R Tutorial : Quantitative Risk Management in R

Key Takeaways

hello my name is Alex McNeil and I'm going to take you through an introduction to quantitative risk management my background is in mathematical statistics actuarial science and quantitative finance together with my colleagues Radhika Frey and Paul M brakes I am the author of the book quantitative risk management concepts techniques and tools published by Princeton University Press if you want the theoretical background to this course I recommend you take a look together with my co-authors and with marius Hoffert i've also created the website qrm tutorial.org which provides complimentary materials to the QRM book in particular there's a lot of our code there which you might want to try after you've taken this course marius and I have two our packages which feature prominently in qrm tutorial these are qrm data which is a large collection of financial data sets for students and researchers and q RM and QM tools which is a set of useful functions you'll use these packages in the course in qrm the goal is to quantify the risk of a portfolio of risky assets measuring the risk is the first step towards managing the risk in the book I wrote we consider the typical portfolios of risky assets held by banks and insurance companies and sometimes also their liabilities but the ideas apply equally to the portfolio of a private investor like you or me managing the risk can entail many things for example you might try to reduce the risk by selling assets by acquiring new assets to increase the diversification of the portfolio or by using so-called hedging strategies for the banks and insurers an important part of managing risk is making sure they have sufficient capital to withstand large losses on their portfolios and remain solvent for this purpose they compute measurements of the amounts that they could lose in periods of extreme market stress a well-known example of one of these risk measures is value at risk in this course you will work up to calculating value at risk for portfolios to begin with it is important to consider where the risk in a portfolio comes from so think of a portfolio of risky assets it might contain some stock index trackers some individual stocks some government bonds or Treasuries some corporate bonds it might contain assets denominated in domestic currency and some denominated in foreign currencies it might contain commodities like gold it might contain some derivative securities designed to hedge risk or speculate on price movements such as equity options the value of this portfolio at any point in time depends on the fluctuating values of many underlying quantities which we call risk factors examples of risk factors are equity indexes individual equity prices foreign exchange rates interest rates for different borrowing periods in the case of bonds and commodity prices let's load the QRM data package and then the data set s be 500 this contains daily values of the well known equity index based on 500 of the most important US stocks using head and tail the first few lines and the last few lines of the data can be displayed note the dates of the first few values in 1950 and the last few values up to the end of 2015 let's plot the index you can see it has generally gone up over time but with some draw downs such as around the 2008 to 2009 financial crisis now it's your turn to work through the exercises and explore some financial risk factor data you'll look at another stock index some individual equity prices and a couple of exchange rates