Stochastic Optimal Control of Iron Condor Portfolios for Profitability and Risk Management

ArXiv ID: 2501.12397 “View on arXiv”

Authors: Unknown

Abstract

Previous research on option strategies has primarily focused on their behavior near expiration, with limited attention to the transient value process of the portfolio. In this paper, we formulate Iron Condor portfolio optimization as a stochastic optimal control problem, examining the impact of the control process ( u(k_i, τ) ) on the portfolio’s potential profitability and risk. By assuming the underlying price process as a bounded martingale within $[“K_1, K_2”]$, we prove that the portfolio with a strike structure of $k_1 < k_2 = K_2 < S_t < k_3 = K_3 < k_4$ has a submartingale value process, which results in the optimal stopping time aligning with the expiration date $τ= T$. Moreover, we construct a data generator based on the Rough Heston model to investigate general scenarios through simulation. The results show that asymmetric, left-biased Iron Condor portfolios with $τ= T$ are optimal in SPX markets, balancing profitability and risk management. Deep out-of-the-money strategies improve profitability and success rates at the cost of introducing extreme losses, which can be alleviated by using an optimal stopping strategy. Except for the left-biased portfolios $τ$ generally falls within the range of [“50%,75%”] of total duration. In addition, we validate these findings through case studies on the actual SPX market, covering bullish, sideways, and bearish market conditions.

Keywords: Iron Condor, Stochastic Optimal Control, Rough Heston Model, Submartingale, Optimal Stopping, Options / Derivatives

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper presents advanced stochastic optimal control theory with proofs using martingales and submartingales, and employs sophisticated Rough Heston simulations. It includes both theoretical validation and empirical case studies on SPX data, demonstrating backtest-ready methodology.

  flowchart TD
    A["Research Goal<br>Stochastic Optimal Control<br>for Iron Condor Portfolios"] --> B["Modeling & Methodology"]
    
    subgraph B ["Methodology"]
        B1["Stochastic Optimal Control<br>Formulation"]
        B2["Bounded Martingale Assumption<br>Underlying Price in [K1, K2"]]
        B3["Proof of Submartingale Value Process<br>Optimal Stopping Time τ = T"]
    end
    
    B --> C["Simulation & Data Generation"]
    
    subgraph C ["Computational Processes"]
        C1["Rough Heston Model<br>Data Generator"]
        C2["Asymmetric & Left-Biased<br>Portfolio Construction"]
    end
    
    C --> D["Outcomes & Validation"]
    
    subgraph D ["Key Findings"]
        D1["Left-Biased Portfolios<br>τ = T are Optimal"]
        D2["Deep OTM Strategies<br>High Profit / Extreme Loss Risk"]
        D3["Optimal Stopping Range<br>τ ∈ [50%, 75%"] of Duration]
        D4["SPX Market Validation<br>Bullish, Sideways, Bearish"]
    end