Financial Engineering to Address Complexity

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

Deadline for manuscript submissions: closed (15 April 2015) | Viewed by 23321

Special Issue Editors


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Guest Editor
Dipartimento di Scienze Economiche, Aziendali e Statistiche. Viale delle Scienze, Edificio 13, 90128 Palermo, Italy
Interests: financial and insurance modeling; portfolio optimization; numerical methods for risk management; computational finance; simulation models

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Guest Editor
Cyprus and The Wharton Financial Institutions Center, University of Cyprus, Philadelphia, PA, USA

Special Issue Information

Dear Colleagues,

The rapid pace of worldwide financial integration has led to a magnification of the interrelations between the different components of the economic systems, thus increasing its complexity as a whole. To give but a few examples, the recent sub-prime crisis has caused serious repercussions on economic growth in many countries and hampered their capacity to pay their debt. International trading of securitized assets caused banking crises and raised questions as to the sustainability of sovereigns’ liabilities after bank bailout. Regulatory boards are struggling to grapple with the manifolds option-like features are embedded in pension plans and insurance policies. As a consequence, the regulators now require the mark-to-market evaluations or credit exposure of such hidden contingent claims, envisaging the possible danger of negative events triggered in sectors that are seemingly associated to the core of their business.

The aim of this Special Issue is to collect financial engineering contributions to addressing such increasing complexity, at the same time being cognizant that financial engineering has contributed to the increased complexity. The objective is to bridge the gap between academics, decision makers and regulators, with the objective of fostering solution models that deal with complexity, instead of oversimplifying it for the sake of theoretical tractability.

The Special Issue will focus both on research topics relevant to the scientific community, and evidence from industry and decision-making case studies. We are interested in original results and innovative methods to solve realistic problems, presented and potentially tested in a rigorous form.

Dr. Andrea Consiglio
Dr. Stavros A. Zenios
Guest Editors

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Keywords

  • financial modelling
  • portfolio optimization
  • scenario generation
  • sovereign debt management
  • option evaluation
  • systemic risk

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Published Papers (3 papers)

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Research

1228 KiB  
Article
The Financial Stress Index: Identification of Systemic Risk Conditions
by Mikhail V. Oet, John M. Dooley and Stephen J. Ong
Risks 2015, 3(3), 420-444; https://doi.org/10.3390/risks3030420 - 16 Sep 2015
Cited by 26 | Viewed by 10977
Abstract
This paper develops a financial stress measure for the United States, the Cleveland Financial Stress Index (CFSI). The index is based on publicly available data describing a six-market partition of the financial system comprising credit, funding, real estate, securitization, foreign exchange, and equity [...] Read more.
This paper develops a financial stress measure for the United States, the Cleveland Financial Stress Index (CFSI). The index is based on publicly available data describing a six-market partition of the financial system comprising credit, funding, real estate, securitization, foreign exchange, and equity markets. This paper improves upon existing stress measures by objectively selecting between several index weighting methodologies across a variety of monitoring frequencies through comparison against a volatility-based benchmark series. The resulting measure facilitates the decomposition of stress to identify disruptions in specific markets and provides insight into historical stress regimes. Full article
(This article belongs to the Special Issue Financial Engineering to Address Complexity)
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1182 KiB  
Article
Supervising System Stress in Multiple Markets
by Mikhail V. Oet, John M. Dooley, Amanda C. Janosko, Dieter Gramlich and Stephen J. Ong
Risks 2015, 3(3), 365-389; https://doi.org/10.3390/risks3030365 - 14 Sep 2015
Cited by 2 | Viewed by 5872
Abstract
This paper develops an extended financial stress measure that considers the supervisory objective of identifying risks to the stability of the financial system. The measure provides a continuous and bounded signal of financial stress using daily public market data. Broad coverage of material [...] Read more.
This paper develops an extended financial stress measure that considers the supervisory objective of identifying risks to the stability of the financial system. The measure provides a continuous and bounded signal of financial stress using daily public market data. Broad coverage of material financial system markets over time is achieved by leveraging dynamic credit weights. We consider how this measure can be used to monitor, analyze, and alert financial system stress. Full article
(This article belongs to the Special Issue Financial Engineering to Address Complexity)
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382 KiB  
Article
Custom v. Standardized Risk Models
by Zura Kakushadze and Jim Kyung-Soo Liew
Risks 2015, 3(2), 112-138; https://doi.org/10.3390/risks3020112 - 20 May 2015
Cited by 9 | Viewed by 5609
Abstract
We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: [...] Read more.
We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: (1) longer horizon risk factors (value, growth, etc.) increase noise trades and trading costs; (2) arbitrary risk factors can neutralize alpha; (3) “standardized” industries are artificial and insufficiently granular; (4) normalization of style risk factors is lost for the trading universe; (5) diversifying risk models lowers P&L correlations, reduces turnover and market impact, and increases capacity. We discuss various aspects of custom risk model building. Full article
(This article belongs to the Special Issue Financial Engineering to Address Complexity)
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