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Estimate nothing

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  • M. Duembgen
  • L. C. G. Rogers

Abstract

In the econometrics of financial time series, it is customary to take some parametric model for the data, and then estimate the parameters from historical data. This approach suffers from several problems. Firstly, how is estimation error to be quantified, and then taken into account when making statements about the future behaviour of the observed time series? Secondly, decisions may be taken today committing to future actions over some quite long horizon, as in the trading of derivatives; if the model is re-estimated at some intermediate time, our earlier decisions would need to be revised - but the derivative has already been traded at the earlier price. Thirdly, the exact form of the parametric model to be used is generally taken as given at the outset; other competitor models might possibly work better in some circumstances, but the methodology does not allow them to be factored into the inference. What we propose here is a very simple (Bayesian) alternative approach to inference and action in financial econometrics which deals decisively with all these issues. The key feature is that nothing is being estimated.

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  • M. Duembgen & L. C. G. Rogers, 2014. "Estimate nothing," Papers 1401.5666, arXiv.org.
    • Handle: RePEc:arx:papers:1401.5666
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    1. Okhrin, Ostap & Xu, Ya Fei, 2017. "A comparison study of pricing credit default swap index tranches with convex combination of copulae," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 193-217.

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