smq/directoryAn evolving quant resource archive
Back to directoryResource
Paper / 2025SMQ / 110660018295854
Option pricing under non-Markovian stochastic volatility models: A deep signature approach
Jingtang Ma / Wenyuan Li / Xianglin Wu
deep learning / numerical methodsResource overview

Option pricing under non-Markovian stochastic volatility models: A deep signature approach

Paper2025

by Jingtang Ma, Wenyuan Li, Xianglin Wu

Tags
deep learningnumerical methodsoption pricingpartial differential equationsrough volatilitystochastic differential equations

Access resource

Open original source Understand this paper

Continue on SelfMadeQuant

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

Keep exploring

Related resources

More ideas from similar topics.

6

From the same author

Jingtang Ma, Wenyuan Li, Xianglin Wu

6