The purpose of this paper is twofold. First, we evaluate the responses to the questions on inflation expectations in the World Economic Survey for sixteen inflation targeting countries. Second, we compare inflation expectation forecasts across countries by using a two-step approach that selects...

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of...

Purpose – We use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017 to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.Design/methodology/approach – We apply six...

Artificial neural networks have become increasingly popular for statistical model fitting over the last years, mainly due to increasing computational power. In this paper, an introduction to the use of artificial neural network (ANN) regression models is given. The problem of predicting the GDP...

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure topological stock market changes as well as the...

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community for their superior predictive properties including robustness to over...

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

Motivated by recurrent neural networks, this paper proposes a recurrent support vector regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR based ARMA model is compared with five...

Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon...

In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...