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Paper / 2024SMQ / 145376634120650
Stochastic Path-Dependent Volatility Models for Price-Storage Dynamics in Natural Gas Markets and Discrete-Time Swing Option Pricing
Antony Ware / Jinniao Qiu / Yang Yang
deep learning / derivativesResource overview

Stochastic Path-Dependent Volatility Models for Price-Storage Dynamics in Natural Gas Markets and Discrete-Time Swing Option Pricing

Paper2024

by Antony Ware, Jinniao Qiu, Yang Yang

Tags
deep learningderivativesmodel calibrationnumerical methodsoption pricingstochastic differential equations

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Understand this paper

Suggested foundations for options and derivatives pricing and volatility models and surfaces. Specialist prerequisites vary by paper.

Before you read

  1. The Binomial Model

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

  2. Market Model and Risk-Neutral Valuation

    Distinguish pricing probabilities from physical forecasts.

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

    Follow diffusion-based models and their assumptions.

  4. ARCH and GARCH

    Separate conditional volatility from observed returns.

Go further

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

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

  2. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

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

    Interpret prices across strikes and expiries.

Explore a related reading path

Lesson links open public previews. Access to full lessons depends on the topic and your membership.

Explore Derivatives Pricing and Computational Finance Explore Advanced Derivatives and Exotic Options

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