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Estimating traffic demand risk - a multiscale analysis

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Abstract

This paper proposes a novel method for estimating the traffic demand risk associated with transportation. Using mathematical properties of wavelets, we develop a statistical measure of traffic demand sensitivity with respect to GDP. This measure can be adapted in a flexible way to capture risk levels relevant for different investment horizons. We demonstrate the time-scale decomposition of risk with Swedish traffic demand data for 1950-2005. In general, rail transports show a stronger co-movement with GDP than road transports. Moreover, we examine the volatility exhibited by traffic demand. Our findings suggest that rail investments are more risky than road investments. Since the findings can be used for optimal investment timing and for choice among public investment alternatives, they are deemed important for public policy in general.

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File URL: http://www.transportportal.se/SWoPEc/CTS2012-14.pdf
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Bibliographic Info

Paper provided by CTS - Centre for Transport Studies Stockholm (KTH and VTI) in its series Working papers in Transport Economics with number 2012:14.

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Length: 29 pages
Date of creation: 04 May 2012
Date of revision:
Handle: RePEc:hhs:ctswps:2012_014

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Postal: Centrum för Transportstudier (CTS), Teknikringen 10, 100 44 Stockholm, Sweden
Web page: http://www.cts.kth.se/

Related research

Keywords: Traffic demand; GDP growth; Wavelet; Risk; Real options; Discount rate;

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  1. Gallegati Marco & Gallegati Mauro, 2007. "Wavelet Variance Analysis of Output in G-7 Countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(3), pages 1-25, September.
  2. Arrow, Kenneth J & Lind, Robert C, 1970. "Uncertainty and the Evaluation of Public Investment Decisions," American Economic Review, American Economic Association, vol. 60(3), pages 364-78, June.
  3. Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon, 2001. "Scaling properties of foreign exchange volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(1), pages 249-266.
  4. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, 09.
  5. Schafer, Andreas, 1998. "The global demand for motorized mobility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(6), pages 455-477, August.
  6. Andersson, Fredrik N G & Elger, Thomas, 2007. "Freight Transportation Activity, Business Cycles and Trend Growth," Working Papers 2007:15, Lund University, Department of Economics.
  7. Lahiri, Kajal & Yao, Vincent Wenxiong, 2006. "Economic indicators for the US transportation sector," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(10), pages 872-887, December.
  8. Ramanathan, R., 2001. "The long-run behaviour of transport performance in India: a cointegration approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(4), pages 309-320, May.
  9. Tapio, Petri, 2005. "Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001," Transport Policy, Elsevier, vol. 12(2), pages 137-151, March.
  10. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2005. "Multiscale systematic risk," Journal of International Money and Finance, Elsevier, vol. 24(1), pages 55-70, February.
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Cited by:
  1. Hultkrantz, Lars & Krüger, Niclas & Mantalos , Panagiotis, 2012. "Risk-adjusted long term social rates of discount for transportation infrastructure investment," Working Papers 2012:14, Örebro University, School of Business.

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