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Paper / 2024SMQ / 225344972521506
Neural and Time-Series Approaches for Pricing Weather Derivatives: Performance and Regime Adaptation Using Satellite Data
Marco Hening Tallarico / Pablo Olivares
derivatives / machine learningResource overview

Neural and Time-Series Approaches for Pricing Weather Derivatives: Performance and Regime Adaptation Using Satellite Data

Paper2024

by Marco Hening Tallarico, Pablo Olivares

Tags
derivativesmachine learningregressiontime series

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Suggested foundations for financial time series and options and derivatives pricing. Specialist prerequisites vary by paper.

Before you read

  1. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  2. Linear Regression

    Read the fitted relationship and its residuals.

  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. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

  2. Regression Assumptions and Diagnostics

    Check the assumptions behind reported estimates.

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

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

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