In this paper we show that the exchange rates of some commodity exporter countries have ability to predict price spot and future contracts aluminum. This is shown with both in-sample out-of-sample analyses. The theoretical underpinning these results relies on present-value model for rate determination tight connection between prices currencies countries. We using traditional statistical metrics...

In a comment to our article entitled ‘‘Forecasting Pharmaceutical Prices for Economic Evaluations When There Is No Market: A Review’’, which was recently published in PharmacoEconomics Open [1], Holko stated that marketspecific aspects could affect efficiency of forecasting price using the price of a close substitute [2]. We thank the author for the comment on our article and agree that there a...

Electricity price forecasting is becoming more important in everyday business of power utilities. Good forecasting models can increase effectiveness of producers and buyers playing roles in electricity market. Price is also a very important element in investment planning process. This paper presents a forecasting technique to model day-ahead spot price using well known ARIMA model to analyze an...

The classical cobweb theorem is extended to include production lags and price forecasts. Price forecasting based on a longer period has a stabilizing effect on prices. Longer production lags do not necessarily lead to unstable prices; very long lags lead to cycles of constant amplitude. The classical cobweb requires elasticity of demand to be greater than that of supply; this is not necessarily...

This study combines extended Kalman filters (EKFs) and support vector machines (SVMs) to implement a fast online predictor for option prices. The EKF is used to infer latent variables and makes a prediction based on the Black-Scholes formula, while the SVM is employed to capture the nonlinear residuals between the actual option prices and the EKF predictions. Taking option data traded in Taiwan...

This paper demonstrates that “commodity currency” exchange rates have remarkably robust power in predicting future global commodity prices, both in-sample and out-of-sample. A critical element of our in-sample approach is to allow for structural breaks, endemic to empirical exchange rate models, by implementing the approach of Rossi (2005b). Aside from its practical implications, our forecastin...

Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has bee...

State-space or latent-variable models for stock prices specify a process for expected returns and expected and unexpected dividend growth, and then derive dividend yields and returns from a present value relations. They are a useful structure for understanding and interpreting forecasting relations. In this note, I connect state-space representations with their observable counterparts, and VAR/...

The purpose of present study is to investigate a nonparametric model that improves accuracy of option prices found by previous models. In this study option prices are calculated using multiple kernel Support Vector Regression with different norm values and their results are compared. L1norm multiple kernel learning Support Vector Regression (MKLSVR) has been successfully applied to option price...