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Paper / 2025SMQ / 264725289814433
Analysing Models for Volatility Clustering with Subordinated Processes: VGSA and Beyond
Sourojyoti Barick / Sudip Ratan Chandra
jump processes / monte carlo simulationResource overview

Analysing Models for Volatility Clustering with Subordinated Processes: VGSA and Beyond

Paper2025

by Sourojyoti Barick, Sudip Ratan Chandra

Tags
jump processesmonte carlo simulationstochastic processes

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Suggested foundations for volatility models and surfaces and stochastic processes and pricing. Specialist prerequisites vary by paper.

Before you read

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

    Follow Brownian dynamics and the second-order term in Itô calculus.

  2. ARCH and GARCH

    Separate conditional volatility from observed returns.

  3. Conditional Expectation

    Understand what it means to condition on current information.

Go further

  1. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

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

    Interpret prices across strikes and expiries.

  3. Market Model and Risk-Neutral Valuation

    Connect stochastic dynamics to no-arbitrage prices.

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