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.
12,303 documents
The document asks how a long straddle could produce positive profit when the underlying finishes at the strike and the option expires worthless, despite high volatility during the holding period. One explanation is dynamic delta hedging: as the underlying…
The document discusses errors encountered when using an R interface to Ox for fitting GARCH models, including FIGARCH and possibly FIEGARCH specifications. The reported failures include undeclared file input and output functions in an Ox source file and a…
The document considers how to evaluate a multi-agent stock market simulation against historical prices for large companies across several sectors. The question proposes matching the volatility of simulated prices to observed volatility while also checking…
The document asks whether range-based estimators such as Garman–Klass can be combined with EWMA or ARCH-family models, and whether a dynamic volatility model is still needed when using such estimators. The response points to research that incorporates…
The document asks whether stock returns can be rescaled to look more normally distributed and describes an attempt to reproduce a published method on S&P 500 returns. The approach clips each return at a multiple of an evolving volatility estimate, updates…
The discussion addresses where to obtain several years of daily implied volatility data for large-cap stocks for backtesting. It suggests Interactive Brokers as a possible source, while noting that access requires a market data subscription. A separate reply…
The document introduces the GARCH model’s decomposition of a return innovation into a time varying conditional scale and a standardized shock. Its variance equation combines a constant, lagged squared innovations, and lagged conditional variances. The author…
The document asks why normal or lognormal return assumptions remain common when empirical return samples can fail normality tests. It presents three perspectives: one answer argues that such models are mathematically valid under restrictive assumptions,…
The discussion offers brief practitioner-oriented guidance on derivative pricing methods. It describes stochastic-volatility and Lévy-process models as popular, while suggesting jump-diffusion models are less common. It confirms that Fourier transform…
The discussion suggests testing a Merton jump-diffusion model on broad equity benchmarks and their exchange-traded funds, naming the Nasdaq, S&P 500, Russell 2000, and examples such as QQQ, SPY, and IWM. It notes that these liquid benchmarks can help compare…
The document explains how to value a payoff based on the difference between a stock price at an intermediate date and its later price. It first clarifies that the stated payoff needs a positive-part operator to describe a put-like claim; under that…
The post questions a claim that puts are priced using historical volatility while calls use implied volatility, attributing the distinction to differences in liquidity. The reply challenges that framing: implied volatility is obtained by solving an…
The document asks whether option sellers should prefer very short expirations, where time decay is concentrated, or sell options with more time remaining. It uses an at-the-money straddle example to compare premiums at two expirations and observes that the…
The document considers how to compare an at-the-money call with an out-of-the-money call when each has a different implied volatility. Its central point is that implied volatility is inferred from market prices through a pricing model, often one that assumes…
The answer explains why local volatility (LV) can misprice double-no-touch options even though it produces random realized volatility along simulated paths. Each path integrates variance across the local volatility surface, so the realized volatility varies…
The document describes why a trader or product issuer might use a forward-starting option instead of a standard option. Such an option’s strike is set at a future date, so it mainly exposes the holder to volatility after that strike-setting date. It may be a…
To rank days by volatility during a fixed intraday window, use the sequence of closing prices sampled at each minute. Convert those prices to logarithms, calculate consecutive differences, and take the standard deviation of the resulting minute log returns…
The discussion asks how to classify a bid-ask spread as favorable for scalping or ordinary day trading, especially when comparing low-priced, thinly traded OTC shares with more liquid stocks. It notes that absolute spread size can mislead: a wider spread in…
The document considers fitting a two-normal mixture to stock returns, with one component intended to represent the distribution's central behavior and another its tail. It highlights the central estimation problem: rare tail events may be absent from a short…
The note considers how to extend an equity local volatility model, initially calibrated to vanilla options, so it can also match liquid quanto option prices. It outlines an approach based on market smiles: use the liquid FX smile and calibrate a compo option…
The document shows how to fit SABR model parameters to a set of observed option volatilities using Python and QuantLib. It computes model volatilities for each strike, measures the root mean squared difference from market quotes, and uses a constrained…
The document asks what implied volatility represents in vanilla option pricing and whether fitting it to market prices can reveal mispricing. It contrasts treating volatility as the adjustable input to Black–Scholes with an alternative parameterization that…
An inverted volatility surface can reflect market conditions, but sparse or unreliable option quotes may make its shape misleading. In the ONDK example, strikes are widely spaced and bid-ask spreads are broad, leaving too few useful observations to estimate…
The document asks whether a forward market model (FMM), extending the classic LIBOR market model to handle backward-looking risk-free rates (RFRs) and term rates together, has been combined with stochastic volatility such as SABR. The motivation is pricing…