Join Rob Navin, founder of Real Time Risk Systems and expert in options strategies, alongside Estifanos Shekour, a Lehigh MFE student focused on option design. They discuss a groundbreaking method for estimating realized volatility through P&L from constant gamma positions. The conversation highlights the challenges of measuring market volatility, the advantages of dynamic hedging strategies, and the evolution of algorithmic trading. Delve into their insights on intraday trading, the significance of gamma capture, and innovative approaches to enhancing volatility analysis.
Rob Navin discusses how the gamma capture method provides a more accurate measure of realized volatility by focusing on trade execution rates.
The podcast emphasizes the importance of understanding market dynamics through nuanced volatility assessments for effective trading strategies.
Deep dives
Introduction to Real-Time Risk Systems
Real-Time Risk Systems specializes in providing real-time derivatives analytics for large hedge funds. The company, which has been operating since 2004, addresses the complexities of building models for various derivative instruments, including equities, rates, and options. It combines real-time analytics with development capabilities to cater to the needs of multi-strategy hedge funds, which face challenges in balancing these elements. This unique approach has enabled the firm to secure funding from major hedge funds and establish a reputation in a competitive financial landscape.
Challenges in Measuring Intraday Volatility
Measuring intraday volatility presents significant challenges, particularly due to the effects of market jumps and statistical assumptions. Traditional measures that focus on close-to-close calculations often lead to inflated values when jumps occur, undermining their reliability. In contrast, the podcast discusses how a more nuanced approach called realized volatility looks at the number of market events to establish a more accurate estimate. This method recognizes that the frequency of trades can impact volatility measurements, shifting the focus from mere statistical averages to actual market behaviors.
Gamma Capture Volatility Concept
Gamma capture volatility introduces a new method for assessing volatility by focusing on the execution rates of limit orders in the market. This approach posits that trading frequency is inherently linked to volatility; higher execution rates during market movements imply greater volatility insights. By utilizing tick data and the concept of crossing two limit order barriers, traders can glean a clearer picture of market dynamics without relying on traditional measures that overlook market nuances. This development allows traders to maintain a more sophisticated understanding of volatility across different trading environments.
Practical Applications and Market Relevance
The insights shared in the podcast highlight the relevance of the gamma capture method in today’s rapid trading environment, especially with the rise of high-frequency trading strategies. The ability to measure intraday volatility accurately benefits traders engaging in multiple trades daily, and it has implications for market makers and arbitrageurs alike. As brief trading strategies gain popularity, this volatility measure can help enhance decision-making and reduce risks associated with traditional methods that may produce misleading results. Ultimately, the podcast suggests that continued exploration and backtesting of this method could yield valuable insights into effective trading strategies.
The podcast welcomes Rob Navin, Founder of Real Time Risk Systems to discuss his new method of estimated a realized volatility based on P&L from a constant gamma position. This is a deep dive quant discussion on Option strategies. Estifanos Shekour, MFE also join the podcast and share his experience on designing option strategies.
Link to Discussion Article
https://optionstree.substack.com/p/gamma-capture-realized-volatility-9b5
https://www.linkedin.com/in/robnavin/
https://www.linkedin.com/in/estifanos-shekour/
https://www.linkedin.com/in/patrick-z-08bb5b5a/
Lehigh MFE Linkedin Page
https://www.linkedin.com/company/lehigh-master-in-financial-engineering
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