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Risk diversification for infinitely divisible distributions
Peng Liu / Tiantian Mao
stochastic processesResource overview

Risk diversification for infinitely divisible distributions

Paper2026

by Peng Liu, Tiantian Mao

Tags
stochastic processes

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Understand this paper

Suggested foundations for stochastic processes and pricing and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Conditional Expectation

    Understand what it means to condition on current information.

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

    Follow Brownian dynamics and the second-order term in Itô calculus.

  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.

Go further

  1. Market Model and Risk-Neutral Valuation

    Connect stochastic dynamics to no-arbitrage prices.

  2. Simulating SDEs

    Translate continuous dynamics into a numerical experiment.

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

    Compare risk estimators and their assumptions.

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Explore Derivatives Pricing and Computational Finance Explore Quantitative Risk Management

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