Special Issue "Portfolio Optimization and Risk Management: New Development and Applications"

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

Deadline for manuscript submissions: 31 March 2019

Special Issue Editors

Guest Editor
Prof. Dr. Qiji (Jim) Zhu

Department of Mathematics, Western Michigan University
Website | E-Mail
Interests: 1. Financial mathematics; 2. Variational and nonsmooth analysis; 3. Optimization
Guest Editor
Prof. Dr. Stanislaus Maier-Paape

Institut für Mathematik, RWTH Aachen University, Germany
Website | E-Mail
Interests: Asset allocation, risk management, portfolio optimization and quantitative finance

Special Issue Information

Dear Colleagues,

Portfolio optimization and related risk analysis is one of the central themes in financial mathematics. Since the pioneering work of Markowitz, portfolio theory has had a great impact on both financial theory and applications. Early portfolio theory focused on the trade-off between mean as an indication for reward and variation as a risk measure.

The need in financial practice stimulated the development of more general reward and risk measures. Recently, new frameworks for portfolio theory have begun to emerge. For instance, practically important drawdown risk measures attracted more attention from both researchers and practitioners.

This Special Issue aims to stimulate discussions on new developments of the portfolio theory and their practical applications. We therefore welcome and encourage the submission of high quality papers related, but not limited to, the following topics:

  • New framework for portfolio optimization
  • Theory on trading strategies (multi-period portfolios)
  • Analysis of risk measures
  • Applied risk management
  • Asset allocation in theory and practice
  • Application in finance and elsewhere

Prof. Dr. Qiji (Jim) Zhu
Prof. Dr. Stanislaus Maier-Paape
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

  • portfolio theory
  • applied finance
  • risk measures
  • asset allocation

Published Papers (2 papers)

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Research

Open AccessArticle Peer-To-Peer Lending: Classification in the Loan Application Process
Received: 20 October 2018 / Revised: 2 November 2018 / Accepted: 5 November 2018 / Published: 9 November 2018
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Abstract
This paper studies the peer-to-peer lending and loan application processing of LendingClub. We tried to reproduce the existing loan application processing algorithm and find features used in this process. Loan application processing is considered a binary classification problem. We used the area under [...] Read more.
This paper studies the peer-to-peer lending and loan application processing of LendingClub. We tried to reproduce the existing loan application processing algorithm and find features used in this process. Loan application processing is considered a binary classification problem. We used the area under the ROC curve (AUC) for evaluation of algorithms. Features were transformed with splines for improving the performance of algorithms. We considered three classification algorithms: logistic regression, buffered AUC (bAUC) maximization, and AUC maximization.With only three features, Debt-to-Income Ratio, Employment Length, and Risk Score, we obtained an AUC close to 1. We have done both in-sample and out-of-sample evaluations. The codes for cross-validation and solving problems in a Portfolio Safeguard (PSG) format are in the Appendix. The calculation results with the data and codes are posted on the website and are available for downloading. Full article
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Open AccessArticle A Threshold Type Policy for Trading a Mean-Reverting Asset with Fixed Transaction Costs
Received: 11 August 2018 / Revised: 16 September 2018 / Accepted: 22 September 2018 / Published: 29 September 2018
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Abstract
A mean-reverting model is often used to capture asset price movements fluctuating around its equilibrium. A common strategy trading such mean-reverting asset is to buy low and sell high. However, determining these key levels in practice is extremely challenging. In this paper, we [...] Read more.
A mean-reverting model is often used to capture asset price movements fluctuating around its equilibrium. A common strategy trading such mean-reverting asset is to buy low and sell high. However, determining these key levels in practice is extremely challenging. In this paper, we study the optimal trading of such mean-reverting asset with a fixed transaction (commission and slippage) cost. In particular, we focus on a threshold type policy and develop a method that is easy to implement in practice. We formulate the optimal trading problem in terms of a sequence of optimal stopping times. We follow a dynamic programming approach and obtain the value functions by solving the associated HJB equations. The optimal threshold levels can be found by solving a set of quasi-algebraic equations. In addition, a verification theorem is provided together with sufficient conditions. Finally, a numerical example is given to illustrate our results. We note that a complete treatment of this problem was done recently by Leung and associates. Nevertheless, our work was done independently and focuses more on developing necessary optimality conditions. Full article
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