Tim Bollerslev

TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.

TL;DR: An overview of some of the developments in the formulation of ARCH models and a survey of the numerous empirical applications using financial data can be found in this paper, where several suggestions for future research, including the implementation and tests of competing asset pricing theories, market microstructure models, information transmission mechanisms, dynamic hedging strategies, and pricing of derivative assets, are also discussed.

TL;DR: In this article, a multivariate time series model with time varying conditional variances and covariances but with constant conditional correlations is proposed, which is readily interpreted as an extension of the seemingly unrelated regression (SUR) model allowing for heteroskedasticity.

TL;DR: In this paper, the authors study the properties of the quasi-maximum likelihood estimator and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood is maximized but the assumption of normality is violated.

TL;DR: In this paper, a multivariate generalized autoregressive conditional heteroscedastic process is estimated for returns to bills, bonds, and stock where the expected return is proportional to the conditional convariance of each return with that of a fully diversified or market portfolio.