We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved components model with an asymmetric cyclical component. The asymmetric cycle is defined as a sine-cosine wave where the frequency of the cycle depends on past oil price observations. We show that oil price forecasts improve significantly when this asymmetry is explicitly modelled.

Load forecasting is a central integral process in the planning and operation of electric utilities. Load forecasting has become in recent years one of the major areas of research in electrical engineering. The main problem for the planning is the determination of load demand in the future. Because electrical energy cannot be stored appropriately, correct load forecasting is very essential for t...

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...

This paper presents a new application of Hidden Markov Model (HMM) as a forecasting tool for the prediction of the currency exchange rate between the US dollar and the euro. The results obtained show that the difference between price gaps which consists open, high, and low price can be selected to produce the best model parameter of Hidden Markov Model. Three model parameters based on Akaike In...

Electricity price predictions have become a major discussion on competitive market under deregulated power system. But, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. In this paper, a new forecast strategy based on the iterative neural network is proposed for Day-ahead price...

Fluctuations in crude oil price significantly impact the global economic market. A rise or a fall leads to redistribution of wealth in both oil-exporting and importing countries. Under such background, efficient and accurate predictions for crude oil price are critical for a stable economic development. However, crude oil price forecasting has been proved to be an extremely tough task, due to i...

Forecasting volatility is important to financial asset pricing because a more accurate forecast will allow for a more accurate model to price financial assets. Currently the VIX is used as a measure of volatility in the market as a whole, but a major issue with this is that it is calculated based on manually traded options on the S&P 500. Another method of forecasting volatility is that of solv...

Observed high-frequency …nancial prices can be considered as comprising two components, a true price and a market microstructure noise perturbation. It is an empirical regularity, coherent with classical market microstructure theories of price determination, that the second moment of market microstructure noise is time-varying. We study the optimal, from a …nite-sample forecast MSE standpoint, ...

Investor sentiment index based on actual trader positions is useful for forecasting S&P 500 index futures returns. We find that large speculator sentiment is a price continuation indicator, whereas large hedger sentiment is a weak contrary indicator. Small trader sentiment does not forecast returns. We show that extreme levels and the combination of extreme levels of sentiments of the two types...

This paper presents out-of-sample inflation forecasting results based on relative price variability and skewness. It is demonstrated that forecasts on long horizons of 1.5-2 years are significantly improved if the forecast equation is augmented with skewness. JEL: E17, E31, C43