The two fields featured in the title of this book—Practical Methods of Financial Engineering and Risk Management—are intertwined. The practical methods I teach in this book focus on the interplay and overlap of financial engineering and risk management in the real world. My goal is to take you beyond the artificial assumptions still relied on by too many financial practitioners who prefer to treat financial engineering and risk management as separate specialties. These assumptions don’t just distort reality—they can be dangerous. Performing either financial engineering or risk management without due regard for the other has led with increasing frequency to disastrous results.
The dual purpose of risk management is pricing and hedging. Pricing provides a valuation of financial instruments. Hedging provides various measures of risk together with methods to offset those risks as best as possible. These tasks are performed not only by risk managers but also by traders who price and hedge their respective trading books on a daily basis. Successful trading over extended periods of time comes down to successful risk management. And successful risk management comes down to robust valuation, which is the main prerogative of financial engineering.
Pricing begins with an analysis of possible future events, such as stock price changes, interest rate shifts, and credit default events. Dealing with the future involves the mathematics of statistics and probability. The first step is to find a probability distribution that is suitable for the financial instrument at hand. The next step is to calibrate this distribution. The third step is to generate future events using the calibrated distribution and, based on this, provide the necessary valuation and risk measures for the financial contract at hand. Failure in any of these steps can lead to incorrect valuation and therefore an incorrect assessment of the risks of the financial instrument under consideration.
Hedging market risk and managing credit risk cannot be adequately executed simply by monitoring the financial markets. Leveraging the analytic tools used by the traders is also inadequate for risk management purposes because their front office (trading floor) models tend to look at risk measures over very short time scales (today’s value of a financial instrument), in regular market environments (as opposed to stressful conditions under which large losses are common), and under largely unrealistic assumptions (risk-neutral probabilities).
To offset traditional front-office myopia and assess all potential future risks that may occur, proper financial engineering is needed. Risk management through prudent financial engineering and risk control—these have become the watchwords of all financial firms in the twenty-first century. Yet as many events, such as the mortgage crisis of 2008, have shown, commonly used statistical and probabilistic tools have failed to either measure or predict large moves in the financial markets. Many of the standard models seen on Wall Street are based on simplified assumptions and can lead to systematic and sometimes catastrophic underestimation of real risks. Starting from a detailed analysis of market data, traders and risk managers can take into account more faithfully the implications of the real behavior of financial market —particularly in response to rare events and exceedingly rare events of large magnitude (often called black swan events). Including such scenarios can have significant impacts on asset allocation, derivative pricing and hedging, and general risk control.