Alpha Exchange Podcast — Episode Summary

Guest: John Marshall, Head of Derivatives Research, Goldman Sachs
Host: Dean Curnutt
Date: June 17, 2025

Overview

In this episode of Alpha Exchange, host Dean Curnutt speaks with John Marshall, Head of Derivatives Research at Goldman Sachs, about the evolving landscape of derivatives markets, especially as it relates to the rapid growth of risk-managed and income-oriented ETFs, changing client priorities, and the innovative application of company-specific data to option pricing. The discussion focuses on the interplay between macro and micro trends, the changing nature of volatility and risk premiums, and "asymmetry alpha" — an approach to uncovering actionable edge in options markets through event-driven and fundamentally grounded analysis.

Key Discussion Points & Insights

1. Background and Shifting Market Correlations

• Marshall’s Career Origins: Started in tech research at Goldman around the end of the tech bubble, observing first-hand the interconnectedness of tech stocks and how market events often ripple across names.

• "It really showed me how volatile markets can be and gave me a quick dose of understanding how little I knew..."
  — John Marshall [03:32]

• Comparing Past and Present Correlation in Tech:

  • Earlier (late '90s/early 2000s), tech stocks showed high correlation, with earnings news from one impacting others.
  • Today, significant dispersion prevails, with idiosyncratic moves in giants like Nvidia and Apple largely decoupled from their peers, driven in part by information disclosure practices and innovation cycles (e.g., AI leadership consolidating in single firms).

• "We had not only that focus of information being released on events, but a lot of themes... that had winners and losers."
  — John Marshall [06:37]

2. Client Demands and the Evolution of Option Selling Strategies

• Cycles of Demand:

  • In growth periods, clients focus on single-stock catalysts and deploying capital around earnings, buybacks, M&A, etc.
  • In low-rate, low-yield periods (2013–2018), a surge in put-selling and volatility-harvesting products (VIX ETNs, etc.) emerged, often using leverage.

• Shift to Unlevered, Income-Oriented ETFs:

  • The rise of covered-call and systematic option-selling ETFs/mutual funds has fundamentally changed market supply/demand, with unlevered, buy-and-hold funds providing a more persistent source of option supply.
  • These funds have proven more stable than historical, leveraged strategies, impacting both realized volatility and available risk premium to harvest for investors.

• "The bulk of the... AUM is selling calls as part of their strategy... We have not seen a significant pullback... it shows the resilience of those types of funds even in volatile macro environments."
  — John Marshall [16:10]

3. Volatility and Skew Risk Premium: Structural Changes

• Compression of Volatility Risk Premium (VRP):

  • Increased participation in systematic, unlevered option selling (especially covered calls) has compressed the VRP; less reversal risk vs. leveraged strategies of past cycles.
  • The "act of delta-hedging" by market makers further suppresses realized volatility—keeping the implied-vol minus realized-vol spread alive but reduced.

• "I no longer spend a huge amount of time trying to develop strategies... about harvesting the volatility risk premium, because I don't know how much longer that's going to be available."
  — John Marshall [20:38]

• Skew Premium:

  • Contrary to common belief, empirical analysis shows the realized and implied distributions of returns have similar skew/kurtosis, suggesting less excess premium in skew than is often assumed.

• "...found that the realized distribution has significantly fatter tails, just as the implied distribution prices in. So I'm less convinced... there's a significant skewness to be harvested..."
  — John Marshall [25:54]

4. Applying Macro and Micro Data to Option Pricing

• Framework for Asymmetry:
  • Developed models (e.g., GS EQ Move) to estimate probabilities of significant up/down moves using variables like ISM new orders, capacity utilization, free cash flow yield, and return on equity.
  • Analyzing divergences between model-implied and option market-implied probabilities reveals trading opportunities, particularly in environments where market is "mispricing" event risk.
• "When you compare these to option prices over time... big gaps... [can] lead to opportunities—to buy or sell calls or... puts."
  — John Marshall [27:50]

Notable Data Points & Insights

• Elevated ROE increases upside probabilities, while low free cash flow yield raises downside risks.
• Events like COVID-19 policy responses revealed model limitations and importance of being aware of "unknown unknowns" and structural breaks.
• Increasing use of AI/ML to improve model robustness and avoid overfitting/outlier sensitivity.

