Effective Forecasting Methods Research Articles

The Realised–Implied Volatility Relationship: Recent Empirical Evidence from FTSE‐100 Stocks

ABSTRACTThis paper examines the long‐run relationship between implied and realised volatility for a sample of 16 FTSE‐100 stocks. We find strong evidence of long‐memory, fractional integration in equity volatility and show that this long‐memory characteristic is not an outcome of structural breaks experienced during the sample period. Fractional cointegration between the implied and realised volatility is shown using recently developed rank cointegration tests by Robinson and Yajima (2002). The predictive ability of individual equity options is also examined and composite implied volatility estimates are shown to contain information on future idiosyncratic or stock‐specific risk that is not captured using popular statistical approaches. Implied volatilities on individual UK equities are thus closely related to realised volatility and are an effective forecasting method particularly over medium forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.

Variations in the lower level of the PBL associated with the Yellow Sea fog-new observations by L-band radar

The Chinese east coastal areas and marginal seas are foggy regions. The development of effective forecasting methods rests upon a comprehensive knowledge of the fog phenomena. This study provides new observations associated with the sea fogs over the northwestern Yellow Sea by means of L-band radar soundings with a high vertical resolution of 30 m. The monthly temperature lapse rate, the Richardson Numbers, and the humidity show obvious seasonal variations in the lower level of the planetary boundary layer (PBL) that are related to the onset, peak and end of the Yellow Sea fog season. The typical pattern of stratification for the sea fog season in the northwestern Yellow Sea is that a stable layer of about 400 m thick caps a 150 m conditionally unstable layer. Besides, the differences between fogs and stratus clouds in terms of humidity, turbulence and temperature are analyzed, which is of significance for sea fog forecast and detection by satellites. The thickness of the sea fogs varies in different stages of the fog season, and is associated with the temperature inversion. The numerical simulation proves that the seasonal variations obtained by the radar well represent the situations over the Yellow Sea.

Managing lumpy demand for aircraft spare parts

This paper deals with effective forecasting methods for typically lumpy demand for aircraft spare parts, and analyzes the behavior of forecasting techniques when dealing with lumpy demand. Twenty forecasting techniques are considered and tested and historical data from Alitalia are used to analyze and compare their performance. The results demonstrate that item lumpiness is the dominant parameter and show that demand forecasting for lumpy items is a complex problem; results from previous studies are not very accurate. The best approaches are found to be weighted moving averages, the Croston method, and exponentially weighted moving average models.