Derivative Market Research

For decades, options were viewed as derivative instruments prices derived from the underlying, offering no predictive information beyond the market's consensus volatility. That view is obsolete. Modern research demonstrates that options prices contain forward-looking information about tail risk, jump probabilities, and even future equity returns. The challenge is extracting that signal from noisy market prices. The most robust predictive framework is the volatility risk premium strategy: sell index options (typically out-of-the-money puts) to harvest the premium that reflects the market's overestimation of tail risk. But the edge has been arbed away through crowding. The new frontier is term structure of skew signals. When near-term skew is steep relative to long-term skew, it predicts near-term downside realization. When the term structure inverts (long-term skew steeper than short-term), it predicts a delayed volatility event. More sophisticated models use machine learning on options chains directly feeding the entire surface of implied volatilities across strikes and maturities into gradient boosting models to predict future realized volatility or equity returns. The features are not just levels but shapes: convexity, curvature, and the relative pricing of out-of-the-money puts versus calls. The firms that master options predictive models are not trading options as derivatives; they are trading options as primary information sources about market expectations. #OptionsTrading #VolatilityRiskPremium #ImpliedSkew #PredictiveModels #MachineLearning #QuantResearch #TailRisk

Today, we launched GPU compute forward curves derived from our prediction market prices. Forward curves are now available on Nvidia B200. H200, and A100 chips. Forward curves track implied future prices. They are how mature commodity markets form expectations, allocate capital, and manage risk. Energy, interest rates/SOFR, FX, metals, and agricultural markets all rely on market-implied forward prices. Despite becoming one of the key inputs in the global economy, compute has lacked that market-derived infrastructure. Compute right now is where oil was before NYMEX — traded only via OTC deals, just like oil used to trade OTC between producers and refiners. As compute becomes as fundamental to the economy as energy, the industry will need a similar derivative market to promote efficient price discovery. Prediction markets are uniquely suited to this problem. Compute is not one uniform commodity and spans many chips, grades, tenors, locations, and contract structures. A live prediction market can aggregate those dispersed views into transparent prices that reflect market expectations for different maturities. The opportunity is big. Hyperscalers are spending over $700B on compute this year and the market is expected to grow to $7-10T by 2030. If this market behaves like traditional commodity markets, a liquid derivative market could be 10-20x bigger than the underlying spot market. Compute is still not uniform enough, but this is a step towards standardization as forward curves will help us see the rise and fall of different model prices and how they correlate. The forward curve is a first step. Up next: futures and perps.

𝗡𝗘𝗪 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗣𝗔𝗣𝗘𝗥: 𝗖𝗢𝗡𝗧𝗥𝗔𝗖𝗧𝗦 𝗙𝗢𝗥 𝗗𝗜𝗙𝗙𝗘𝗥𝗘𝗡𝗖𝗘𝗦 My research paper on CFDs has been published! Contracts for Difference (CFDs) are derivative financial instruments that allow traders to speculate on the price movements of various assets, such as stocks, currencies, commodities, and indices, without actually owning them. Introduced in the 1990s, CFDs have become a significant part of global trading, particularly in markets like the UK. They provide leverage, enabling traders to gain exposure with minimal initial capital, but they also amplify risks, leading to concerns from a Shariah perspective. Mechanics of CFDs and Their Risks: CFDs operate on margin, meaning traders only deposit a fraction of the trade’s total value. The profit or loss is determined by the difference in the opening and closing prices of the CFD. CFDs allow both long (buy) and short (sell) positions, enabling profit potential in both rising and falling markets. However, CFDs present several risks: 🔷 Leverage Risk – High leverage can magnify losses, potentially exceeding the initial investment. 🔷 Margin Calls – If losses exceed available funds, traders must deposit additional capital or risk forced liquidation. 🔷 Liquidity and Execution Risks – Sudden price changes or market conditions can cause orders to be executed at unexpected prices. 🔷 Counterparty Risk – Traders rely on the financial stability of CFD providers, who may default on obligations. 🔷 Hidden Costs – CFD trading includes commissions, bid-offer spreads, overnight financing charges, and other fees that impact profitability. Shariah Review of CFDs From a Shariah compliance perspective, CFDs are deemed non-compliant due to multiple factors: ❌ Lack of Ownership – Traders do not own or possess the underlying asset, violating the principle that trade must involve real asset transfer. ❌ Bay’ al-Ma’dum (Selling Non-Existent Assets) – CFDs resemble speculative contracts where no actual ownership exists. ❌ Presence of Riba (Interest) – CFDs often involve overnight financing charges and cash-settled price differences, leading to Riba al-Fadl (excess in exchange) and Riba al-Nasi’ah (delayed settlement). ❌ Gharar (Excessive Uncertainty) – The speculative nature of CFDs, with highly uncertain outcomes, introduces excessive uncertainty, which is forbidden in Islamic transactions. ❌ Qimar (Gambling) – CFD trading involves speculative betting on price movements without real economic activity, making it akin to gambling, which is explicitly prohibited in Sharia. Other Implications ❌ CFDs create a zero-sum environment where one party’s gain directly results in another’s loss. The focus on speculation rather than productive investment raises ethical concerns even beyond the Shariah perspective. High leverage and the potential for rapid financial ruin further reinforce the argument that CFDs resemble gambling rather than legitimate trading.

