Derivatives Trading Basics

This advance concept will take your options trading and analysis to the next level Volatility Risk Premium It refers to the compensation investors receive for bearing the risk of fluctuations in the underlying asset's price. It represents the difference between the implied volatility, typically derived from options prices, and the realized or historical volatility of the underlying asset. Let's break it down further Implied Volatility (IV): As previously explained, IV is the market's expectation of future volatility, derived from the prices of options contracts. Historical Volatility (HV): This is the realized volatility of the underlying asset over a past period, calculated from historical price data. Mathematically, VRP can be calculated as: VRP=IV−HV If IV is greater than HV (IV > HV), then the VRP is positive. This suggests that the market is pricing in higher future volatility than what has been realized historically. In other words, investors are demanding compensation for the uncertainty or risk of higher volatility in the future. Conversely, if IV is less than HV (IV < HV), then the VRP is negative. This indicates that the market expects lower future volatility compared to historical levels. Investors might interpret this as a sign of complacency or a belief that volatility will revert to lower levels. Practical Use in Options Trading: A. Selling Volatility: VRP becomes relevant when traders seek to sell volatility by writing (selling) options contracts. When the VRP is high, indicating a significant premium in implied volatility compared to historical levels, traders may consider selling options to capture this premium. They would sell options contracts at relatively higher prices and hope that the actual volatility remains lower than the implied volatility, allowing them to profit from the difference. B. Assessing Market Conditions: By analyzing the VRP, traders can gauge market sentiment and expectations regarding future price movements. High VRP may suggest increased uncertainty or fear in the market, leading to elevated options premiums. Conversely, low VRP may indicate complacency or confidence, potentially signalling a favourable environment for option buyers. C. Timing Trades: Traders can use VRP as a timing indicator for entering or exiting options positions. For example, during periods of exceptionally high VRP, traders may opt to sell options to capitalize on inflated premiums. Conversely, during periods of low VRP, traders may consider buying options to benefit from potentially undervalued premiums. D. Managing Risk: Understanding VRP allows traders to better manage their risk exposure in options trading. By being aware of the premium embedded in options prices, traders can adjust their position sizing, select appropriate strike prices, or employ hedging strategies to mitigate potential losses in case of adverse price movements. Below is the Chart of the VRP of Banknifty Platform -Quantsapp

Mondelez reported Q4 earnings yesterday and one of their comments made me doubt for a minute. They read in the inverted cocoa futures market that the cocoa S&D eventually balances. This is incorrect. An inverted futures curve reflects -most of the time- underlying physical tightness of a commodity. Market participants are prepared, or forced to pay a premium for nearby delivery of the commodity, as apparently they cannot wait for future deliveries. I say most of the time, as especially cocoa futures, but it happens in other commodities, have been squeezed, meaning, participants have taken a dominant long position to corner bonafide short hedgers, with the sole purpose to move the price. If futures prices invert for this reason, without justification from the physical underlying, what then usually happens is that cash differentials collapse (including product ratios), to pull physical cocoa beans to the exchange for physical delivery. This happened in the famous July 2010 squeeze, which eventually failed as there was no physical shortage, and the inverse collapsed shortly after the July delivery. An inverted futures market, meaning lower prices on the deferred, is not a prediction of lower future prices which people sometimes think. These are current prices for deferred delivery, period. Nor is it a prediction for the S&D to become balanced, after three consecutive deficits and potentially a fourth one. It is a reflection of current (extreme) tightness. And if Mondelez, or other chocolate confectionary companies need futures in exchange of products on 2025 positions, it can't cover that with 2026 futures. If the structural supply issues are not being resolved, and demand destruction does not accelerate faster than their reported minus 2% ish, then the futures market should remain inverted, and elevated. Maybe counterintuitive to Mondelez, but if hypothetically the market would price a large surplus next year, the futures curve could flatten, or even move into a carry, which leads to much less deferred downside price pressure than the nearby. For the rest, "emerge stronger" is obligatory and popular management language to engage employees and investors in tough situations. Time will tell. "Closely monitor and remain agile" is also popular management language. It means something like "Yeah yeah, we are in a tough spot, but at the moment we can't do much more than watching the market and hope for the best". I think this applies to many among the chocolate confectioners at the moment. The cocoa S&D will balance, and move to a surplus over time, slowly but surely. That is the purpose of the futures market, by impacting behavior of producers, processors and consumers. It's a slow, painful, and fascinating process. Seatbelts fastened.

