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Paper / 2026SMQ / 260597649308723
Option Pricing under Stochastic Volatility and Jumps:A PIDE Framework with Empirical Evidence
Abigail Anokyewaa Mensah / Ayush Jha / Frank J. Fabozzi / Hongwei Mei / Rui Wang / Svetlozar T. Rachev
jump processes / numerical methodsResource overview

Option Pricing under Stochastic Volatility and Jumps:A PIDE Framework with Empirical Evidence

Paper2026

by Abigail Anokyewaa Mensah, Ayush Jha, Frank J. Fabozzi, Hongwei Mei, Rui Wang, Svetlozar T. Rachev

Tags
jump processesnumerical methodsoption pricingpartial differential equationsstochastic processes

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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.

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

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Abigail Anokyewaa Mensah, Ayush Jha, Frank J. Fabozzi, Hongwei Mei, Rui Wang, Svetlozar T. Rachev

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