SEBI just decided to change how risk is measured in the derivatives market. For those who are still not sure what does the change mean and it's impacts, here's a detailed breakdown: 1. Market-Wide Position Limit for Single-Stock Derivatives to be linked to the Cash Market 💡 Current System: MWPL for a stock in the derivatives market is currently based on open interest, without a strong link to the underlying cash market. The problem is that OI counts all contracts equally, be it be ITM, ATM or OTM contracts. This leads to situations where a stock enters an F&O ban even though the actual liquidity and risk in the cash market do not justify it. 💡 SEBI’s Proposal: MWPL for single-stock derivatives should be determined based on cash market activity to ensure that position limits are more reflective of the stock’s actual liquidity. 2. Instead of using a fixed method, SEBI proposes that MWPL should be set based on the lower of these two values: 1️⃣ 15% of the stock’s free-float market capitalisation 2️⃣ 60 times the stock’s Average Daily Delivery Value (ADDV) in the cash market Let’s assume a stock meets the following conditions: Free-float market cap: ₹10,000 crore 15% of free-float market cap: ₹1,500 crore Average Daily Delivery Value (ADDV): ₹100 crore 60× ADDV: ₹6,000 crore 👉 MWPL for this stock = ₹1,500 crore (Lower of the two) This means traders cannot take derivative positions exceeding ₹1,500 crore in this stock. Impacts: ✅ Fewer F&O bans ✅ Less market manipulation: Big institutional players have sometimes artificially inflated OI by opening large numbers of out-of-the-money contracts with little real impact on price movement. ✅ More accurate data for traders: Right now, traders who use OI trends to analyze market sentiment sometimes get misleading signals because low-risk contracts are also counted in the OI calculation. And the best part: SEBI has made this proposal open for public feedback until March 17, 2025. This means traders, investors, and stakeholders can review and provide suggestions before it is finalized. So, what’s your take on this?

Pleased to share our new research with Parviz Rakhmonov published in Review of Derivatives Research: "Stochastic volatility for factor Heath–Jarrow–Morton framework" We've extended the Factor Heath-Jarrow-Morton (FHJM) framework by incorporating stochastic volatility drivers that can capture the positive implied volatility skews observed in interest rate derivatives markets. Key contributions: • Analytical valuation formulas for swaptions and options on rate futures using moment generating functions • Implementation with both CIR and log-normal SV drivers • Application to Nelson-Siegel term structure model with successful calibration to USD swaptions and SOFR options • Novel convexity adjustment formulas that incorporate both skew and smile effects The framework provides a robust toolkit for pricing, risk management, and scenario generation for fixed income derivatives in an arbitrage-free manner. Full paper available (limited downloads): https://lnkd.in/dwEKvRn4 Working paper on SSRN: https://lnkd.in/dTBcrZBC Code implementation: https://lnkd.in/dUy54yxn #QuantitativeFinance #InterestRateModeling #StochasticVolatility #FixedIncome

