Comparing market phase features for cryptocurrency and benchmark stock index using HMM and HSMM filtering

A desirable aspect of financial time series analysis is that of successfully detecting (in real time) market phases. In this paper we implement HMMs and HSMMs with normal state-dependent distributions to Bitcoin/USD price dynamics, and also compare this with S&P 500 price dynamics, the latter being a benchmark in traditional stock market behaviour which most literature resorts to. Furthermore, we test our models’ adequacy at detecting bullish and bearish regimes by devising mock investment strategies on our models and assessing how profitable they are with unseen data in comparison to a buy-and-hold approach. We ultimately show that while our modelling approach yields positive results in both Bitcoin/USD and S&P 500, and both are best modelled by four-state HSMMs, Bitcoin/USD so far shows different regime volatility and persistence patterns to the one we are used to seeing in traditional stock markets.peer-reviewe

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods