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Paper / 2022SMQ / 269641337755988
A Stackelberg reinsurance-investment game under $α$-maxmin mean-variance criterion and stochastic volatility
Guohui Guan / Yilun Song / Zongxia Liang
insurance / optimizationResource overview

A Stackelberg reinsurance-investment game under $α$-maxmin mean-variance criterion and stochastic volatility

Paper2022

by Guohui Guan, Yilun Song, Zongxia Liang

Tags
insuranceoptimizationstochastic processes

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Suggested foundations for volatility models and surfaces and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Stochastic Processes, Brownian Motion and Itô's Lemma

    Follow diffusion-based models and their assumptions.

  2. ARCH and GARCH

    Separate conditional volatility from observed returns.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

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  1. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

  2. Reading the Implied Vol Surface — Moneyness, Term Structure, and Quoting Conventions

    Interpret prices across strikes and expiries.

  3. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

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