Paper: arXiv 2402.14555

Authors: Moshe A. Milevsky, Thomas S. Salisbury

Abstract

This paper presents a new type of modern accumulation-based tontine, called the Riccati tontine, named after two Italians: mathematician Jacobo Riccati (b. 1676, d. 1754) and financier Lorenzo di Tonti (b. 1602, d. 1684). The Riccati tontine is yet another way of pooling and sharing longevity risk, but is different from competing designs in two key ways. The first is that in the Riccati tontine, the representative investor is expected – although not guaranteed – to receive their money back if they die, or when the tontine lapses. The second is that the underlying funds within the tontine are deliberately {\em not} indexed to the stock market. Instead, the risky assets or underlying investments are selected so that return shocks are negatively correlated with stochastic mortality, which will maximize the expected payout to survivors. This means that during a pandemic, for example, the Riccati tontine fund’s performance will be impaired relative to the market index, but will not be expected to lose money for participants. In addition to describing and explaining the rationale for this non-traditional asset allocation, the paper provides a mathematical proof that the recovery schedule that generates this financial outcome satisfies a first-order ODE that is quadratic in the unknown function, which (yes) is known as a Riccati equation.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 3.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces a novel structural design for modern tontines using non-indexed asset allocation optimized against stochastic mortality, supported by a formal Riccati ODE proof. However, it leans heavily on analytical formulation with limited empirical backtesting or historical market data validation.

Research Flowchart

  flowchart TD
    A[Goal: Design Tontine with Principal Recovery & Max Survivor Payouts] --> B[Inputs: Stochastic Mortality Rates & Non-Indexed Risky Asset Returns]
    B --> C[Method: Asset Allocation with Negative Correlation to Mortality Shocks]
    C --> D[Math Model: Formulate Recovery Schedule as First-Order Quadratic ODE]
    D --> E[Solve Riccati Equation for Optimal Capital Allocation]
    E --> F[Outcomes: Expected Principal Return on Death/Lapse & Resilience to Pandemics]