🧨 Triple Witching, De-Fanged: How $6 Trillion in Derivatives Expire Four Fridays a year (March, June, September, December) feel like Wall Street’s version of a full-moon party: stock-index futures, index options, and single-stock/ETF options all die on the same afternoon. 1️⃣ Core Concept — What, When, Why the Spooky Name? • Three giant stopwatches—futures, index options, equity options—hit 00 : 00 together. • 1980s floor traders called the hour “bewitched,” so the nickname stuck. • In June 2025, roughly $6.5 trillion of contracts will vanish in one session. 2️⃣ Market Mechanics in Plain English • Dealer Gamma Unwind – Market makers are short options; as gamma spikes into expiry, they yank hedges, jolting prices. • Institutional Rolling – Asset managers close June futures, open September, spraying algos all day. • Options Pinning – Index hovers near “max-pain” strikes (huge open-interest magnets) until contracts die. • Witching Hour – 3–4 p.m. ET delivers the biggest spikes in volume and spreads. 3️⃣ How It Shows Up on the Tape • Volume: S&P futures trade about 2× a normal Friday; equities add ~1.5 billion shares. • Volatility: Intraday realized vol jumps ~40 %, then mean-reverts by Tuesday. • Spreads: Bid-ask gaps balloon in the final 30 minutes—market orders become landmines. 4️⃣ Why the Noise Rarely Lasts • Twenty-five-year data show an average triple-witch return of –0.04 %—statistically flat. • By Monday afternoon, the index is almost exactly where a random walk would predict. • Translation: fireworks intraday, no lasting trend. 5️⃣ India’s Perpetual “Mini Witching” • Last-Thursday monthly expiry already collapses all four contract types into one session. • Weekly index options replay the drama most Thursdays (moving to Tuesday for NSE from 1 Sep 2025). • Result: Indian traders live with a functional “triple witching” monthly, plus weekly aftershocks. 6️⃣ Investor Impact — Who Should Care? • Long-Horizon Investors – Stay diversified; ignore the strobe lights. • Active Equity Traders – Avoid late-day market orders; tighten stops; manage slippage. • Derivatives Specialists – Expiry is opportunity and risk; edge comes from gamma maps, OI heat-screens, strict sizing. 7️⃣ Practical Option Set-Ups (Illustrative Only) • Harvest IV Crush: Zero-DTE iron condor, exit an hour before the bell. • Ride a Vol Spike: ATM long straddle, gamma-scalp every ±0.25 % move. • Exploit Pinning: Broken-wing butterfly at max-pain strike • Institutional Roll Mimic: Futures calendar spread—sell expiring, buy next. 🔔 Takeaway — Fireworks or Minefield? For most investors, triple witching is a quarterly light show best watched from the safety of a long-term portfolio. For nimble intraday traders it’s fertile but treacherous ground. Edge exists—if you trade defined-risk structures, follow real-time data, and know when to walk away. Follow Quantace Research Disclosures :https://lnkd.in/d58vgQwk