Want a deep dive on India’s Derivatives Market? Read this… Almost everyone has begun trading options but these 6 data points give a good perspective about India’s derivatives market. I am not getting into the debate of notional & premium options turnover & the FX rate used for conversion. For sake of simplicity, we will use volume instead of turnover. 🔘 Total volume (# of contracts traded across all instruments- futures & options) has grown 16x from 4.87Bn contracts in 2019 to 80Bn contracts in 2023 🔥 🔘 Total volume in the Year to Date June 2024 period is 59Bn already! This means we will mostly end 2024 with 110-120Bn contracts, implying 24x growth v/s 2019- unheard/unseen anywhere else globally!🔥 🔘 98% of the total derivatives volume comes from the Weekly Index Options. In fact, weekly options on Nifty & Bank Nifty are most liquid contracts globally🔥 🔘 Single Stock Options contribute 1.3%, Index Futures contribute 10 basis points & Single Stock Futures contribute 40 basis points to the total derivatives volume! 🔘 Almost 60% of total options volume is done by the HFTs (High Frequency Trading firms), while the Retail investors account for nearly 35% of total options volume! 🔥 🔘 Almost 65% of total trades are done through Algos (Colocation & Non Colocation DMAs combined), while 28% of total trades are done by internet & mobile-based trading & only 7% are non-algo trades. The boom in options trading has completely changed the composition of volumes! 🔥 A smooth digital payments system, ease of opening a demat account (which by the way have quadrupled from 4 crore to 16.2 crore in 4.5 years), abundant liquidity in the market & a highly adaptive young population (median age 29) have led India to a pole position in the global derivatives markets! Hope that reading this has given you a good idea about the size of India’s derivative market! Happy to hear your thoughts on these data points! Data Source: NSE India Website! Also, the pic was taken while trekking to the Everest Base Camp in Nepal. In the background is the imposing Mt Pumori (7,161 metres/23,490 feet), which can be seen while trekking from Lobuche to Gorakshep! #students #education #equities #trading #investmentbanking #markets #nse #derivatives #options #iimlucknow #iimbangalore #jobs #careers #iimindore #CFA #MBA #FRM #CAIA #CPA #CA #iimcalcutta #mentoring #recruitment #hiring #networking #manankaro

LLM‑Powered Dynamic Hedging for Derivatives Markets move at tweet‑speed. A team from Columbia University & UC San Diego just plugged real‑time news and social‑media sentiment into their derivatives hedging engine. The result: portfolios that adapt every hour instead of once a day, earning more and losing less when volatility spikes. Key takeaways: 📈 Better returns: Risk‑adjusted performance (Sharpe) jumped from 1.25 to 1.85 in tests. 🛡️ Smaller drawdowns: Max loss fell from 15 % to about 10 % in rough markets. ⚡ Real‑time moves: The model reads thousands of news & social posts per day and tweaks hedge ratios on the fly. 🚀 Dynamic beats static: Constantly updated hedges out‑performed fixed ones on both profit and downside risk. 🤖 LLM sentiment matters: Text signals gave an extra boost even over basic dynamic rules. ⚙️ Scaling is next hurdle: Computational load and reliable data feeds remain the big barriers to day‑to‑day use. Authors: J Yang, Y Tang, Y Li, L Zhang, H Zhang The paper tests a hedge that watches news headlines and social posts with an AI language model. When the mood swings, the hedge changes size automatically. In simulations it made more money and lost less than a traditional “set once” hedge. The idea is promising, but running it live will need fast computers and good data pipes. #LLM #DynamicHedging #Derivatives #SentimentAnalysis #AIinFinance #RiskManagement #SharpeRatio #FinTech #PortfolioOptimization #MarketVolatility Follow us at Zanista.AI for more updates!

