✦ LIBER ✦

Confidence limits for data mining models of options prices

✍ Scribed by J.V. Healy; M. Dixon; B.J. Read; F.F. Cai

Publisher: Elsevier Science
Year: 2004
Tongue: English
Weight: 245 KB
Volume: 344
Category: Article
ISSN: 0378-4371
DOI: 10.1016/j.physa.2004.06.112

No coin nor oath required. For personal study only.

✦ Synopsis

Non-parametric methods such as artificial neural nets can successfully model prices of financial options, out-performing the Black-Scholes analytic model (Eur. Phys. J. B 27 (2002) 219). However, the accuracy of such approaches is usually expressed only by a global fitting/ error measure. This paper describes a robust method for determining prediction intervals for models derived by non-linear regression. We have demonstrated it by application to a standard synthetic example (29th Annual Conference of the IEEE Industrial Electronics Society, Special Session on Intelligent Systems, pp. 1926Systems, pp. -1931)). The method is used here to obtain prediction intervals for option prices using market data for LIFFE ''ESX'' FTSE 100 index options (http://www.liffe.com/liffedata/contracts/month_onmonth.xls). We avoid special neural net architectures and use standard regression procedures to determine local error bars. The method is appropriate for target data with non constant variance (or volatility).

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