Financial Risk Modeling and Forecasting

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (28 February 2015) | Viewed by 12545

Special Issue Editors


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Guest Editor
Economics Department, University of Essex, Wivenhoe Park, Colchester C04 3SQ, Essex, UK
Interests: financial networks; systemic risk modeling; extreme market events; computational simulators for market and policy design

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Guest Editor
Department of Economics, University of Western Ontario, Social Science Centre Room 4071, London, ON N6A 5C2, Canada
Interests: finance; financial econometrics; computational finance; econometrics
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Special Issue Information

Dear Colleagues,

The financial crisis of 2007-2009 is considered by many economists to be the worst crisis since the Great Depression of the 1930s. While the proximate origin of the crisis was arguably the bursting of the US housing bubble, it had worldwide repercussions and the global recession that followed has spurred a heated debate concerning its causes.

Poor risk management has been highlighted to be one of the factors as to why the crisis took on the magnitude that it did. With the failure of standard volatility and Value at Risk (VaR) based single asset and/or portfolio risk management, should multivariate regime-sensitive approaches, instead, be recommended in the determination of risk measures and risk management?

Moreover, there are failures in extant risk measures to price negative externalities and endogenous risk such as liquidity funding risk, model risk and counterparty risk from interconnectedness. Indeed, can a meaningful distinction be made between micro- and macro-prudential risk management?

Finally, the so called volatility paradox which signals low volatility and correlation measures during market booms may have encouraged procyclical leverage and risk taking which exacerbate market downturns and worsen financial crises. More generally, realizing that market price systemic risk measures such as Co-VaR and Marginal Expected Shortfall have been found to have little or no early warning capabilities, how should risk be forecasted?

This special issue focuses on new thinking and approaches needed to address the shortfalls that have been observed in the aftermath of the 2007 financial crisis on modelling and forecasting financial risk. Among the general topics of research to be considered in this area are:

  1. Identification, estimation and forecasting of higher order risks and risk premia for extreme financial events.
  2. Multivariate financial models, multivariate risk measures and forecasting.
  3. Regime-sensitive risk measures.
  4. Incorporation of the Volatility Paradox in risk measures.
  5. Can distinctions be drawn between micro- and macro-prudential risk management?
  6. Systemic risk and Interconnectedness.
  7. Failure of market-price-based risk measures for yielding early warning signals.

Professor Sheri Markose
Professor Lars Stentoft
Guest Editors

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Published Papers (2 papers)

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Research

1066 KiB  
Article
Volatility Forecast in Crises and Expansions
by Sergii Pypko
J. Risk Financial Manag. 2015, 8(3), 311-336; https://doi.org/10.3390/jrfm8030311 - 5 Aug 2015
Cited by 1 | Viewed by 5989
Abstract
We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow [...] Read more.
We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as linear heterogeneous autoregressive model and GARCH specifications. Finally, we show how to derive closed-form expression for multiple-step-ahead forecasting by exploiting information about the conditional distribution of returns. Full article
(This article belongs to the Special Issue Financial Risk Modeling and Forecasting)
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918 KiB  
Article
Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors
by Mauro Bernardi and Lea Petrella
J. Risk Financial Manag. 2015, 8(2), 198-226; https://doi.org/10.3390/jrfm8020198 - 7 Apr 2015
Cited by 11 | Viewed by 5586
Abstract
This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t [...] Read more.
This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t Markov switching (MS) model and the multiple-conditional value-at-risk (CoVaR) (conditional expected shortfall (CoES)) risk measures introduced in Bernardi et al. (2013), accounting for both the stylized facts of financial data and the contemporaneous multiple joint distress events. The Shapley value methodology is then applied to compose the puzzle of individual risk attributions, providing a synthetic measure of tail interdependence. Our empirical investigation finds that banks appear to contribute more to the tail risk evolution of all of the remaining sectors, followed by the financial services and the insurance sectors, showing that the insurance sector significantly contributes as well to the overall risk. We also find that the role of each sector in contributing to other sectors’ distress evolves over time according to the current predominant financial condition, implying different interdependence strength. Full article
(This article belongs to the Special Issue Financial Risk Modeling and Forecasting)
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