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Bayesian Analysis of Realized Matrix-Exponential GARCH Models
Asai, M. / McAleer, M.J.
bayesian statistics / garchResource overview

Bayesian Analysis of Realized Matrix-Exponential GARCH Models

Paper2018

by Asai, M., McAleer, M.J.

Tags
bayesian statisticsgarchstatistical inferencetime seriesvolatility forecasting

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Understand this paper

Suggested foundations for volatility models and surfaces and probability and statistical reasoning. Specialist prerequisites vary by paper.

Before you read

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

    Follow diffusion-based models and their assumptions.

  2. ARCH and GARCH

    Separate conditional volatility from observed returns.

  3. Random Variables and Distributions

    Read the distributions and expectations used in probabilistic arguments.

  4. Conditional Expectation

    Interpret expectations after information is observed.

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. Confidence Intervals and Hypothesis Testing

    Assess uncertainty rather than treating an estimate as a fact.

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