This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution...

This paper presents an exchange rate forecasting model which combines the multi-state Markov-switching model with smoothing techniques. The model outperforms a random walk at short horizons and its superior forecastability appears to be robust over different sample spans. Our finding hinges on...

The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...

This study contributes to the relevant literature by providing an explanation on the reason and the economic conditions under which the spread proves to be such a powerful predictor of economic activity. For over two decades numerous studies have provided evidence on the predictive ability of...

Bitcoin (BTC), as the dominant cryptocurrency, has attracted tremendous attention lately due to its excessive volatility. This paper proposes the time-varying transition probability Markov-switching GARCH (TV-MSGARCH) models incorporated BTC logarithmic daily trading volume or Google daily...

This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In the empirical application, we forecast U.S. business cycle turning points with state-level employment data. We find that forecasts...

In this paper we suggest a novel inhomogeneous Markov switching approach for probabilistic forecasting of electricity load of industrial companies, for which the load switches at random times between a production and a standby regime. The model we propose describes the transitions between the...

We show how to construct a composite Hidden Markov Model (HMM) to calculate real-time recession probability, using the jubilee and ldhmm packages in R. The input data is the unemployment rate (UNRATE) which is released monthly by the U.S. government. There are two sub-models: The one-year...