High-Frequency Volatility Modelling: A Markov-Switching Autoregressive Conditional Intensity Model

36 Pages Posted: 27 May 2016 Last revised: 16 Jul 2019

See all articles by Yifan Li

Yifan Li

University of Manchester - Alliance Manchester Business School; Lancaster University - Department of Accounting and Finance

Ingmar Nolte

Lancaster University - Department of Accounting and Finance

Sandra Nolte (Lechner)

Lancaster University Management School

Date Written: July 12, 2019

Abstract

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional parameters, and show that it can be reliably estimated via the Stochastic Approximation Expectation-Maximization algorithm. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm-specific news arrivals. We propose a novel daily volatility decomposition based on the two detected volatility regimes.

Keywords: Intensity Modelling, Market Microstructure Invariance, Volatility Modelling

JEL Classification: C58, C51, C22, C41

Suggested Citation

Li, Yifan and Nolte, Ingmar and Nolte (Lechner), Sandra, High-Frequency Volatility Modelling: A Markov-Switching Autoregressive Conditional Intensity Model (July 12, 2019). Available at SSRN: https://ssrn.com/abstract=2785499 or http://dx.doi.org/10.2139/ssrn.2785499

Yifan Li (Contact Author)

University of Manchester - Alliance Manchester Business School ( email )

Booth Street West
Manchester, M15 6PB
United Kingdom

Lancaster University - Department of Accounting and Finance ( email )

The Management School
Lancaster LA1 4YX
United Kingdom

Ingmar Nolte

Lancaster University - Department of Accounting and Finance ( email )

Lancaster, Lancashire LA1 4YX
United Kingdom

Sandra Nolte (Lechner)

Lancaster University Management School ( email )

Lancaster, Lancashire LA1 4YX
United Kingdom

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