Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
16,761 documents
The document compares programming needs in quantitative finance and argues that language choice depends on the work being done. For research and backtesting, it favors high-level languages such as Python, Matlab, and R because they shorten the path from an…
The discussion addresses whether maximum-likelihood estimates of a Heston stochastic-volatility model, often used in derivative pricing, can be applied to value at risk. The reply distinguishes risk-neutral calibration for pricing from real-world risk…
The document discusses whether the efficient market hypothesis (EMH) is literally true and what that means for quantitative research and active investing. Its central explanation is that many researchers treat efficiency as a useful benchmark: prices may…
The document asks how to select the autoregressive order and distributed-lag order in an ADL(p,q) model, preferably through an automated workflow in a statistical package. The author is looking for a working example that uses information criteria such as AIC…
The document discusses how portfolio optimization changes when skewness and kurtosis are added to mean and variance. It emphasizes that mean-variance optimization does not itself require normally distributed returns: the chosen portfolio can differ across…
The discussion addresses whether a long historical price series is suitable for forecasting a stock whose behavior has changed over time. It notes that an LSTM can be configured with a chosen number of time steps, so each training sample can use a recent…
The document considers how to compare strategies with different instruments, trade sizes, leverage, and capital needs. It argues that price movement or pip counts alone can misrepresent performance when strategies face different spreads, trading costs,…
The document derives the expected number of observation dates on which an asset price falls between two bounds. It treats the count as a sum of indicator variables and uses linearity of expectation, so the calculation needs the probability of being inside…
The discussion considers whether a regression can use several days of lagged prices, volatility, and trading range to forecast today's price. One proposed specification gives each factor and lag its own coefficient; another combines the factors observed on…
The document examines a long-horizon commodity-price simulation based on geometric Brownian motion, with drift and volatility estimated from historical log returns. It asks whether averaging many simulated paths offers a forecast and how the distribution…
The document considers how to estimate the future discounted exposure of a European stock put under the real-world measure, including whether a Black–Scholes value evaluated at the expected future stock price is an adequate approximation. It explains that…
The document describes a panel regression problem in which a large dataset is reduced to fewer observations by selecting a shorter time window and aggregating firms into groups. The author reports that estimated significance changes depending on how firms…
This answer outlines a visual approach to choosing autoregressive and moving-average orders in an ARIMA model. It recommends using the autocorrelation function (ACF) and partial autocorrelation function (PACF) to identify candidate AR and MA terms, with…
The document explores ways to estimate daily or weekly security prices when only monthly security observations are available, alongside higher-frequency index and sub-index data. One proposal applies expectation-maximization to log returns, treating them as…
The document presents a C++ implementation of a Leisen-Reimer binomial tree for pricing American options and describes cases where its output becomes NaN. The example computes transformed probabilities from two standardized option inputs, then uses those…
The document asks how to use a Python-defined local volatility function with QuantLib’s Black–Scholes process. It clarifies that a local volatility term structure handle cannot simply be constructed from an arbitrary Python function in the shown way; the…
The document asks how to obtain a reliable benchmark for American option values under the Heston stochastic-volatility model, with the goal of assessing bias in least-squares Monte Carlo (LSM). It considers a stochastic-volatility binomial model but asks for…
The document considers which size factor to use when estimating three-, four-, and five-factor performance regressions: the SMB series associated with the three-factor model or the version used in the five-factor model. It describes the regressions as…
The document examines why the German HML value factor can have a positive average return over a long sample yet a negative, statistically insignificant average in a later subperiod. A replication using German stock data reports a positive and significant HML…
The document explains how to interpret a period with no Value-at-Risk (VaR) violations. Under the unconditional coverage view, if observed returns never reach or fall below the forecast loss threshold, the model has produced fewer breaches than expected,…
The document describes a practitioner’s attempt to derive a money-market yield curve from observable interest rate swap rates whose floating coupons reference an average short-term forward rate. The author bootstraps the curve using linear interpolation…
The document presents an R simulation intended to reproduce a Kelly capital growth example. It defines a set of bets with different win probabilities and odds, assigns probabilities to selecting each bet, and associates each with an optimal Kelly stake. The…
The document asks how to establish convergence and estimate the convergence order of the Euler–Maruyama (EM) method using geometric Brownian motion (GBM). It distinguishes an empirical comparison of simulated means with the known GBM mean from a strong-error…
The document presents a Python attempt to reproduce results from Duan’s 1995 GARCH option pricing paper. It specifies GARCH parameters and a risk-neutral adjustment, simulates daily variance and innovations, evolves a stock price, and estimates a call price…