FAQ in Quantitative Finance

(Feel free to suggest any additions or further refinements)

• 1827: Robert Brown discovers Brownian motion for small particles in liquid. Log-normal random walk later becomes a classical model for stock prices.
• 1900: Louis Bachelier develops mathematics for random walks in his PhD thesis, "The Theory of Speculation".
• 1900s: Vinzenz Bronzin discusses put-call parity and delta hedging in his book, "Theorie der Pr?miengesch?fte".
• 1905: Albert Einstein publishes a paper on diffusion and thermodynamics, laying the groundwork for stochastic calculus.
• 1908: Vinzenz Bronzin publishes a book on options pricing and introduces the concept of risk neutrality.
• 1911: Lewis Richardson solves diffusion equations using finite differences and investigates fractals.
• 1915, 1926/7: Frederick Mills and Maurice Olivier discover high peak and fat tails in price data.
• 1923: Norbert Wiener develops a rigorous mathematical theory for Brownian motion.
• 1950s: Paul Samuelson pioneers derivative pricing through rational expectations.
• 1951: Kiyoshi Itō proves lemma relating stochastic variables and their functions, enabling the SDE for an option from the SDE for the underlying asset:

• 1952: Harry Markowitz introduces portfolio selection, maximizing expected return for a given risk level.

• 1962: Benoit Mandelbrot identifies fat tails in cotton price returns, challenging assumptions of normality.
• 1963: William Sharpe, John Lintner, and Jan Mossin independently develop the Capital Asset Pricing Model (CAPM).

• 1966: Eugene Fama proposes the Efficient Market Hypothesis (EMH) in his paper, "Random Walks in Stock Market Prices".
• 1968: Edward Thorp discovers the optimal Blackjack strategy, builds a wearable computer, and develops an option pricing model.
• 1973: Fischer Black, Myron Scholes, and Robert Merton derive the Black-Scholes equation using geometric Brownian motion:

• 1974: Robert Merton models firm value using call options, predicting default risk.

• 1977: Phelim Boyle relates option prices to Monte Carlo simulations of asset paths.

• 1977: Old?ich Va?í?ek derives the bond pricing equation, a precursor to the Hull-White model.

• 1979: John Cox, Stephen Ross, and Mark Rubinstein develop the binomial options pricing model.

• 1981: Harrison, Kreps, and Pliska introduce equivalent martingale measures and risk-neutral pricing.
• 1986: Thomas Ho and Sang-Bin Lee develop the first no-arbitrage model for fitting the yield curve.
• 1992: David Heath, Robert Jarrow, and Andrew Morton (HJM) introduce a framework for modeling the evolution of interest rates.
• 1990s: Practitioners value multi-asset options using Monte Carlo simulations.
• 1994: Mark Rubinstein calculates implied probability distributions from option prices.
• 1996: Marco Avellaneda and Antonio Paras create a nonlinear model for uncertain volatility.
• 1997: Alan Brace, Dariusz Gatarek, and Marek Musiela (BGM) develop a multi-factor interest rate model.

• 2000: David X. Li introduces copula functions for modeling collateralized debt obligations (CDOs).

• 2002: Patrick Hagan et al. propose the SABR model for interest rates and the volatility smile.
• 2007/8: The global financial crisis exposes the limitations of simplistic quantitative models.
• 2010s: Quants focus on liquidity risk and funding costs in derivatives pricing post-crisis.
• 2010s: Machine learning and alternative data gain traction in quantitative investing.
• 2010s: High-frequency and algorithmic trading strategies proliferate.
• 2010s: Regulations such as Basel III and FRTB aim to strengthen risk management.
• 2010s: Blockchain, cryptocurrencies, and DeFi emerge as new frontiers for quants.
• 2020s: Machine learning and AI continue to advance and find new applications in finance.
• 2020s: ESG and sustainable investing become mainstream considerations for quants.
• 2020s: Quantum computing shows promise for solving complex financial problems.