Residual value forecasting using asymmetric cost functions

Korbinian Dress, Stefan Lessmann and Hans-Jörg von Mettenheim

International Journal of Forecasting, 2018, vol. 34, issue 4, 551-565

Abstract: Leasing is a popular channel for marketing new cars. However, the pricing of leases is complicated because the leasing rate must embody an expectation of the car’s residual value after contract expiration. This paper develops resale price forecasting models in order to aid pricing decisions. One feature of the leasing business is that different forecast errors entail different costs. The primary objective of this paper is to identify effective ways of addressing cost asymmetry. Specifically, this paper contributes to the literature by (i) consolidating prior work in forecasting on asymmetric functions of the cost of errors; (ii) systematically evaluating previous approaches and comparing them to a new approach; and (iii) demonstrating that forecasting using asymmetric cost of error functions improves the quality of decision support in car leasing. For example, if the costs of overestimating resale prices are twice those of underestimating them, incorporating cost asymmetry into forecast model development reduces costs by about 8%.

Keywords: Asymmetric cost of error; Quantile regression; Neural networks; Ensemble learning; Automotive industry (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:34:y:2018:i:4:p:551-565

DOI: 10.1016/j.ijforecast.2018.01.008

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