Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2018.1530607
Non-structural approach to implied moments extraction
Tea Šestanović
orcid.org/0000-0002-6279-6070
; Department of Quantitative Methods, Faculty of Economics, Business and Tourism, University of Split, Split, Croatia
Josip Arnerić
orcid.org/0000-0002-2901-2609
; Department of Statistics, Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia
Zdravka Aljinović
orcid.org/0000-0002-9133-0149
; Department of Quantitative Methods, Faculty of Economics, Business and Tourism, University of Split, Split, Croatia
Sažetak
Moments of future prices and returns are not observable, but it is
possible to measure them indirectly. A set of option prices with
the same maturity but with different exercise prices are used to
extract implied probability distribution of the underlying asset at
the expiration date. The aim is to obtain market expectations
from options and to investigate which non-structural model for
estimating implied probability distribution gives the best fit. Nonstructural models assume that only dynamics in prices is known.
Mixture of two log-normals (MLN), Edgeworth expansions and
Shimko’s model (representatives of parametric, semiparametric
and nonparametric approaches respectively) are compared.
Previous researches are inconclusive about the superiority of one
approach over the others. This article contributes to finding which
approach dominates. The best fit model is used to describe
moments of the implied probability distribution. The sample
covers one-year data for DAX index options. The results are
compared through models and maturities. All models give better
short-term forecasts. In pairwise comparison, MLN is superior to
other approaches according to mean squared errors and DieboldMariano test in the observed period for DAX index options.
Ključne riječi
Edgeworth expansions; implied moments; mixture of two log-normals; Shimko’s mode
Hrčak ID:
217020
URI
Datum izdavanja:
3.12.2018.
Posjeta: 921 *