Subtitles section Play video Print subtitles The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JAKE XIA: This is the second time we are having this class. We had it last year in a smaller version. That was for six units of a credit, and we had it once a week. And mostly practitioners from the industry, from Morgan Stanley, talking about examples how math is applied in modern finance. And so we got some good response last year. So, with the support of the math department, we decided to expand this class to be 12 units of credit and have twice a week. So, we have every Tuesday and Thursday afternoon from 2:30 to 4:00, as you know, in this classroom. So last year, Dr. Vasily Strela and I-- by the way, I'm Jake Xia and that's Dr. Vasily, and we were the main instructors last year. Now we doubled it up to four main instructors. That's Dr. Peter Kempthorne and Dr. Choongbum Lee. The reason we doubled up the main instructors is we have newly added math lectures, mostly focusing from linear algebra, probability to statistics, and some stochastic calculus to give you the foundation to understand the math will be used in those examples in the lecture taught by the practitioners from the industry. And the purpose of this course is really to give you a sampling menu to see how mathematics is applied in modern finance and help you to decide if this is a field that you would be-- RECORDED VOICE: Thank you, for using WebEx. Please visit our website at www.webex.com. JAKE XIA: OK, you heard that. And so hopefully, this will give you enough information to decide this is a field you would like to pursue in your future career. In fact, last year when we finished the class, we had a few students coming to work in the industry. Some work at Morgan Stanley, some work at elsewhere. So that's really the goal. And at the same time, obviously, you will further solidify your math knowledge and learn new content. And we put the prerequisite about the math part a bit later. So I will use today's first lecture's time to give you an introduction, really, to prepare you some basic background knowledge about the financial markets. Some terminologies will be used, which you may not have heard before. So before I get into the introduction, I always like to know who are actually in the classroom, so let me ask you a few questions. You just need to raise your hands so I know roughly what kind of background and where you are. So how many undergraduate students are here? So I would say 80% percent. How many graduate students are here, just to verify? Yep, that's about right, 20%. And how many students are in finance and business major? Just one. And how many of you are a math major? Most of you. How many of you are engineering majors? A few. How many of you actually are from other universities? Great, because last year we had quite a few, so I want to specifically tell you that you're very welcome to attend the classes here. So it's open door. And last year I remember we had a couple of students from Harvard. That's where I actually work right now. I forgot to mention that, but I'm affiliated with both the math department and the Sloan school here. So anyway, thanks for that. We will be doing a bit more polling along the way, mainly to get feedback of how you feel about the class. Last year we had it online, so if you feel the class is going too fast, or the math part is going too slow, or the finance part is a bit confusing, the easiest way is really just to send us emails, which you will find from the class website. So anyway, today-- VASILY STRELA: And all of us got MIT emails. JAKE XIA: Yes. We all have MIT emails, which are listed on the website. VASILY STRELA: [INAUDIBLE]. JAKE XIA: And obviously, we have offices here. You can easily stop by Peter and Choongbum's offices. And Vasily and I probably will be less often on campus, but we'll be here quite often and definitely love to be more. So anyway, I will start today's lecture with a story, and a quiz at the end. Don't worry, it's not a real quiz. Just going to ask you some questions you can raise your hand and give your answer. But let me start with my story. This is actually my personal story. I want to tell you why I tell the story later. But the story actually was in the mid '90s. I just left Salomon Brothers -- that was my first financial industry job -- to go to Morgan Stanley in New York to join the options trading desk. So the first day, I sat down, I opened the trading book, I found something was missing. So, I turned around, I asked my desk quant. I said, where is the vega report? So, let me show you. So that's the story. So I'm obviously not going to tell you the story of Pi or "Life of Pi." That's not a financial story. The rest of the story, alpha, beta, delta, gamma, theta, which you will learn from Peter and Choongbum and Vasily's classes. So I'm going to talk about vega. So by the way, before I tell you the story, what's unique about vega on this list? AUDIENCE: It's not a Greek letter. JAKE XIA: It's not a Greek letter. That's right. So I turned around and asked my desk quant, I said, where's the vega report? But how many of you actually know what a vega is? OK, lot of people know. So anyway, I'm not going to-- just for the people who haven't heard about it before, it's a measurement about a book or portfolio or position's sensitivity to volatility. So, what is volatility? Which again, you will learn more in rigorous terms how it's defined in mathematics. But the meaning of it is really a measurement or indication of how volatile, or what's the standard deviation of a price can change over time. That's all you need to know right now. I'm not going to ask you questions later. So my desk quant look at me, said-- this is supposed to be options trading desk, so he look at me puzzled. So instead of answering my question, he handed over me a training manual for new employees and new analysts. So I opened the training manual and looked it through. I actually found my answer. So actually, at Morgan Stanley this is not called vega, it's called kappa. So now, I remember to call it kappa. Kappa is actually a Greek letter. So further, I look on the same page there was actually a footnote, which I copied down. So the footnote about why it's called kappa at Morgan Stanley. Kappa is also called vega by some uneducated traders at the Salomon Brothers. That's where I came from. I just joined. They have mistaken vega as a Greek letter after gambling at Vegas. So anyway, so that was my first day. So obviously, I learned how to call kappa very quickly, because I came from Salomon Brothers. And I called it kappa in the last 17 years, but you will hear people calling it vega. Obviously, I have probably more people calling it the vega.