Ganna Marchenko: Clustering non-stationary financial time series data
12 February 2019
IDSIA meeting room @10:00
Non-stationarity in data can arise due to the changes in various unobserved influencing factors. One way to account for non-stationarity is to employ models with time-varying parameters. Such models can be parametric or non-parametric depending on underlying assumptions they impose. The presented non-stationary approach identifies the optimal number of hidden regimes in data and the (a priori unknown) regime-switching dynamic without employing restrictive parametric assumption about the data-generating process. Within the regime, data is modelled using Maximum Entropy density, where the optimal number of density parameters is inferred via Lasso regularization technique. The resulting non-parametric methodology provides simultaneously the simplest and the least biased description of the data.