Discover our latest industry insights, technical articles and more about clearing risk management for exchange-traded derivatives.
ISDA SIMM version 2.9 previews the methodology expected at the next calibration. It re-cuts Interest Rate and FX, cutting rates delta weights across the curve and expanding the high FX volatility currency group from nine currencies to thirty-seven.
ISDA SIMM v2.8+2512 introduces the latest semiannual calibration, updating risk weights, correlations, and concentration thresholds across asset classes to reflect market data through December 2025.
FIA data for Q1 2026 shows record exchange-traded derivatives activity, elevated open interest, rapid options growth, and sharp shifts across energy, rates, agriculture, metals, crypto, and prediction markets as geopolitical risk reshaped hedging demand.
Prediction markets let participants trade event contracts that turn collective views on future outcomes into prices. This primer covers how these instruments are classified in the US, how the market structure works, and the regulatory issues now drawing the most attention.
ISDA SIMM v2.8+2506 introduces the latest semiannual calibration, updating risk weights, correlations, and concentration thresholds across asset classes to reflect market data through June 2025.
Position transfers are mission-critical and still too manual. DMIST’s new Position Transfer Final Standard brings a single client request form, harmonised CCP upload fields, and clear timing targets. It’s built to cut re-keying, reduce errors, and pave the way for API-driven automation.
In this blog, we break down the key updates in ISDA SIMM 2.7+2412.1, including the new versioning scheme and adjustments to risk weights, correlations, and concentration thresholds across asset classes.
Artificial intelligence is no longer just an emerging trend in capital markets—it’s becoming embedded across trading, risk management, compliance, and operations. Recognising the speed of this transformation, IOSCO released a consultation report titled “Artificial Intelligence in Capital Markets: Use Cases, Risks, and Challenges.”
As derivatives markets evolve, questions of transparency, risk visibility, and price discovery have come back into focus. In its recent report, “Shining a Light on Derivatives,” WFE makes a clear case for the enduring value of exchange-traded, centrally cleared derivatives and warns of risks if market structures drift too far from transparency.
The rapid development of artificial intelligence is spurring many firms in the global derivatives markets to look for ways to deploy this new technology. In this article, executives from Cumulus9 and Chata.ai point to risk management as an area where AI can have a transformative impact.
In this blog, we'll delve into the key changes introduced in ISDA SIMM 2.7 compared to the previous version 2.6, focusing on adjustments in risk weights, correlations, and concentration thresholds across all asset classes.
Operational resilience in financial services ensures firms can withstand and recover from disruptions, maintaining critical operations under adverse conditions. This concept is crucial due to increasing risks from cyber threats, technological failures, and other operational challenges.
As derivatives market participants, staying informed and prepared for potential market disruptions is critical. The recent ESMA report on the 2023 global CCP fire drill offers valuable insights and highlights the importance of these exercises in enhancing the resilience of the financial system.
In an era where technology continuously reshapes the landscape of industries, the futures and options markets are on the cusp of a significant transformation. FIA Boca 2024 illuminated this trajectory, focusing on the revolutionary roles of artificial intelligence (AI) and tokenization in global capital markets.
Margin rates in the global exchange-traded derivatives markets have reached record-high levels. An insightful analysis of margin rates and anticipating their direction, particularly in the energy sector.
The landscape of global central counterparty margin models has witnessed a significant transformation in risk management. Central to this shift is the migration from the long-established SPAN model to various VaR-based models.
The U.S. Treasury market, integral to the U.S. and the global financial system, has evolved significantly over the past decades, representing a staggering 95% of the U.S. GDP today, compared to just 34% 20 years ago.
As the landscape of derivatives trading continues to evolve, it is crucial for market participants to be well-versed not only in central counterparty (CCP) initial margin requirements but also in the nuances of uncleared derivatives margin.
In a rapidly evolving digital landscape, the financial sector, including the derivatives markets, faces unprecedented challenges in terms of cyber threats and information and communication technology (ICT) related disruptions.
The recent cyber incident involving ION, a major vendor in the financial industry, served as a wake-up call. The industry’s operational resilience was tested and the consequences were significant.
As we navigate through the exciting digital landscape of fintech, we stand on the brink of a new era. We have been witnessing the increasing advancement and adoption of innovative technologies, including cloud computing, artificial intelligence and Web 3.0.
The Covid-19 pandemic has caused a period of high market volatility in March 2020. Notably, large increases in aggregate margin requirements were seen in both the centrally and non-centrally cleared markets.
As the price of crude oil futures plummeted to -$40 a barrel, the Black 1976 options pricing model - which cannot handle negative prices - was switched to the Bachelier model. But what are the implications of this switch?
Derivatives users are probably aware of the Standard Portfolio Analysis of Risk (SPAN) method used by most CCPs globally. Many would say that an update to the decades-old SPAN methodology is long overdue. For this reason, CCPs are gradually shifting from SPAN to VaR-based methodologies for margin calculation.
