The prediction of volatility is of primary importance for business applications in risk management, asset allocation and pricing of derivative instruments. This paper proposes a novel measurement model which takes into consideration the possibly time-varying interaction of realized volatility...

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk,...

This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and...

We propose a new spatio--temporal model with time--varying spatial weighting matrices. The filtering procedure of the time--varying unknown parameters is performed using the information contained in the score of the conditional distribution of the observables. We provide conditions for the...

We present a new modelling framework for the bi-variate hidden Markov model. The proposed specification is composed by five latent Markovian chains which drive the evolution of the parameters of a bi-variate Gaussian distribution. The maximum likelihood estimator is computed via an expectation...