This paper studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time dependent conditional variances. This TVP model is flexible to accommodate a wide variety of timevariation patterns but requires effective shrinkage...

The aim of this paper is to construct a forecasting model oriented on predicting basic macroeconomic variables, namely: the GDP growth rate, the unemployment rate, and the consumer price inflation. In order to select the set of the best regressors, Bayesian Averaging of Classical Estimators...

Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...

Financial markets have experienced several negative sigma events in recent years; these eventsoccur with much more regularity than current risk models can predict. There is no guarantee thatthe training set's data generating process will be the same in the test set in finance. Mathematicalmodels...

We explore in this paper the use of deep signature models to predict equity financial time series returns. First, we use signature transformations to model the underlying shape of the input equity returns; further assuming the underlying shape remains the same, we predict future values based on...

Abstract. This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger...

In this paper we consider the problem of interpreting the signs of the estimated coefficients in multivariate time series regressions where the regressors are correlated. Using a continuous time model, we argue that focussing on the signs of individual coefficients in such regressions could be...

The primary objective of the paper is to forecast the beta values of companies listed on Sensex, Bombay Stock Exchange (BSE). The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies. To reach out the predefined objectives of the research, Auto...

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

The fuzzy transform (F-transform), introduced by I. Perfilieva, is a powerful tool for the construction of fuzzy approximation models; it is based on generalized fuzzy partitions and it is obtained by minimizing a quadratic (L₂-norm) functional. In this paper we describe an analogous...