Читать книгу Advanced Portfolio Management - Giuseppe A. Paleologo - Страница 19

We saw that a simple factor model enables you to do performance attribution, risk decomposition and hedging. These three tasks are tightly connected. If from risk decomposition you can detect that market risk is large, then it is more likely that a substantial fraction of your PnL is attributable to market returns, and this also means that this market risk can be completely hedged, and that the market-originated PnL can be eliminated. Behind it all, this framework enables you to see at work a fundamental design principle: separation of concerns [Dijkstra, 1982]. Disparate activities like building a car, writing the code for a video game or managing a portfolio have one thing in common: they are tasks that require solving many problems at once. If we had to address them all at once, we would likely build a Ford Pinto,¹¹ release Duke Nukem Forever¹² or be forced to shut down in record time. In order to control complexity, we need to identify smaller, simpler parts of the system; we need to separate them and solve the issues related to them; and finally we have to put them back together with little effort. Modularization, Encapsulation, and Composition. Factor models allow us to separate factor idiosyncratic returns. In the process, they help us separate unwanted risk and performance from intentional risk and performance. When we size our positions, we do so to capture the sources of returns that are specific to a firm. We have a clean way to address the unwanted risk, via hedging. Even better: in the following chapters you will learn about factors and their interpretation, so that the subsystem that constitutes “unwanted risk” can give you context about the investing environment. And this environment can be further decomposed in smaller components, such as country, industry and style risks, which we will cover in the following chapters.