We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically...

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

This paper introduces a new specification for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coefficients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for...

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...

High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialised estimators have been developed, they have had limited availability in open source software. HighFrequencyCovariance...