Jonathan Schachter

This, the fifth and final volume in the Trinity Tales series, completes a cycle that began with tales from the 1960s. It invites readers to step into the world of Trinity College as it was in the first decade of this century through the reflections of students who attended the university during those years. Within its pages lie the stories of twenty-eight graduates from a mix of diverse backgrounds whose experiences may dispel the myths of what it means to be a ‘Trinity student’. The collection reveals the rapidly changing world of the early 2000s. This was a time of the internet revolution, when social media first affected student life, when mobile phones and laptops became ubiquitous, when handwritten work was passing into history, when The Buttery closed its doors – and all this coming against the backdrop of an overheating then imploding Irish economy. This kaleidoscope of recollections captures a student body in transformation and features stories of personal discovery and achievement against the odds. For some it proved a life-changing era when sexual, racial or class barriers were confronted. This volume concludes a remarkable half-century journey, portraying the lives of others, and of ourselves.

A First Course in Model Validation and Model Risk Management explains in step-by-step, practical terms how mathematical models owned by financial institutions are essential to their public (sales, trading, risk management, and internal audit) and private (merger and acquisition, and IPO) activities. Like a diverse fleet of cars maintained by a rental car location, a bank must make sure customers can “drive” any of its models in seeking a profit or hedge in a specific financial product. The book is divided into three sections on conventional pricing and risk models, including risk-neutral and historical measures. Chapters consider modeling basics, marked-to-market asset classes, market risk, credit risk, portfolio risk, operational risk, capital model risk, and financial crime, along with machine learning, AI, and Python specific modeling and risk assessment techniques. Problems sets, video examples, sample Python code, and an instructor manual are offered on companion and instructor sites to support learning and provide an opportunity to put concepts into practice. A refresher in statistics and an abbreviation glossary are included across two appendices.