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A Coupled Component GARCH Model for Intraday and Overnight Volatility
Linton, O. / Wu, J.
garch / statisticsResource overview

A Coupled Component GARCH Model for Intraday and Overnight Volatility

Paper2018

by Linton, O., Wu, J.

Tags
garchstatisticstime seriesvolatility forecasting

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Suggested foundations for volatility models and surfaces and financial time series. 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. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  4. Linear Regression

    Read the fitted relationship and its residuals.

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. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

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

Explore Advanced Derivatives and Exotic Options Explore Econometrics and Financial Time Series

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