Although volatility clustering has a long history as a salient empirical regularity
characterizing high-frequency speculative prices, it was not until recently that applied …

Financial market volatility is an important input for investment, option pricing, and financial
market regulation. The emphasis of this review article is on forecasting instead of modelling; …

A complete resource for finance students, this textbook presents the most common empirical
approaches in finance in a comprehensive and well-illustrated manner that shows how …

We find support for a negative relation between conditional expected monthly return and
conditional variance of monthly return, using a GARCH‐M model modified by allowing (1) …

This paper derives the asymptotic power envelope for tests of a unit autoregressive root for
various trend specifications and stationary Gaussian autoregressive disturbances. A family …

A multivariate time series model with time varying conditional variances and covariances,
but constant conditional correlations is proposed. In a multivariate regression framework, the …

A voluminous literature has emerged for modeling the temporal dependencies in financial
market volatility using ARCH and stochastic volatility models. While most of these studies …

We provide a framework for integration of high–frequency intraday data into the
measurement, modeling, and forecasting of daily and lower frequency return volatilities and …

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test
statistics in dynamic models that jointly parameterize conditional means and conditional …

The new class of Fractionally Integrated Generalized AutoRegressive Conditionally
Heteroskedastic (FIGARCH) processes is introduced. The conditional variance of the …