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Introduction to Volga in Option Trading

Posted on February 7, 2024February 14, 2024 by n01a

Introduction to Volga in Option Trading

Introduction to Volga in Option Trading

Introduction to Volga in Option Trading of an option to implied volatility shifts is determined by the Volga of the option. High Volga options amplify exposure to implied volatility changes, turning even modest changes in volatility into outsized gains when directionally positioned correctly.

Unlike Vega, which only increases or decreases linearly with implied volatility, Volga increases or decreases at an accelerating, nonlinear rate. This reflects the convexity of the relationship between volatility and price.

Volga differs by strike, peaking for at-the-money options and declining with increasing out-of-the-money (OTM) strikes. In addition, Volga varies by expiration date due to time decay. Strike diversification helps curb sensitivity concentrations and volatility skew exposure when constructing Volga-driven strategies.

Exploring Volga: The Hidden Gem of Options Trading Strategies

Specific market conditions or events can dramatically influence Volga in both short- and long-term horizons. For example, quarterly earnings announcements often cause sharp overnight implied volatility spikes or drops due to a sudden change in expectations, directly impacting Volga for all strikes. Similarly, significant economic data releases or statements from Federal Reserve officials can rapidly change interest rate policy expectations, driving volatility shifts and influencing Volga in rates-sensitive markets.

Using a hedging technique called Vanna-Volga (or simply Volga) allows traders to infer an implied volatility smile from three different market quotes for a given maturity. The procedure involves adding the appropriate hedging costs to Black-Scholes values and converting the resulting smile-consistent prices into an option’s vega using the formula below. For knockout options the Vanna term is usually multiplied by some factor smaller than one (often a function of the no-touch probability of the barrier) to create the desired smile-consistent Black-Scholes value.

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More About Elton

Elton Gastrell is a dedicated software engineer and technical lead at No1A. He has been working at the company for the past five years, leading the development of various products and solutions. He is a highly skilled programmer with expertise in C/C++, Python, JavaScript, Java, and various database technologies.

Elton has a passion for developing innovative software solutions that help streamline processes and increase efficiency. He enjoys collaborating with other engineers to solve complex problems while keeping an eye on trends in the industry. Elton’s work has been instrumental in driving product success across different departments within No1A.

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