Special Issue "Applications of Stochastic Optimal Control to Economics and Finance"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (31 January 2019)

Special Issue Editors

Guest Editor
Prof. Salvatore Federico

Department of Economics and Statistics, University of Siena, Piazza San Francesco 7/8, Siena 53100, Italy
Website | E-Mail
Interests: stochastic optimal control theory in finite and infinite dimension; problems with delay; stochastic partial differential equations; viscosity solutions of PDEs; singular stochastic control and optimal stopping
Guest Editor
Prof. Giorgio Ferrari

Center for Mathematical Economics, IMW, Bielefeld University, PO Box 10 01 31 33 501 Bielefeld, Germany
Website | E-Mail
Interests: singular stochastic control; optimal stopping; free-boundary problems; impulse control; stochastic games; real options; mathematical finance; mathematical economics; insurance mathematics
Guest Editor
Dr. Luca Regis

Department of Economics and Statistics, University of Siena, Piazza San Francesco 7/8, Siena 53100, Italy
Website | E-Mail
Interests: actuarial mathematics; insurance; risk management; longevity risk; asset-liability management; financial mathematics; corporate finance

Special Issue Information

Dear Colleagues,

Many problems in economics, finance, and actuarial science naturally lead to optimal agents’ dynamic choices in stochastic environments. Examples include optimal individual consumption and retirement choices, optimal management of portfolios and of risk, hedging, optimal timing issues in pricing American options or in investment decisions.

Stochastic control theory provides the methods and results to tackle all such problems, and this Special Issue aims at collecting high quality papers on the theory and application of stochastic optimal control in economics and finance, and its associated computational methods.

Prof. Salvatore Federico
Prof. Giorgio Ferrari
Dr. Luca Regis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Individual’s optimal investment/consumption policies;
  • Optimal annuitization;
  • Real options;
  • Optimal timing issues in financial and economic problems;
  • Stochastic games;
  • Pricing and hedging of financial derivatives;
  • Systemic risk;
  • Credit risk;
  • Pension funds management;
  • Theoretical and computational methods in stochastic processes.

Published Papers (2 papers)

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Research

Open AccessArticle Dealing with Drift Uncertainty: A Bayesian Learning Approach
Received: 20 November 2018 / Revised: 25 December 2018 / Accepted: 4 January 2019 / Published: 9 January 2019
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Abstract
One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated [...] Read more.
One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain level of confidence, we focus on drift uncertainty in this paper. Building on filtering techniques and learning methods, we use a Bayesian learning approach to solve the Markowitz problem and provide a simple and practical procedure to implement optimal strategy. To illustrate the value added of using the optimal Bayesian learning strategy, we compare it with an optimal nonlearning strategy that keeps the drift constant at all times. In order to emphasize the prevalence of the Bayesian learning strategy above the nonlearning one in different situations, we experiment three different investment universes: indices of various asset classes, currencies and smart beta strategies. Full article
(This article belongs to the Special Issue Applications of Stochastic Optimal Control to Economics and Finance)
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Open AccessArticle On the Failure to Reach the Optimal Government Debt Ceiling
Received: 19 September 2018 / Revised: 19 November 2018 / Accepted: 27 November 2018 / Published: 4 December 2018
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Abstract
We develop a government debt management model to study the optimal debt ceiling when the ability of the government to generate primary surpluses to reduce the debt ratio is limited. We succeed in finding a solution for the optimal debt ceiling. We study [...] Read more.
We develop a government debt management model to study the optimal debt ceiling when the ability of the government to generate primary surpluses to reduce the debt ratio is limited. We succeed in finding a solution for the optimal debt ceiling. We study the conditions under which a country is not able to reduce its debt ratio to reach its optimal debt ceiling, even in the long run. In addition, this model with bounded intervention is consistent with the fact that, in reality, countries that succeed in reducing their debt ratio do not do so immediately, but over some period of time. To the best of our knowledge, this is the first theoretical model on the debt ceiling that accounts for bounded interventions. Full article
(This article belongs to the Special Issue Applications of Stochastic Optimal Control to Economics and Finance)
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