An Exploration of the Evolution of Risk: Past, Present and Future
Nicholas C Silitch
Foreword
Introduction
An Exploration of the Evolution of Risk: Past, Present and Future
Risk Trading, Risky Debt and Financial Stability
Skating on Thinner Ice: A Macroeconomic Outlook at the End of the Credit Cycle
Climate Change: Managing a New Financial Risk
The Quest to Save Risk-Weighted Assets
The Evolution of the CLO Market since the Global Financial Crisis and a Valuation Approach for CLO Tranches
Homo Ex Machina: Finance Rebooted
Innovation and Digitisation in Credit: A Global Perspective
The Lending Revolution: How Digital Credit Is Changing Banks from the Inside
Digital Lending in Asia: Disruption and Continuity
Digitisation and Automation in Commercial Lending: Disruption without Distraction
Credit Risk Management in the Era of Big Data: From Measurement to Insight
Artificial Intelligence and Machine Learning in Credit Risk Analytics: Present, Past and Future
Integrated Loan Portfolio Modelling and Risk Management
The Role of Banks in Illiquid Credit Markets, and the Disruption and Evolution of Credit Portfolio Management
Epilogue
The shape and texture of debt instruments and portfolios have morphed materially since the early 1900s, and while this has changed the way credit risk manifests, it has not changed the foundational need for the analysis of each issuer’s business model, cashflows and balance sheet. What has changed is the ability to assemble broadly diversified portfolios of credit risk efficiently and economically; this is critically important when investing in instruments where the distribution of outcomes is severely limited on the upside and conceivably unlimited on the downside. This evolution has made it vitally important to understand the drivers of correlation, and how they may change through time, in assessing portfolios of credit risk. While for many the evaluation of correlations is limited to a mathematical exercise formed by the careful analysis of historical return data, this approach is inherently shortsighted. The data is inevitably limited to a much shorter time period than we would like: 10, 15 or 30 years. Consequently, the number of truly systemic events that occur within the lifespan of our data is limited. How do we enhance our understanding of what awaits us as we look to the
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