Insegnamento a.a. 2018-2019

• Deterministic cash flows. The basic premise in cash flow modeling is the understanding of the time value of money. Thus, the timing of cash flows affects asset values and rates of return. The simplest cash flows are those that are deterministic, either with one or several periods. Fixed-income securities belong to this class and can be analysed by means of interest rates.
• Random cash flows. Typically, the initial cost of an investment is known, while its future cash flows are random. Cash flow uncertainty can be analysed by means of different techniques and we focus here on the mean-variance and the arbitrage analysis. The starting point of our analysis is that investors like returns and dislike risk. After defining precisely what the term risk means, we relate it to investments and look at methods to measure risk. Finally, we discuss the relation between risk and return, and use it to determine security prices.
• Derivative cash flows. The next level of complexity pertains to cash flow streams that are random and depend functionally on another asset. We introduce simple derivative securities such as futures, forwards and (European) options and describe how they work. Pricing is done through arbitrage analysis and we see how derivative assets can be used to increase returns or limit losses.

• Identify the quantitative models and methods for pricing financial assets.
• Identify the quantitative models and methods for portfolio formation, i.e. bundling financial assets, in order to mitigate risk.

• Design a portfolio of bonds that minimizes interest rate risk.
• Design a portfolio of stocks that is optimal in the mean-variance sense.
• Design a portfolio of options that allows to profit from future movements in the underlying price.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)

"Exercises" include:

• Problem sets: at the end of each unit, exercises (problem sets) are circulated in class. Problem sets resemble the structure of the final exam, and thus serve as mock training. Problem sets do not count towards the course grade, but help in timely checking their knowledge acquisition as the course progresses.
• Applications: at the end of each unit, we use laptops to implement some of the tools developed during the lectures with real world data.

Written exam with closed-ended questions.

D.G. LUENBERGER, Investment Science, Oxford University Press, 2013, 2nd edition.