This paper introduces a general class of combined neural network-GARCH models suitable to financial time series analysis. We put special emphasis on designing a full model-building cycle for this class of models that includes all stages of econometric modelling (specification, estimation and...

We investigate predictive abilities of nonlinear models for stock returns when density forecasts are evaluated and compared instead of the conditional mean point forecasts. The aim of this paper is to show whether the in-sample evidence of strong nonlinearity in mean may be exploited for...

Even with the presence of numerous institutional players in the market, there exist a noticeable number of cash group shares which are hardly transacted. To sustain the growth of investors in the market, there is need for assuring easy and quick liquidity to the securities. To serve the same...

This paper proposes a predictive maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for...

This paper evaluates short-run out-of-sample exchange rate predictability with real-time data for 15 OECD countries from 1973 to 2013. We consider the Taylor rule fundamentals model, where the variables that enter the Taylor rule are used to forecast exchange rate changes, and the Taylor rule...

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even...

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters...

In this paper the out-of-sample prediction of Value-at-Risk by means of models accounting for higher moment dynamics is studied. We consider models differing in terms of skewness and urtosis and, in particular, the GARCHDSK model, which allows for dynamic skewness and kurtosis. The issue of VaR...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag model is chosen. The results show that a sharp...

This study tested the unbiased pricing hypothesis in the copper, aluminum, nickel, and lead markets for the period October 2011 to May 2021. Wavelets and a time-varying parameter model with Bayesian priors were the primary tools. Each metal market was found to be weak market efficient, the...