Hedging Fixed Assets: Enabling Variable Benefits Through Swaps In the intricate world of bank treasury management, hedging strategies play a pivotal role in stabilising financial performance against market volatilities. One essential strategy involves hedging fixed assets, particularly through interest rate swaps, which can substantially shield a bank's balance sheet from interest rate fluctuations. Consider a typical scenario where a bank has substantial fixed-rate assets, such as long-term loans. While these provide a stable income stream, they also pose a risk should the interest rates rise, increasing the bank's funding costs. To mitigate this risk, banks often enter into interest rate swaps as a hedging mechanism. In an interest rate swap, the bank would agree to pay a fixed rate to a counterparty while receiving a variable rate in return. The crux of this strategy lies in the variable receive leg of the swap. This variable rate adjusts with market conditions, ideally increasing when the interest rates climb. Thus, if the bank's funding costs rise due to higher interest rates, the receipts from the variable leg of the swap also increase, offsetting or covering the heightened costs. This approach is not only conservative but also advantageous, as it aligns the bank’s income with its expenses in a manner that is both prudent and responsive to market conditions. By employing such hedging tactics, banks can better manage their asset-liability mismatches and enhance financial resilience. Understanding these strategies is essential for banking professionals who strive to ensure the financial health of their institutions in an unpredictable economic environment. Through such realistic and accurate risk management practices, banks can safeguard their operations and maintain a robust balance sheet.

Learning Quantitative Trading:🔍 **Exploring Market-Implied Probability Distribution and Local Volatility Smile** 🔍- Lessons from Virtual Barrels by Dr. Ilia Bouchouev Here's a breakdown of the key takeaways: - **Inverse Problem Solving**: By leveraging options prices across all strikes, we can reverse-engineer the **market-implied probability distribution**, (the second derivative of options with respect to strike price K). This allows us to move beyond simple models and understand the actual probability landscape, critical for accurate pricing and risk management. - **Risk-Neutral Probabilities**: The distribution we extract is not a real-world probability, but a **risk-neutral probability**—a construct used in pricing models where the real-world drift is neutralized. This distinction is essential for traders relying on these models for accurate predictions. - **Butterfly Spread Analysis**: Butterfly spreads help us approximate the second derivative of option prices, revealing the **Dirac delta function** at a strike price, which represents the market-implied probability density. Traders use this to bet on precise price levels, making butterfly spreads a sharp tool in the arsenal for identifying price level probabilities. - **Spotting Arbitrage Opportunities**: Market-implied probability distributions are invaluable for volatility traders in spotting **arbitrage opportunities**. Unlike implied volatilities, which smooth out anomalies, probability distributions expose any inconsistencies, making them visible "under the microscope." - **Local Volatility Function**: To capture trading opportunities fully, it's crucial to model the evolution of prices and the **local volatility function**. This function ties option prices with nearby strikes and expirations, intertwining them in ways that are essential for hedging and pricing, particularly in the oil market. - **Practical Limitations**: Direct application of theoretical models like the **Dupire equation** faces practical limitations, especially in markets like oil, where options with a continuum of maturities are not available. This challenges traders to adapt their models creatively to the realities of market data. 💡 **Takeaway**: Understanding and applying market-implied probability distributions can significantly enhance your trading strategy, providing clarity on price distributions and uncovering hidden arbitrage opportunities. But remember, it's not just about seeing the snapshot—the evolution of prices and volatility over time is where the real edge lies. 🔗 **Let’s Discuss**: How do you integrate market-implied probability distributions into your trading strategy? Have you spotted any recent arbitrage opportunities using this method? Share your thoughts and experiences below! 👇 #Finance #QuantitativeTrading #OptionsTrading #RiskManagement #VolatilityArbitrage #MarketInsights #TradingStrategy

