Investment Attraction and Tax Reform: a Stochastic Model

Vadim Arkin, Alexander Slastnikov () and Svetlana V. Arkina ()
Additional contact information
Svetlana V. Arkina: University Paris I

A chapter in Operations Research Proceedings 2005, 2006, pp 501-506 from Springer

Abstract: Summary We study a model of the behavior of a potential investor (under risk and uncertainty) who wishes to invest in a project of creating a new enterprise and chooses an investment time (timing problem). This model takes the tax environment exhaustively into account. An optimal rule of investment and its dependence on parameters of tax system are obtained. Investigation is based on solving an optimal stopping problem for two-dimensional geometric Brownian motion. We apply Feinmann-Kac formula and variational inequalities as basic methods for deriving the closed-form formulas for optimal investment time and expected tax revenues from future enterprise into budgets of different levels. Based on those formulas, an analysis of the Russian reform of corporate profit taxation (2002) is undertaken, as well as of the tax cuts in VAT (2004) and Unified Social Tax (UST) Rates (2005).

Keywords: Geometric Brownian Motion; Federal Budget; Regional Budget; Investment Threshold; Optimal Investment Time (search for similar items in EconPapers)
Date: 2006
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:oprchp:978-3-540-32539-0_79

Ordering information: This item can be ordered from
http://www.springer.com/9783540325390

DOI: 10.1007/3-540-32539-5_79

Access Statistics for this chapter

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