Academic Research on Business Recovery Risk 

• A multi-factor approach for systematic default and recovery risk, Rsch, D., & Scheule, H. H. (2005). A multi-factor approach for systematic default and recovery risk.Journal of Fixed Income,15(2), 63-75. The following article develops a simultaneous multi-factor model for defaults and recoveries. Applying this model, risk parameters can be forecast using systematic and idiosyncratic risk fac-tors and their implied correlations. The theoretical framework is accompanied by an empirical analysis in which a negative correlation between defaults and recoveries over the business cycle is observed. In the study, default and recovery rates are modeled by business cycle indicators and the properties of the economic and regulatory capital given these risk drivers are shown.
• An empirical analysis of bond recovery rates: exploring a structural view of default, Covitz, D. M., & Han, S. (2004). An empirical analysis of bond recovery rates: exploring a structural view of default. A frictionless, structural view of default has the unrealistic implication that recovery rates on bonds, measured at default, should be close to 100 percent. This suggests that standard "frictions" such as default delays, corporate-valuation jumps, and bankruptcy costs may be important drivers of recovery rates. A structural view also suggests the existence of nonlinearities in the empirical relationship between recovery rates and their determinants. We explore these implications empirically and find direct evidence of jumps, and also evidence of the predicted nonlinearities. In particular, recovery rates increase as economic conditions improve from low levels, but decrease as economic conditions become robust. This suggests that improving economic conditions tend to boost firm values, but firms may tend to default during particularly robust times only when they have experienced large, negative shocks.
• Company accounts-based modelling of business failures and the implications for financial stability, Bunn, P., & Redwood, V. (2003). Company accounts-based modelling of business failures and the implications for financial stability. This paper examines the determinants of failure among individual UK public and private companies over the period from 1991 to 2001. Using information on profitability, interest cover, capital gearing, liquidity, company size, industry, whether a firm is a subsidiary and overall economic conditions, it is possible to construct estimates of the probability of failure for individual companies. These are used to calculate each company's 'debt at risk': the probability of failure multiplied by its outstanding debt. By summing the firm-level debt at risk over all companies it is possible to produce an aggregate measure of financial risk that takes account of how debt is distributed across individual companies. Aggregate debt at risk, as a percentage of total debt, has fallen from the levels reached in the early 1990s and has remained relatively stable despite the build-up in corporate debt since then.
• The value of non-financial information in SME risk management, Altman, E. I., Sabato, G., & Wilson, N. (2008). The value of non-financial information in SME risk management. Within the commercial client segment, small business lending is gradually becoming a major target for many banks. The new Basel Capital Accord has helped the financial sector to recognize small and medium sized enterprises (SMEs) as a client, distinct from the large corporate. Some argue that this client base should be treated like retail clients from a risk management point of view in order to lower capital requirements and realize efficiency and profitability gains. In this context, it is increasingly important to develop appropriate risk models for this large and potentially even larger portion of bank assets. So far, none of the few studies that have focused on developing credit risk models specifically for SMEs have included qualitative information as predictors of the company credit worthiness. For the first time, in this study we have available non-financial and 'event' data to supplement the limited accounting data which are often available for non-listed firms. We employ a sample consisting of over 5.8 million sets of accounts of unlisted firms of which over 66,000 failed during the period 2000-2007. We find that qualitative data relating to such variables as legal action by creditors to recover unpaid debts, company filing histories, comprehensive audit report/opinion data and firm specific characteristics make a significant contribution to increasing the default prediction power of risk models built specifically for SMEs.
• Application impact analysis: A risk-based approach to business continuity and disaster recovery, Epstein, B., & Khan, D. C. (2014). Application impact analysis: A risk-based approach to business continuity and disaster recovery.Journal of business continuity & emergency planning,7(3), 230-237. There are many possible disruptions that can occur in business. Overlooking or under planning for Business Continuity requires time, understanding and careful planning. Business Continuity Management is far more than producing a document and declaring business continuity success. What is the recipe for businesses to achieve continuity management success? Application Impact Analysis is a method for understanding the unique Business Attributes. This AIA Cycle involves a risk based approach to understanding the business priority and considering business aspects such as Financial, Operational, Service Structure, Contractual Legal, and Brand. The output of this analysis provides a construct for viewing data, evaluating impact, and delivering results, for an approved valuation of Recovery Time Objectives (RTO).