financial-econometrics

A smart contract does not need an overflow, reentrancy bug, or broken access-control check to lose money. Sometimes, the exploit is hidden inside an ordinary division: uint256 result = amount * rate / SCALE; The expression looks harmless. It may even produce the expected answer in every unit test. But financial smart contracts operate with integer arithmetic. Fractions are discarded, rounding dir…

I am trying to understand how climate risk impacts the financial market and I am calculating VaR and ES. I am applying the GARCH-MIDAS model to the FTSE MIB, using the Climate Policy Uncertainty Index (CPU) by Konstantinos Gavriilidis as an explanatory variable. The problem is that within my model confidence set, the best results are given by the GARCH and GJR models, while the GARCH-MIDAS is alw…

The activities that trigger financial crises can be undertaken by any financial institution, including both banks and nonbanks. The post For warning signs of the next global financial crisis, watch the activities of both banks and nonbanks appeared first on Atlantic Council .

I've bought Gatheral's book on Local Volatility and I have troubles with understanding a part where he shows that local variance is a conditional expectation of instantaneous variance. Why in the second equation from the bottom he just skips the term $\theta (S_T-K)dS_T$? He says that it's because $F_{t,T}$ is a martingale. I see that $F_{t,T}$ is a martingale, but don't know how this helps. Also…

Many people are turning to AI for financial advice but there are questions over the reliability of its responses

In a previous blog post of this series, the main univariate Value-at-Risk (VaR) estimation methods were described. Among these, and for scenario-based VaR estimation like historical VaR or Monte Carlo VaR, the most widely used [non-parametric] estimator is the corresponding order statistic of the empirical quantile of the portfolio return distribution, or a linear combination of two subsequent or…

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 …

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…

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…

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?

Quantitative finance has a reputation for being gatekept behind expensive certificates and heavy math. Some of the math is real. But the fastest way in is not a reading list, it is building the core models yourself in Python until they are no longer mysterious. Here is the path that works, and what you are really learning at each step. Start with returns and risk Everything begins with returns. S…

Quantitative Finance (Quant): The Comprehensive Learning Path Introduction Quantitative Finance (Quant) is the application of mathematical and statistical methods to financial and risk management problems. Quants are the "rocket scientists of Wall Street," blending deep mathematical rigor, financial theory, and computer science to price complex derivatives, manage risk, and identify profitable al…

A pre-submission readiness check for the Review of Financial Studies: how to judge contribution framing, identification strategy, robustness, the public code-release condition, and double-blind anonymization before you pay the submission fee.

Financial institutions have spent years building AI: fraud models, credit models, recommendation engines and risk systems. While this sprawl of task-specific models has been effective, it’s also constrained by siloed systems.  Siloed systems prevent institutions from developing a unified understanding of consumers’ financial behavior. As enterprise datasets keep growing, so does the gap between w…

A comprehensive guide to Agent-to-Agent (A2A) protocols, solving fragmentation, and orchestrating autonomous AI in modern finance. Explore capability advertising, stateful collaboration, opacity architecture, and the broader protocol stack (MCP, ACP, AGP) powering the next generation of financial infrastructure. 📊 Deep Research Topics: quantitative finance, investment analysis, financial educat…

PurposeThis study investigates the impact of financial development on CO2 emissions in BRICS-T countries within the framework of the Environmental Kuznets Curve (EKC) hypothesis over the period 1990–2021.MethodsThe study employs panel quantile regression to examine heterogeneous effects across different emission levels. Robustness analyses were conducted using CUP-FM and CUP-BC estimators. In add…

This is a summary of links recently featured on Quantocracy as of Wednesday, 05/27/2026. To see our most recent links, visit the Quant Mashup. Read on readers! Quantpedia Awards 2026 Winners Announcement [Quantpedia] Welcome to the Quantpedia Awards 2026 winners announcement. For the third time, we are proud to celebrate excellence in quantitative research and […] The post Recent Quant Links from…

I've been modelling the relationship between government debt auction calendars and implied volatility surfaces in emerging market contexts — specifically whether φ (sovereign refinancing pressure) and λ (liquidity stress) create predictable, calendar-driven dislocations that standard models like Heston and Bates don't capture. The intuition: retail-dominated EM options markets systematically unde…

Using the following Python code I am setting USD LIBOR Swap quotes. I found that by default settlementdays uses whatever is associated with the Index (in C++: if (settlementDays_==Null<Natural>()) settlementDays_ = iborIndex->fixingDays(); ). If I wanted to explicitly set settlementDays = 0 , how can I do that? I tried just use settlementDays = 0 , but the code does not seem to like named argumen…

I was able to obtain some tick data on a particular asset and I wanted to calculate the daily realized variance of the asset. After browsing through a few threads here, it seems the formula to calculate daily realized variance is simply (assuming you have constant time intervals): Where R^2 is the squared log returns from the constant time interval t , with a total of m time intervals during the …