Forthcoming articles

International Journal of Financial Engineering and Risk Management

International Journal of Financial Engineering and Risk Management (IJFERM)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Financial Engineering and Risk Management (3 papers in press)

Regular Issues

  • An Equity-Credit Hybrid Model for Asset Correlations   Order a copy of this article
    by Fabio Dias 
    Abstract: Single factor Gaussian copula models are widely used to manage credit risk of loan portfolios, even driving how many large financial institutions are capitalised under Basel II / III. Under this formulation, the default correlation between two separate firms is directly explained by their asset correlation to a systematic factor, which can be estimated using either equity correlations or observed default rates, with the portfolio losses usually being simulated under a Gaussian copula model. Though it is widely accepted that the use of observed default rates or even equity returns to calibrate a single factor Gaussian copula model is likely to understate the tail risk, this paper proposes a Bayesian approach for a single factor Gaussian copula where the asset correlations are modelled using an inverse Wishart prior with the scale parameter calibrated to observed default rates and the degrees of freedom chosen using the in-sample continuous ranked probability score whilst the equity correlations are used to obtain the posterior distribution. The proposed hybrid model is shown to produce probabilistic forecasts of defaults with better out-of-sample performance than the standard single factor Gaussian copula even though it maintained low complexity and ease of implementation.
    Keywords: asset correlations; credit risk management; structural model of credit risk; factor copula models.

  • Financial markets, energy stock prices and energy political decisions: The case of Cyprus   Order a copy of this article
    by Augustinos Dimitras 
    Abstract: The impact of energy deals announcements on the stock markets is a topic that concerns not only academics but investors and funds too. This study examines the linkages between financial markets and energy firms' stock price returns after the decision of the Cypriot government to exploit its oil and natural gas deposits within its exclusive economic zone. This decision has several geopolitical aspects which affect a large number of interested parties. We employ a dynamic conditional correlation model to measure the effect on financial markets as well as energy firms' stock returns. Results provide evidence that there is no significant impact on either financial markets or energy firms stock prices. Moreover, we highlight the importance of similar political decisions and the reactions of Cyprus' neighbouring countries.
    Keywords: energy; financial markets; dynamic conditional correlation; geopolitics of energy.

  • The Accuracy of Alternative Supervisory Methodologies for the Stress Testing of Credit Risk   Order a copy of this article
    by Michael Jacobs 
    Abstract: Banking supervisors have grappled with the problem of determining the optimal level of loss absorbing capital re-sources that institutions should hold to support their risk taking activities. Following the recent financial crisis tra-ditional approaches such as regulatory capital ratios have been supplanted to supervisory stress testing as a primary tool for managing systemic risks. Financial institutions are mandated to perform stress testing to forecast o performance over hypothetical multi-year stress scenarios, in the process developing models to support these forecasts. In parallel supervisors conduct their own stress tests and develop supporting models, to set financial institutions minimum regulatory capital requirements in multiple jurisdictions, yet nothing is revealed to the public regarding the the accuracy of such models. In this study we investigate a modeling framework that we believe to be very close to that employed by the regulators, which projects various financial statement line items for an aggregated average bank. We use various time periods, including the 2008 financial crisis, to assess the accuracy of alternative stress test modeling approaches, in particular simple single equation as compared to more complex multiple equation approaches, and in the latter case whether accounting for the correlation between line items has an influence on model results on both an in- and- out-of-sample basis. Our results show potentially inaccuracies in stress test model fore-casts, even for models that fit the data exceptionally well in-sample, especially where more complex multi-equation models similar to those used by the Federal Reserve are mispecified and underperform simple models in explanatory power, due to incorrectly accounting for the dependency structure. We find that in the test sample, the 2-equation VAR model for IBTEIA and TAG performs best, and the single equation models for IBTEIA and TAG performs worse, while the single equation AR model for IBTEI has intermediate performance. Our results highlight the public policy need for reconsidering the existent regulations that fail to place limits on the use of regulatory stress tests, and the need for supervisory models to be subject to model validation and governance standards.
    Keywords: Stress Testing; CCAR; DFAST; Credit Risk; Financial Crisis; Model Risk; Capital Adequacy; Income before Taxes and Extraordinary Items.