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Advanced Options Volatility: Delta-Neutral Skew & Portfolio Hedging

963 Learners
12 Hours
Go beyond Greeks and master advanced options volatility concepts through hands-on Python labs and real case studies. Learn to measure and trade skew, calculate IV skew, IV rank, and skew rank, and develop delta-neutral portfolios. Build and backtest strategies like straddles and calendar spreads, apply machine learning for entry and exit rules, and manage portfolio risk effectively using Greeks such as delta and gamma.
Level
Advanced
Author
QuantInsti®
Price Lifetime Access
₹23586₹30239(Additional 22% off)
Original Price: ₹53999

No Cost EMI available

  • Learning OutComes
  • Case Studies
  • Python Lab
  • Syllabus
  • Reviews
  • Faqs

Live Trading

  • Analyse volatility skew & surfaces; compute delta-neutral skew and thresholds.
  • Construct delta-neutral options portfolios; hedge with Greeks (Δ/Γ/ν).
  • Trade event-driven volatility (e.g., FOMC meetings) while avoiding IV crush.
  • Apply portfolio hedging frameworks and stress testing.
  • Build Python backtests for skew/event strategies on real options data.
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REAL-WORLD CASE STUDIES

Case Study 1

Case Study 1: Delta-Neutral Skew Trading

Detect skew using Δ=±0.25 puts vs calls; set rule-based signals (e.g., > +10% / < –5%)

The case study demonstrates trading volatility skew using a delta-neutral options strategy by simultaneously buying and selling options at different strikes. This exploits mispricing in implied volatilities while hedging directional exposure. Results show how traders can capture edge from volatility differences rather than predicting market direction.

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Case Study 1: Delta-Neutral Skew Trading

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Case Study 2

Case Study 2: Event-Driven Long Straddle

Enter ~14 days pre-event to capture IV build-up; exit T-1 to avoid IV crush.

The case study applies a long straddle around scheduled events like FOMC meetings, buying both ATM call and put options ~14 days before, then exiting the day before the announcement. Backtests suggest this approach can benefit from the rise in implied volatility while avoiding post-event “volatility crush,” though outcomes vary across events. Try the python notebook in the course to backtest across historical FOMC meetings; visualize IV and P&L.

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Case Study 2: Event-Driven Long Straddle

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Hands-On Labs in Python

  • Compute Greeks; plot vol skew graphs; estimate IV rank/skew rank.
  • Run GARCH & Monte Carlo simulations (for comparison to skew/event methods).
  • Compute Implied Volatility using LSTM Network.
  • No setup: Start instantly with a pre-configured browser environment
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Course Features

  • Community
    Community

    Faculty Support on Community

  • Interactive Coding Exercises
    Interactive Coding Exercises

    Interactive Coding Practice

  • Capstone Project
    Capstone Project

    Capstone Project Using Real Market Data

  • Trade & Learn Together
    Trade & Learn Together

    Trade and Learn Together

  • Get Certified
    Get Certified

    Get Certified

Prerequisites

This course is ideal for options traders, quant/algo developers, portfolio managers, and risk professionals. It is recommended to complete the courses “Volatility Trading Strategies for Beginners“ and “Options Volatility Trading: Concepts and Strategies” beforehand, as these courses provide valuable background knowledge.

Syllabus

Module 1: Introduction & Core Concepts

Introduction: Course overview, structure, and interactive learning tools.

Options Volatility: Fundamentals of volatility behaviour and trading opportunities.

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Sourcing Data: Importance of data, sourcing/storing in pickle files, and available sources.

Options Pricing: Black-Scholes-Merton model, its assumptions, and alternative pricing models.

Deliverable: Starter notebook with data sourcing and BSM model examples.

Module 2: Volatility Skew & Surfaces

Volatility Skew: OTM puts vs calls, IV differences, plotting skew.

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Kinks in Volatility Surface: Why surfaces aren’t always smooth, irregularities, and trading limitations.

Calculation of Volatility Skew: Objective methods and interpretation.

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Trading Volatility Skew: Hypothesis formation, backtesting skew-based strategies, performance analysis.

Deliverable: Volatility skew analysis notebook.

Module 3: Delta-Neutral & Volatility Strategies

Delta Neutral Skew Analysis: Using delta vs average OTM IVs, trading delta-neutral skew.

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Volatility and Mean Reversion: Mean-reversion behaviour of volatility, why it doesn’t trend like prices, and trading strategies.

Trading Strategy on VIXY Using VIX: VIX index insights, VIXY ETF, and applying mean reversion to short VIXY strategies.

Deliverable: Delta-neutral and VIXY trading strategy backtest.

Module 4: Ranks & Advanced Forecasting

IV Rank: Intuition and calculation, context of IV values.

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IV Rank in Trading: Strategy creation, backtest, and performance.

Skew Rank: Intuition, calculation, and combining IV & Skew Rank.

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Skew Rank in Trading: Short Straddle Strategy - IV Rank and Skew Bank.

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Forecasting IV Using Machine Learning: Using multiple variables, ML-driven predictions.

LSTM’s Role in Forecasting IV: LSTM for IV forecasting, trading strategies on predicted IV.

Deliverable: Forecasting and rank-based strategy generator.

Module 5: Event-Driven Volatility & Options Pricing

Volatility Around Events: Historical patterns, pre-event volatility surges, and event-driven strategies.

Volatility and Options Pricing: Impact of volatility on option pricing and trading.

Relative View on Volatility: Long calendar spread strategy, backtest, and performance analysis.

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Deliverable: Event-driven volatility strategy backtest notebook.

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Module 6: Risk Management & Hedging

Risk Management of a Volatility Position: Dollar-based risk management.

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Risk Management Using Option Greeks: Managing risk through Greeks in volatility positions.

Hedging the Option Greeks: Example of hedging a short straddle with Greeks.

Risk Management Using Delta Hedging: Concept and application in short straddle.

Threshold for Delta Hedging: Selective hedging to reduce transaction costs.

Implementation of Delta Hedging: Selective delta hedging in Python.

Hedging an Options Portfolio Using Greeks: Portfolio-level hedging across multiple assets.

Summary: Recap of learnings, with downloadable code and data.

Deliverable: Risk management and hedging implementation notebook.

Capstone Project 1: Strategy Deployment and Analysis

In this project, you will apply all the concepts you have learned to design, test, and evaluate a trading strategy. You will:

Deploy a short strangle strategy.

Backtest it on historical data.

Analyse the strategy’s performance using key metrics.

Deliverable: A complete backtest notebook with performance analysis.

Capstone Project 2: Risk Management of an Options Volatility Position
This project challenges you to tackle risk management in a practical setting. You will be presented with a problem statement related to managing the risks of an options volatility position. Along with this, you will receive a solution template to guide your work and a model solution for reference.
Deliverable: A risk management notebook and a completed template with your solution.

about author

QuantInsti®
QuantInsti®
QuantInsti is the world's leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.
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learning track 3

This course is a part of the Learning Track: Quantitative Trading in Futures and Options Markets

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