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Paper / 2022SMQ / 177629932797480
Information Geometry of Risks and Returns
Andrei N. Soklakov
mathematical finance / optimizationResource overview

Information Geometry of Risks and Returns

Paper2022

by Andrei N. Soklakov

Tags
mathematical financeoptimizationrisk management

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Suggested foundations for risk measurement and stress and options and derivatives pricing. Specialist prerequisites vary by paper.

Before you read

  1. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  2. Expected Shortfall and Other Tail Risk Measures

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

  3. The Binomial Model

    Build replication and risk-neutral probabilities in a tractable model.

  4. Market Model and Risk-Neutral Valuation

    Distinguish pricing probabilities from physical forecasts.

Go further

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

    Compare risk estimators and their assumptions.

  2. Backtesting VaR and ES: Coverage Tests and Model Validation

    Check forecasts against realised losses.

  3. The Black-Scholes PDE, Formula and Implied Volatility

    Connect the pricing equation, closed-form value and implied volatility.

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