Purpose: The aim of our paper is twofold. First, we examine the predictive ability of log bookmarket, dividend-price, earnings-price and dividend-earnings ratios on the most recent data set of the strongest securities in the UK economy; unlike the majority of the studies in this data set, our...

Enhanced machine learning methods provide an encouraging alternative to forecast asset prices by extending or generalizing the possible model specifications compared to conventional linear regression methods. Even if enhanced methods of machine learning in the literature often lead to better...

The appearance of negative bond yields presents significant challenges for the fixed income markets, which mainly concern related forecasting models. The Nelson-Siegel-Svensson model (NSS) is one of the models that is most frequently used by central banks to estimate the term structure of...

This paper attempts to provide an economic interpretation of the factors that drive the movements of interest rates of bonds of different maturities in a continuous-time no-arbitrage term structure model for Chile. The dynamics of yields in the model are explained by two latent factors, namely...

Usage of a random effects panel logit model have shown in this paper that the high propensity to pay dividends by the state-controlled companies quoted on the Warsaw Stock Exchange over the last years was not a result of the tunneling effect but was the maturity effect. The state-controlled...

Motivated by the need to avoid possible parameter bias associated with previous works, we examined the impacts of private sector credit on economic growth in Nigeria using the Gregory and Hansen (1996) cointegration test that accounted for structural breaks and endogeneity problems. The method...

The paper deals with maritime risk, which we consider important, no doubt, for ship-owners acting in volatile markets. Traditionally, risk is measured by "standard deviation". Other risk measures like "excess kurtosis", "excess skewness", "long-term dependence" and the "catastrophe propensity"...

In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from...

The research investigated the impact of macroeconomic variables on the volatility of the commodity futures market in India (together with oil futures, agricultural commodity futures and metal futures). The monetary policies, financial market information and economic environments are determined...

The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The...