Forecasting realized volatility of oil futures market: A new insight

In this study we propose several new variables, such as continuous realized semi‐variance and signed jump variations including jump tests, and construct a new heterogeneous autoregressive model for realized volatility models to investigate the impacts that those new variables have on forecasting oil price volatility. In‐sample results indicate that past negative returns have greater effects on future volatility than that of positive returns, and our new signed jump variations have a significantly negative influence on the future volatility. Out‐of‐sample empirical results with several robust checks demonstrate that our proposed models can not only obtain better performance in forecasting volatility but also garner larger economic values than can the existing models discussed in this paper.     

New product forecasting

Forecasting new-product performance has been called ‘one of the most difficult and critical management tasks’. It has attracted considerable attention because of the magnitude of the resources devoted to product development and because of the sizeable risks involved in making the go–no-go decisions. In comparison with forecasting sales for established products, there is no sales history, or more generally, the company has no product specific experience related to consumer acceptance, trade support and competitive reactions. This article first presents a review of new product forecasting techniques with an emphasis given to the more recent developments in forecasting models. Then, forecasting procedures are assessed by discussing their benefits and their costs. The third part of the article discusses trends in new product forecasting.     

Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

Forecasting of electricity price through a functional prediction of sale and purchase curves

This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy.     

Forecasting the price evolution of new electronic products

This study tests the hypothesis that prices in new markets change in a pattern similar to and related to the product life cycle. Three stages of price decline have been identified in electronic product markets and can be associated with the introduction, take-off and growth, and maturity stages of the product life cycle. The study integrates the methodologies of the product life cycle concept and experience curve theory and analyses price and product behaviour for radios, monochrome (black and white) televisions, colour televisions and video cassette recorders. The empirical evidence supports the hypothesis and suggests, as a general rule, that a better understanding of a product's life cycle and the factors that affect it can aid in the development of price forecasts. In fact, the marriage of experience curve theory and the product life cycle concept provides a methodology for quantifying the impact of influential factors and gives the analyst the ability to bound the uncertainty around price forecasts, ultimately leading to better strategic decisions, today, for tomorrow's markets.