5. Asymmetry Alpha & Event-Driven Trades

• Least Crowded Edge:

  • Focus on "asymmetry alpha" — trading based on differences in distribution of up/down tail risk, especially across single stocks.
  • Model incorporates variables such as free cash flow, dividend yield, ETF ownership, upcoming corporate events.

• Catalyst-Driven Option Buying:

  • Contrary to intuition, buying at-the-money calls before earnings or analyst days can be profitable, likely because more of a stock's positive return is realized during events.

• "Buying calls ahead of single stock events tends to be profitable or has in history... by strategically buying calls ahead of particular events on single stocks, this gives you an advantage..."
  — John Marshall [39:39]

  • At-the-money calls outperform out-of-the-money calls; less crowding and more direct exposure to positive event-driven returns.
  • Earnings day returns tend to be positive, and excluding stocks around earnings from portfolios harms long-term performance more than it helps by reducing idiosyncratic volatility.

6. Integrating Fundamental Data into Single-Stock Option Valuation

• Free Cash Flow Yield as a Core Variable:
  • Crucial for capturing downside risk; high-yield companies can de-lever, offering natural protection.
• Event Classification:
  • Separate models for earnings, analyst days, and "no event" periods.
  • Firms with "event-heavy" volatility should have different options pricing approaches for event and non-event months.
• Positioning/Crowding:
  • Retail and hedge fund activity levels (e.g., call volume spikes) forecast upside tail moves but are less relevant for downside risk.
  • Most-shorted baskets provide insight into short-squeeze-driven volatility.

7. Technology’s Role in Research

• LLMs and Programming:
  • LLMs have democratized quantitative research—industry veterans can code models themselves, rapidly iterate, and leverage domain expertise.
  • This shift vastly improves both speed and breadth of research.
• "What the large language models have enabled me to do is become a programmer... you are going to be able to do amazing things with the knowledge that's in your head..."
  — John Marshall [55:58]

8. Cross-Asset Applications and Limitations

• Equities vs. Credit:
  • The liquidity and price transparency in equities make it easier to trust that standard relationships are already well-arbitraged.
  • In OTC or illiquid markets (e.g., credit), fundamentals may play a bigger role, but less certainty that the ground work has been "done," so fundamental overlays may be less actionable.

Memorable Quotes & Moments

• On Market Certainty:
  "Doubt is an unpleasant condition, but certainty is absurd." — Dean Curnutt [05:17]
• On Volatility:
  "There are unexpected things... even the most experienced strategists don't see coming. You have to reserve something in your view for those unknown unknowns." — John Marshall [03:32]
• On Crowding and Model Overfit:
  "No free lunch is probably the most intellectually honest starting point. 'Free lunches' or the perception of them create crowding in a hurry and it undoes it." — Dean Curnutt [36:08]
• On LLMs in Finance:
  "People in finance that have decades of experience are now going to be able to use some of the tools that specialists may program..." — John Marshall [55:58]

Timestamps for Key Segments

• Background & Career Trajectory: 03:32
• Changes in Tech Sector Correlations: 06:37–09:05
• Client Demand Evolution: 11:00–13:39
• Growth & Trends in Options-Based ETFs: 15:01–16:10
• Volatility Risk Premium Discussion: 19:17–24:43
• Skew Risk Premium Analysis: 24:43–26:49
• Macro-Micro Option Pricing Approach: 26:49–33:21
• Model Limitations, AI/ML Use: 33:21–36:08
• Asymmetry Alpha & Event Strategies: 36:08–42:37
• Catalyst-Driven Option Performance: 39:26–44:18
• Single-Stock Option Modeling: 44:54–51:55
• Crowding and Positioning Data: 52:11–54:37
• LLMs and Data Analysis in Finance: 55:58–58:02
• Cross-Asset Considerations: 58:02–58:37

Closing

This conversation offers a rare, nuanced take on both the macro shifts in derivatives market structure and the micro innovations in single-stock option analysis. John Marshall’s insights highlight both the stability brought by new ETF flows and the persistent edge available via meticulous, data-driven study of firm-specific factors and market positioning. For any listener eager to understand how modern risk-premia, dispersion, and advanced analytics interact in today’s equity derivatives markets, this episode is essential.