What If Two Different Interest Rates Datasets Allow for Describing the Same Financial Product?

Valeria D’Amato (), Antonio Díaz (), Emilia Di Lorenzo (), Eliseo Navarro () and Marilena Sibillo ()
Additional contact information
Valeria D’Amato: University of Salerno, Department of Economics and Statistics, Campus Universitario
Antonio Díaz: Universidad de Castilla-La Mancha, Department of Economics and Finance
Emilia Di Lorenzo: University of Naples Federico II, Department of Economic and Statistical Sciences, via Cintia
Eliseo Navarro: Universidad de Alcalá, Departamento de Economía y Dirección de Empresa
Marilena Sibillo: University of Salerno, Department of Economics and Statistics, Campus Universitario

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 289-293 from Springer

Abstract: Abstract The chance to choose among more than one dataset for representing and describing the movements in the financial market of the same financial entity has noteworthy effects on the practical quantifications. The case we consider in the paper concerns two datasets, different and deemed to be equivalent between them, referred to risk free interest rates. In light of the volatility term structure discrepancies between the two databases and of some closed formulas for stochastically describing the behavior of the financial valuation discrepancies by means of the Vasicek interest rate process, we show two relevant practical evidences. The application concerns the pricing of two derivative cases. The aim is to quantify how much the use of one dataset rather than the other impacts on the final result.

Keywords: Yield curve estimation; Volatility term structure; Vasicek process; Derivative pricing (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_52

Ordering information: This item can be ordered from
http://www.springer.com/9783319898247

DOI: 10.1007/978-3-319-89824-7_52

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().