*** Four Models in Quantitative Finance *** Four models in quantitative finance aren’t just mathematical abstractions—they shape markets, risk strategies, and derivative pricing with precision and elegance. 1. Black-Scholes Model A benchmark in option pricing theory, the Black-Scholes model revolutionized finance by offering a closed-form solution. Key Concepts: • Purpose: To price European-style options without dividends. • Assumptions: Lognormally distributed returns, constant volatility, frictionless markets. Why It Matters • Provides intuitive insights into how time, volatility, and interest rates affect option value. • Basis for volatility surfaces and risk metrics like delta, gamma, and vega. 2. Binomial Tree Model A discrete-time model that builds flexibility into option pricing. Key Concepts: • Structure: Price evolves through “up” and “down” moves in a recombining tree. • Setup Parameters: Time steps, up/down factor, risk-neutral probability. • Pricing Logic: Work backward from terminal payoffs using probabilistic expectations. Advantages: • Flexibility: Works with American options (early exercise). • Intuition: Visual tool to model asset price evolution. • Adaptability: Can incorporate changing volatility or dividends. 3. Monte Carlo Simulation This is a powerful numerical technique for pricing and risk analysis, especially in complex or path-dependent cases. Key Concepts: • Foundation: Simulate thousands of paths for underlying assets using stochastic processes. • Applications: Exotic options, Value-at-Risk (VaR), portfolio stress tests. • Key Elements: Random number generation, payoff averaging, and variance reduction methods. Why It’s Powerful: • Can handle multi-dimensional problems where no analytical solution exists. • Allows incorporation of real-world features, like jumps or stochastic volatility. 4. Finite Difference Method A grid-based numerical technique for solving partial differential equations, like those in the Black-Scholes framework. Key Concepts: • Approach: Replace derivatives with discrete differences (e.g., Δt, ΔS). • Types: • Explicit Method (forward time, centered space) • Implicit Method (backward time, stable for larger steps) • Crank-Nicolson (balanced hybrid of the two) Applications: • Pricing options with barriers, path dependence, or early exercise features. • Handles boundary conditions efficiently. --- B. Noted

💼 Quantitative Analyst | Trader | Market Strategist | Python for Finance Executed a comprehensive market analysis pipeline integrating quantitative methods and real-time financial data to support trading strategies and macro-level insights. 📊 Key Deliverables: 📉 Volatility Forecasting: Tracked and visualized the CBOE VIX over 12 months. Identified key risk levels: 📍High: 52.33 📍Low: 11.86 📍Mean: 18.52 Used VIX behavior to inform options hedging and volatility-arbitrage strategies. 📈 Sector Rotation Analysis: Computed 1-year cumulative returns across 11 sectors. Outperformance observed in: Communication Services: +23.87% Consumer Discretionary: +23.83% Financials: +23.00% Applied findings to reallocate portfolio exposure and develop long/short trade ideas. 📉 Yield Curve Dynamics: Assessed US Treasury yields (3M: 4.24%, 10Y: 4.44%, 30Y: 4.90%) for macro strategy alignment and duration hedging. 🧠 Market Sentiment Modeling: Derived SPY options put/call ratio = 0.47, signaling bullish sentiment — integrated this as a momentum overlay in tactical trading. 🛠️ Stack: Python | Pandas | Matplotlib | yFinance | Data Visualization | Seaborn 🔍 Focus Areas: Macro Strategy | Quant Research | Portfolio Optimization | Market Psychology #QuantTrader #FinancialAnalysis #PythonQuant #TradingStrategies #VolatilityModeling #MarketSentiment #MacroAnalysis #VIX #SectorRotation #FixedIncome #PutCallRatio #DataScienceInFinance #InvestmentResearch #LinkedInFinance #AlphaDriven #CFA #Finance

CVA Swap: a new type of credit derivative Usually, credit risk is hedged by using credit default swaps (CDSs) on a counterparty’s credit name. However, there has been a shift in the landscape, with single-name CDSs becoming less liquid, and the largest portion of credit trading now done on indexes. This trend contributes to banks’ preference of transferring CVA risk to the buy side. A CVA swap is a new type of derivative that allows banks to hedge credit risks. "In this setup, trading desks pay an incremental CVA charge at the begining of the trade, followed by the XVA desk hedging all of the associated risks, which, in the event of a default, bears all the losses. In the context of CVA swaps, apart from the initial upfront payment of CVA from the bank to a hedge fund, there are also daily margins. Effectively, the hedge fund pays the daily difference in CVA. If no default occurs before the trade matures, CVA diminishes to zero at maturity. However, in the event of a default, it equates to losses incurred by the bank. Since CVA is always negative for the bank, the hedge fund pays when CVA increases in absolute value, and receives payment when CVA decreases in absolute value. For example, if CVA changes from -$1000 today to -$1100 tomorrow, the hedge fund pays $100. Conversely, if tomorrow’s CVA is -$950, the hedge fund receives $50." https://lnkd.in/gjkVcjZX #riskmanagement #creditrisk #derivativestrading #cva

