mathematical-finance

I am working on vol surface modeling for cryptocurrencies and generally find that SABR calibrates to the vol surface on ETH better. I know that eSSVI ensures arbitrage free surfaces and therefore that we sacrifice a bit of goodness of fit for it, while SABR isn't guaranteed to be arbitrage free when interpolated, but even by adding safeguards and penalties to SABR to push the general surface towa…

I'm new in quant math, I'm self-studying it. I have two question in exp. daily range topic. How can we make the possibly most accurate estimation for expected daily ranges? My idea was to take data from yahoo finance, calculate realized vol using garman-klass-yang-zhang formula, then use a model (dunno which one) to calculate an expected trailing seven days avg historical vol for SPX. After that …

Digital assets are moving into a phase of institutional integration into derivatives markets. Trading venues,... Read more Digital Assets and Derivatives: Where Next?

If you've written a crypto backtester, there's a decent chance one line in it is quietly wrong. It's not the slippage model, not the funding rate, not the fill logic. It's the fee. I've reviewed a lot of grid-bot and market-making backtests over the past couple of years, and the fee handling falls into three buckets, roughly in order of how common they are: fee = 0.0 # "I'll add fees later" (you …

I know that the risk-neutral price of a call option with strike price $K$ is given by: $$C(K,S_T) = e^{-rT}\int_{0}^{\infty }(S_T-K)^+g(S_T)dS_t$$ Since a payoff is only valid when $ S_T >K $ this turns into: $$C(K,S_T) = e^{-rT}\int_{K}^{\infty }(S_T-K)\times g(S_T)dS_t$$ Now I wanted to differentiate the call formula once and twice with respect to K, so $\frac{\partial C}{\partial K}$ and $…

Financial charts often combine metrics that live on completely different scales. For example: company revenue measured in billions of dollars, stock prices measured in tens or hundreds of dollars. I wanted to recreate the infographic style seen on Visual Capitalist using pure Python and SVG. The chart combines: annual revenue as bars, monthly stock prices as a line, a secondary axis, a clipped gr…

Background I am trying to calculate the returns on a sequence of trades performed by an entity, where I do not know the starting capital. Therefore I assume a starting capital of zero. From these returns I want to calculate the Sharpe ratio of a portfolio on which these trades are performed. The initial idea of simply comparing successive portfolio values to calculate returns falls apart in this …

Every quant developer knows the feeling: you write an algorithmic strategy, run it against a basic backtesting script, and the equity curve looks like a flawless, vertical rocket ship. You feel like a market genius. But then you deploy that exact same strategy against a high-fidelity system—or live capital—and it immediately bleeds money. What happened? The strategy worked perfectly on paper beca…

The intrinsic value of a call option is found by subtracting the discounted strike price from the current share price: $IV = S - X/e^{rT}$ Put-Call parity: $S + p = c + X/e^{rT}$ $c = p + (S - X/e^{rT})$ Since the second term is literally the definition of the intrinsic value of a call, should the time value of the call option $(c-IV)$ be equal to the value of a put with the same strike price??? …

The SEC EDGAR API is one of the best-kept secrets in financial data engineering: every mandatory disclosure filed by every U.S. public company, available as clean JSON, for free, with no API key. If you've ever paid for a "fundamentals" data vendor or scraped a brokerage page for a balance sheet, you've been working harder than you need to. The raw, authoritative source — quarterly revenue, insid…

A comprehensive diagnostic analysis examining the structural market mechanics, valuation excesses, and macroeconomic vulnerabilities of the $4 Trillion tech listing wave. SpaceX, Anthropic, and OpenAI face unprecedented scrutiny amid warning signs of market overheating. 📊 Deep Research • 🎧 Podcast Available 🎧 Listen to Podcast: https://open.spotify.com/episode/5FNm8Qo5XB4yS3F8XSDDTS?si=4b8CPROc…

From the Derivatives Practice Group: This week, the CFTC published notices of proposed rulemaking for whistleblower rules and event contracts. The post Derivatives, Legislative and Regulatory Weekly Update (June 12, 2026) appeared first on Gibson Dunn .

This video explores how harness engineering provides the operational infrastructure that powers autonomous AI agents in quantitative finance — from execution runtimes and secure sandboxes to memory compaction and recursive skill orchestration. 🎥 Video Tutorial 🎥 Watch Video: https://youtu.be/_fcDzh04ntc Topics: quantitative finance, investment analysis, financial education, financial education…

LSEG CEO Julia Hoggett has issued a scathing critique of systematic internalisers’ (SIs) enthusiasm for a consolidated pre-trade tape in the UK, arguing that all market participants must contribute to […]

CME Group, the world’s leading derivatives marketplace, along with Morningstar, a leading provider of independent investment insights, announced that they have entered into a multi-year licensing agreement for CME Group to launch derivatives products based on key Morningstar equity index benchmarks, including the Morningstar US Total Market, Large Cap, Large Cap Value, Large Cap Growth, Mid Cap, …

Is it possible to construct a central bank meeting date curve, using futures prices & OIS rates, in QuantLib? Specifically, I mean a yield curve with flat (constant) forward rates in between meeting dates, and discontinuous moves on meeting dates (or 1 day after I guess, given that typically the rate change applies the next day). If so, how?

This article was originally published on the FlyTradr blog . Paper Trading vs Live Trading: Why Your Results Will Always Differ Paper trading is the step between backtesting and live trading. You run your strategy in real market conditions, with real prices and real market hours, but without any actual money at risk. It is a genuinely useful tool, and it should be a required step in any systemati…

In the hyper-competitive landscape of quantitative finance, raw LLMs are fundamentally ill-equipped for rigorous, fault-intolerant environments. The competitive moat has shifted to the operational infrastructure that wraps around them: The Harness. Explore execution runtimes, secure sandboxes, memory compaction, authorization fabrics, and the recursive autonomy of skills calling skills. 📊 Deep R…

What are the sources one can search for or view / download research articles and other publications on quantitative finance in addition to the Internet search engines?