Introduction

• Author: Weidong Tian
• Date: 01 June 2017
• DOI: http://doi.org/10.1111/irfi.12127
• Published date: 01 June 2017

Introduction

WEIDONG TIAN

Belk College of Business, University of North Carolina at Charlotte, Charlotte, NC, USA

On March 18, 2016, the International Review of Finance and the University of

North Carolina at Charlotte co-hosted the conference on “Model Risk.”Six

papers were selected for presentation among over 40 submissions, and about

100 participants from universities and policy-making institutions in USA,

Europe, Australia, Canada, and China were in attendance.

In conjunction with this conference, the International Review of Finance had

a Call for Papers for a special issue on “Model Risk.”The submitted and solicited

papers for the special issue cover a variety of important topics on model risk,

including asset pricing under model risk, portfolio selection under model risk,

measuring and managing model risk, the role of model risk in comprehensive

capital analysis and review (CCAR), and model calibration and inconsistency.

After a rigorous double-bind review process, five papers are selected for inclusion

in this special issue of Model Risk.

The first paper, entitled “Tail Dependence and Systemic Risk in Operational

Losses of the US Banking Industry”by A. Abdymomunov and I. Ergen, addresses

one important area of the role of model risk in operational risk assessment (see

Basak and Buffa, 2016; BCBS, 2014). The authors find strong evidence of high

correlations among tail losses of different operational risk types within U.S. bank-

ing industry. These correlations of operational losses across banks can be high as

40%, and thus, the operation risk could be significantly underestimated without

the accounting of these correlations. This study also identifies a systemic risk

component from the simultaneous occurrence of operational tail losses in large

financial institutions.

The second paper, entitled “Model Uncertainty Effect on Asset Prices”by

J. Jiang and W. Tian, studies a no-arbitrage asset pricing problem under model

uncertainty, focusing on model validation in derivative markets. The traditional

approach to address this type of asset pricing problems is to find a set of prices

that are bounded by all prices in plausible asset pricing models. This study

proposes a weighted-average approach by following an arbitrage-free concept

under model uncertainty and reveals a non-trivial “model uncertainty effect,”a

special type of the uncertainty effect discussed in Gneezy, List, and Wu (2006).

This study shows that if the model uncertainty is generated by the volatility

parameter uncertainty and when the contingent claim has either a convex or a

concave payoff structure, the asset price remains within the pricing bounds of

all feasible models, which is regarded as exhibiting no model uncertainty effect.

© 2017 International Review of Finance Ltd. 2017

International Review of Finance, 17:2, 2017: pp. 173–175

DOI: 10.1111/irfi.12127