risk-management

The modern digital banking ecosystem has expanded far beyond the traditional boundaries of internal IT parameters. With financial institutions increasingly relying on complex cloud architecture, core banking application programming interfaces (APIs), and third party FinTech vendor pipelines, the corporate threat Read More ... The post Continuous Monitoring and Vendor Risk Management in Banking fi…

The five skill gaps every BFSI L&D head needs to close in 2026 are: regulatory and compliance literacy, credit risk and NPA management, AI and digital risk readiness, ESG and climate risk awareness, and operational and fraud risk capabilities. Closing Read More ... The post The 5 Skill Gaps Every BFSI L&D Head Needs to Close in 2026 first appeared on Risk Management Association of India .

Why would a bonds discount margin widen but its price increase? Shouldn't the price be falling when margins are widening? Looking at the bond pricing formula, if the price is higher doesn't the rate of return have to be lower? What am I missing?

I am genuinely interested in understanding a little more about the risk aggregation approaches that are out there. I have been recently working on building an operational risk model under Basel 3.1, where the following approach applies. Risks are being modelled via LogN distributions (with the option to use Weibull or Pareto). Given there is no data history, an optimistic and a pessimistic loss i…

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 …

Scientific Reports, Published online: 15 June 2026; doi:10.1038/s41598-026-48479-2 A novel belief-degree-based uncertain Tchebycheff norm DEA model for the case study of risks prioritizing in e-business projects

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 $…

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??? …

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 .

The structural vulnerability of Non-Banking Financial Companies (NBFCs) within the Indian financial ecosystem has become a focal point of intense regulatory scrutiny. Unlike traditional commercial banks that enjoy a stable base of retail low-cost deposits, NBFCs operate under a highly Read More ... The post Liquidity Stress Testing Frameworks and NBFC Risk Governance first appeared on Risk Manage…

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…

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?

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…

I am implementing the layer-by-layer calibration of the time-dependent SABR model described in Hagan, Lesniewski and Woodward, Managing Vol Surfaces . In particular, I am using the effective-parameter formulas around equations (3.7)–(3.8) and the piecewise-constant integral update formulas in equation (3.11) of the paper. For each expiry $T_j$ , I first calibrate a standard SABR smile and obtain …

Almost every strategy that dies in production looked great in a backtest. The backtest wasn't unlucky — it was wrong, in one of three specific, detectable ways. Here's each one, the exact test that catches it, and why your usual metrics never warn you. 1. Lookahead bias — the silent killer It's almost never a deliberate shift(-1) . It hides in subtle places: Structural indicators computed over th…

Banks are increasingly turning to Artificial Intelligence (AI) to strengthen credit risk management by improving how they identify, analyse and mitigate potential defaults and portfolio losses, according to an analysis in The Financial Express. AI‑based credit risk models can process Read More ... The post AI tools help banks adopt smarter credit risk management first appeared on Risk Management …