theory and code (MATLAB).

• Paul Glasserman: Monte Carlo Methods in Financial Engineering (Springer 2003).
  The definitive MC reference: variance reduction, quasi-MC, American options (LSM), Greeks by MC.

• The Mathematics of Financial Derivatives — Wilmott, Howison, Dewynne (Cambridge 1995).
  Readable introduction to PDE methods for option pricing.

E.4 Volatility and Stochastic Volatility

• The Volatility Surface — Gatheral (Wiley 2006).
  SVI parametrisation, Dupire local vol, Heston, variance swaps. Essential reading.

• Bergomi: Stochastic Volatility Modeling (CRC Press 2016).
  Modern rough volatility perspective; forward variance models.

• Hagan et al. (2002): "Managing Smile Risk". Wilmott Magazine, July 2002.
  Original SABR paper; Hagan's implied vol formula used in Chapter 26.

E.5 Fixed Income

• Fixed Income Securities (4th ed.) — Fabozzi (Wiley 2016).
  Comprehensive coverage of bonds, MBS, duration, convexity, structured products.

• Interest Rate Risk Modeling — Nawalkha, Soto, Beliaeva (Wiley 2005).
  Duration vectors, key-rate durations, factor models.

• Andersen & Piterbarg: Interest Rate Modeling (3 volumes, Atlantic 2010).
  The most rigorous multi-curve and XVA treatment available.

E.6 Credit Risk

• Credit Risk: Measurement, Evaluation and Management — Bluhm, Overbeck, Wagner (Springer 2002).

• Credit Derivatives: Trading, Investing and Risk Management — Meissner (Blackwell 2005).

• Li (2000): "On Default Correlation: A Copula Function Approach." Journal of Fixed Income, 9(4).
  Original Gaussian copula paper that defined CDO pricing for a decade.

E.7 Risk Management

• Value at Risk (3rd ed.) — Jorion (McGraw-Hill 2006). Comprehensive VaR reference.

• The Basel III Accord — Bank for International Settlements.
  https://www.bis.org/bcbs/publ/d424.pdf

• FRTB Final Rule (Jan 2019) — BIS.
  https://www.bis.org/bcbs/publ/d457.pdf

• Counterparty Credit Risk, Collateral and Funding — Brigo, Morini, Pallavicini (Wiley 2013).
  CVA, DVA, FVA, KVA with rigorous SDE pricing.

E.8 Portfolio Management

• Active Portfolio Management (2nd ed.) — Grinold & Kahn (McGraw-Hill 1999).
  Information ratio, alpha, factor models, portfolio construction.

• Advances in Financial Machine Learning — López de Prado (Wiley 2018).
  Feature engineering, meta-labelling, combinatorial purged CV. Modern ML for quant finance.

• Asset Management — Ang (Oxford 2014).
  Risk factors, illiquidity risk, smart beta, endowment investing.

E.9 Algorithmic Trading and Market Microstructure

• Algorithmic Trading and DMA — Johnson (4Myeloma Press 2010).
  DMA, execution algorithms, market microstructure. Practical reference.

• Optimal Trading Strategies — Kissell & Glantz (AMACOM 2003).

• Almgren & Chriss (2001): "Optimal Execution of Portfolio Transactions." Journal of Risk, 3(2).
  Foundational paper for the execution model in Chapter 23.

• High-Frequency Trading — Aldridge (Wiley 2013).
  Infrastructure, colocation, latency, statistical arbitrage.

E.10 Machine Learning in Finance

• Machine Learning for Asset Managers — López de Prado (Cambridge 2020).

• Artificial Intelligence in Finance — Hilpisch (O'Reilly 2020).
  Neural networks applied to option pricing, time series, reinforcement learning.

• Machine Learning in Finance: From Theory to Practice — Dixon, Halperin, Bilokon (Springer 2020).

E.11 Key Papers

E.12 Online Courses and Lectures

• MIT 18.S096 Topics in Mathematics with Applications in Finance — MIT OpenCourseWare
  Stochastic calculus, Black-Scholes, portfolio theory. Free lectures.

• Coursera: Financial Engineering and Risk Management — Columbia University
  Binomial trees, Monte Carlo, regression-based methods.

• QuantLib — open-source quant finance library (C++). Reading the source is educational. https://www.quantlib.org

• Quantopian / QuantConnect — cloud-based algorithmic trading platforms with Python APIs. Useful for backtesting ideas before implementing in OCaml.

E.13 Key Papers (Extended)

The table below extends the core list with additional seminal papers referenced in the book.

E.14 Difficulty-Graded Reading List

Introductory (no prior quant finance knowledge required)

• Hull: Options, Futures, and Other Derivatives — Chapters 1–12
• Minsky et al.: Real World OCaml — Chapters 1–8
• Clarkson et al.: OCaml Programming: Correct + Efficient + Beautiful — Chapters 1–10
• Shreve: Stochastic Calculus for Finance I — Chapters 1–4

Intermediate (undergraduate mathematics, some programming experience)

• Shreve: Stochastic Calculus for Finance II — Chapters 1–6
• Glasserman: Monte Carlo Methods in Financial Engineering — Chapters 1–4
• Gatheral: The Volatility Surface — Chapters 1–5
• Brigo & Mercurio: Interest Rate Models — Chapters 1–6
• López de Prado: Advances in Financial Machine Learning — Chapters 1–8

Advanced (graduate mathematics, professional quant experience)

• Andersen & Piterbarg: Interest Rate Modeling (3 volumes)
• Bergomi: Stochastic Volatility Modeling
• Brigo, Morini, Pallavicini: Counterparty Credit Risk, Collateral and Funding
• Wilmott: Paul Wilmott on Quantitative Finance (3 volumes)

E.15 OCaml Ecosystem Resources

Core Libraries Used in This Book

Community and Support

• OCaml Discourse — the main community forum
• OCaml GitHub — compiler source and issue tracker
• Jane Street Open Source — Core, Async, OxCaml, and more
• OCaml Weekly News — weekly digest of OCaml community activity
• #ocaml on Libera.Chat IRC — real-time community help

OxCaml (Chapter 31)

• OxCaml GitHub — compiler and runtime
• OxCaml Documentation — modes, stack allocation, SIMD, unboxed layouts
• Jane Street Tech Blog: "Oxidizing OCaml" series — motivation and design decisions

This bibliography is current as of early 2026. The field moves quickly; check arXiv (q-fin section) and SSRN for the latest working papers.