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