Hedge funds started back in the 1960s when Alfred Winslow Jones launched the first one. It was weird at the time because he used leverage and short selling. Nobody else was doing that. But the industry stayed small until the late 1980s.
Every interest rate model you have seen so far in this book assumes some form of random process. Brownian motion, mean reversion, stochastic volatility. They all start with “assume interest rates follow this stochastic differential equation” and then build a pricing framework on top. Chapter 68 of Wilmott’s book throws all of that out the window. No random walks. No probability distributions. No volatility parameters. Just bounds. This is the Epstein-Wilmott model, and it is refreshingly different.
In Part 1 we covered what alternative investments are and how hedge funds are structured. Now we get into the fun stuff. How do hedge funds actually make money? What strategies do they use? And how does leverage turn a 10% market gain into a 23% return?
In the previous chapter, we learned how to allocate money between risky and safe assets in continuous time. The answer was clean: hold a constant fraction in stocks and rebalance. But that result assumed the world follows a smooth lognormal random walk with no sudden jumps. What if you know that a crash could happen at any moment? Not a small dip, but a real crash, like October 1987. Chapter 67 of Wilmott’s book, written with Ralf Korn, tackles exactly this question. The answer is no longer a constant fraction, and the way the optimal allocation changes over time matches our intuition perfectly.
In the one-period portfolio models like Markowitz and CAPM, you make your investment decision once and then sit and wait. You cannot change your mind. But in real life, you check your portfolio, see how the market moved, and rebalance. You do this every day, every hour, maybe every minute. Chapter 66 of Wilmott’s book, based mostly on Merton’s work, develops the theory of continuous-time investment and portfolio rebalancing. The results are surprisingly elegant and give genuine practical insight.
Chapter 1 opens with a warning. If you’re new to hedge funds, you will get overwhelmed. There’s a lot of terminology. There’s a lot of moving pieces. But Mirabile does a good job laying the foundation here. Let’s walk through it.
So I picked up this book called Hedge Fund Investing: A Practical Approach to Understanding Investor Motivation, Manager Profits, and Fund Performance by Kevin R. Mirabile. And honestly, it’s one of those books that sounds intimidating but actually breaks things down pretty well.
One of the core assumptions behind most of quantitative finance is that stock returns are independent. What happened yesterday tells you nothing about today. The stock went up ten days in a row? Irrelevant. Tomorrow is a fresh coin toss. But is this really true? Chapter 65 of Wilmott’s book looks at the evidence for serial autocorrelation in stock returns and asks what happens to our models when returns are not independent.
Ask most options traders which parameter matters more for pricing: volatility or dividends. Almost everyone says volatility. And almost everyone is wrong. Chapter 64 of Wilmott’s book shows that for many common option structures, the sensitivity to dividend yield actually exceeds the sensitivity to volatility. Once you see the numbers, you start treating dividends very differently.
Here is something that should make every options trader stop and think. The “optimal” time to exercise an American option depends on who you are. The textbook answer assumes the holder is delta hedging. But if the holder were delta hedging, why would they buy the option in the first place? Chapter 63 of Wilmott’s book, based on a 1998 paper with Dr. Hyungsok Ahn, digs into this question and reaches a conclusion that is great news for option writers.