Recently, 𝐍𝐢𝐟𝐭𝐲'𝐬 𝐕𝐨𝐥𝐚𝐭𝐢𝐥𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 𝐨𝐫 𝐕𝐈𝐗 reached 12.72 indicating some relief among market traders. But what is VIX?   VIX (Volatility Index) is a key market sentiment indicator that measures expected volatility in the Indian stock market in the near term, typically over the next 30 days   Unlike the NIFTY index, which tracks market direction based on stock prices, India VIX focuses solely on volatility. It is calculated using the Black-Scholes model based on NIFTY options prices, considering factors like: - Strike price - Market price of the stock - Time to expiry - Risk-free rate - Volatility   💡𝐅𝐨𝐫𝐦𝐮𝐥𝐚 VIX = 100 * √((Sum[Weighted Implied Volatility Squared])/Total Weight)    - Sum[Weighted Implied Volatility Squared] denotes the sum of the squared implied volatilities (IV) multiplied by the respective weights. Implied volatilities (IV) are usually available on the NSE website -->Market Data-->Derivatives-->Option Chain and their respective weights are the corresponding open interest (OI) values - that tell us how many contracts are currently active for an option strike price - Total Weight denotes the sum of the open interest of all options used in the calculation   💡 𝐇𝐨𝐰 𝐢𝐬 it 𝐮𝐬𝐞𝐝? - 𝐇𝐢𝐠𝐡 𝐕𝐈𝐗 = 𝐇𝐢𝐠𝐡 𝐕𝐨𝐥𝐚𝐭𝐢𝐥𝐢𝐭𝐲: A rising VIX signals increased uncertainty, market swings, or fear (e.g., financial crises) - 𝐋𝐨𝐰 𝐕𝐈𝐗 = 𝐒𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: A declining VIX indicates lower volatility and steady market conditions - Investor Sentiment Gauge: Helps traders assess risk levels before making investment decisions   💡𝐈𝐧𝐝𝐢𝐚 𝐕𝐈𝐗 𝐓𝐫𝐞𝐧𝐝𝐬 - Typically ranges between 15-30 (normal volatility) - Spiked to ~85-90 during the 2008 financial crisis & 2020 pandemic, signalling extreme fear

So you’ve found an actionable trade setup. What’s next?  Deciding whether to take an active or passive approach.  There are two main trade categories:  Active entries and passive entries.  An active entry is when you believe a significant move is about to happen. For example, if you spot a breakout in a stock like USO moving above its trading range: In this scenario, you’re willing to pay a debit (upfront cost) for the trade, as long as the potential reward justifies the risk. Typically, you’ll look for at least a 2:1 reward-to-risk ratio, often using a vertical call or put spread. Let’s say USO breaks out to $14.50 and you set a target price at $16.00.  You’d want your short strike at $16.00 and your long strike as low as possible while still maintaining your 2:1 reward-to-risk ratio.  Ideally, your options expiration should be between 4 and 10 trading days out.  For example, buying a two-week-out 15/16 call vertical for $0.33 or less could fit your risk parameters and trading plan. A passive entry, on the other hand, is based on the belief that a major move is NOT going to happen.  Suppose a stock or ETF is trading near a strong support or resistance level, and you feel confident that it won’t break through.  In this situation, you’re not expecting a dramatic reversal, just that the price will hold steady.  Here, a credit spread is often the best strategy, as you profit if the stock stays above support or below resistance. For a passive trade, set your short strike at the price level you believe will be defended (the support or resistance), and choose your long strike to ensure your risk/reward ratio fits your strategy.  This approach allows you to collect premium while betting on stability, rather than a big move.  By understanding when to use active versus passive entries, you can tailor your trades to match your market outlook and risk tolerance. That increases your chances of consistent success.