  In this paper we propose univariate volatility models for irregularly spaced
financial time series by modifying the regularly spaced stochastic volatility
models. We also extend this approach to propose multivariate stochastic
volatility (MSV) models for multiple irregularly spaced time series by
modifying the MSV model that was used with daily data. We use these proposed
models for modeling intraday logarithmic returns from health sector stocks data
obtained from Trade and Quotes (TAQ) database at Wharton Research Data Services
(WRDS).