📢 New Trading Ideas!: "Deep Learning for Options Trading: An End-to-End Approach" 📈 Keep reading! 🔻 👉 Researchers have developed a new deep learning approach to options trading that learns trading signals directly from market data, without requiring specific pricing models or market assumptions. This research offers an alternative to traditional methods that depend on complex market dynamics specifications. 👉 Testing on a decade of S&P 100 equity options data, their models showed improved performance compared to conventional strategies. The LSTM model achieved a Sharpe ratio of 1.329, notably higher than benchmark approaches. The study focused on delta-neutral straddle options with portfolio-level volatility targeting at 15% annually. 👉 The implementation includes turnover regularization to address transaction costs, with the LSTM model maintaining performance up to 50 basis points in costs. The research found that mean-reversion strategies generally performed better than momentum strategies, and simple linear models proved effective with proper regularization. 👉 During market stress periods, including the COVID-19 selloff, the models demonstrated consistent performance. The framework's design allows for potential application to other derivatives and instruments where sufficient market data exists. 👉 The study contributes to the growing field of quantitative trading by showing how machine learning can be applied to options trading without relying on traditional pricing models. The results suggest potential for improving options trading strategies through data-driven approaches. ----------------------- → Join 3000+ Asset Pricing & Quant Finance enthusiasts who receive top new research ideas weekly in their email: bit.ly/3suSS6e ----------------------- Link to the paper: https://lnkd.in/dMwHF_v3

The Decline in Professional Investor Demand and Elevated Equity Volatility Risk The attached chart highlights a key market development: the decline in demand from professional investors for S&P futures, swaps, and options. This dynamic, shown by a widening Average Funding Spread (left axis) and corresponding movements in the SPX level (right axis), underscores rising risks for equity volatility. Key Observations: 1. Funding Spreads Widening: • The average funding spread (difference between futures contracts and Fed Funds rates) surged significantly through 2024, peaking around October before moderating slightly. • Higher spreads indicate reduced appetite among professional investors for leveraged equity positions. 2. SPX Price Decline: • The S&P 500 Index climbed steadily until late 2024, tracking rising risk premiums before entering a sharp decline in Q4. • This fall reflects growing caution and risk-off sentiment, aligned with lower liquidity and reduced hedging activity. 3. Elevated Equity Volatility: • The combination of declining demand for derivatives and widening spreads signals increased vulnerability to market shocks. • Lower participation by professional investors weakens the market’s ability to absorb sharp price movements, heightening volatility risks. Implications for Investors: • The divergence between equity prices and funding conditions suggests caution in the near term. • Elevated spreads point to potential dislocations in the derivatives market, with implications for portfolio hedging strategies. • Market participants should consider the interplay between liquidity and volatility in structuring portfolios for 2025. Source: Goldman Sachs Group Inc.

The International Council for Derivative Trading (ICFDT) has released a survey showing growing demand for specialist derivatives knowledge across trading, risk, and investment roles, with employers increasingly valuing candidates who can demonstrate practical expertise in futures, options, volatility, and risk management. According to the survey, 82% of employers said specialist derivatives knowledge positively influences hiring decisions, while 77% believe candidates often overstate their practical derivatives experience during recruitment. Meanwhile, 65% said derivatives-focused certifications help validate foundational product knowledge. The survey also found strong recognition of the Certified Futures and Options Analyst (CFOA) designation among employers familiar with it. Around 86% associated the qualification with futures and options knowledge, 80% with options strategy expertise, 73% with derivatives risk management, and 70% with volatility analysis. Additionally, 83% said it increases confidence in a candidate’s understanding of core derivatives concepts, while 67% said it would positively influence screening decisions for derivatives-focused roles. The findings come as global derivatives markets continue to expand, with exchange-traded derivatives volumes exceeding 200 billion contracts in 2024 and participation in options markets reaching record levels across both institutional and retail investors. https://lnkd.in/gcJrvMGp