This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing probability death. We employed different definitions dead coins, ranging from academic literature professional practice; alternative forecasting models, scoring models machine learning time-series-based models; horizons. found that the choice coin-death definitio...

In Structural Vector Autoregressive (SVAR) models, heteroskedasticity can be exploited to identify structural parameters statistically. In this paper, we propose to capture time variation in the second moment of structural shocks by a stochastic volatility (SV) model, assuming that their log variances follow latent AR(1) processes. Estimation is performed by Gaussian Maximum Likelihood and an e...

In this paper, finite phase semi-Markov processes are introduced. By introducing variables and a simple transformation, every finite phase semi-Markov process can be transformed to a finite Markov chain which is called its associated Markov chain. A consequence of this is that every phase semi-Markovian switching system may be equivalently expressed as its associated Markovian switching system....

Continuous time modified Cox-Ingersoll-Ross (1985) stochastic model is employed, combining with Hamilton (1989) type Markov regime switching framework, to study daily foreign exchange rates, where all parameter values depend on the value of a continuous time Markov chain. The Expectation-Maximization algorithm is extended, generalized, applied to a more general class of regime switching models ...

We analyze the differential influence of Mexican oil price, exchange rate and S&P 500 Index on Stock Exchange: S&P/BMV IPC ESG Tilted (sustainable stock market index), (General index) in two different regimes. First, we estimate conditional volatility series using a univariate GARCH model under t-Student distribution. Second, Markov Switching Vector Autoregressive is developed. The evid...

This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estima...

In this study, two-state Markov switching count data models are proposed as an alternative to zero-inflated models, in order to account for preponderance of zeros typically observed in accident frequency data. Similar to zero-inflated models, two-state Markov switching models assume an existence of two states of roadway safety. One of the states is a zero-accident state, which is safe. The othe...