Midterm 1 and Current Course Grades in Finance 4366

I just uploaded the midterm 1 grades, along with current attendance/participation, quiz, problem set, and Finance 4366 course grades, to Canvas.

As stated in the course syllabus, final numeric course grades will be determined according to the following equation:

Final Course Numeric Grade =.10(Attendance and Participation) +.10(Quizzes) +.20(Problem Sets) + Max{.20(Midterm Exam 1) +.20(Midterm Exam 2) +.20(Final Exam),.20(Midterm Exam 1) +.40(Final Exam),.20(Midterm Exam 2) +.40(Final Exam)}

As I noted in my February 25th blog posting entitled “Finance 4366 Grades on Canvas”, as the spring semester progresses and I continue to collect grades in the attendance, quiz, problem set, and exam categories, then the course grade listed on Canvas will dynamically incorporate that information on a timely basis for each student; now that we have Midterm 1 Exam grades, the equation that I am now using (until Midterm 2) is:

Course Numeric Grade after Midterm 1 = (.10(Attendance and Participation) +.10(Quizzes) +.20(Problem Sets) +.20(Midterm 1))/.6

There are n = 15 students enrolled in Finance 4366. Here are the current grade statistics, broken down by grade category:

As you can see from this table, more than half of the students enrolled in Finance 4366 have scored the mean or higher in all grade categories other than quizzes (since in all but the quiz category, the median is higher than the mean). Although actual letter grades won’t be assigned until after the final exam, hypothetically, you can determine where your course letter grade currently stands by comparing it with the course letter grade schedule that also appears in the course syllabus:

If you are disappointed by your performance to date in Finance 4366, keep in mind that the final exam grade automatically double counts in place of a lower midterm exam grade. In case both midterm exam grades are lower than the final exam grade, then the final exam grade replaces the lower of the two midterm exam grades.

If any of you would like to have a chat with me about your grades in Finance 4366, then by all means, stop by my office (Foster 320.39) 3:30-4:30 pm TR, or set up a Zoom appointment with me.

Binomial spreadsheet – replicating portfolio and risk neutral valuation approaches

Linked below is the spreadsheet we used in class today. After looking it over a bit closer, it is in fact correctly coded. My “error” was in forgetting that I had coded the 1 and 2 timestep call and put models contained therein using backward induction. Under backward induction, one calculates option prices by starting at the end of the binomial tree and working back, node by node, to the beginning of the binomial tree. Although backward induction is required to price options under the delta hedging and replicating portfolio approaches, it is not needed under the risk-neutral valuation approach.

If you open the binomial option spreadsheet, you’ll notice that the 1-timestep call and put prices obtained via backward induction are $3.20 and $1.72 respectively, whereas the 2-timestep call and put prices are $2.56 and $1.08. Of course, these are also the prices obtained via the application of the Cox-Ross-Rubinstein model equations:



1 and 2 timestep binomial option pricing spreadsheet

VIX is back in the news (Page 1 feature article in today’s WSJ)!

It’s back!  At the beginning of this semester, I introduced our class to the CBOE’s Implied Volatility Index (VIX) in my blog posting entitled “On the relationship between the S&P 500 and the CBOE Volatility Index (VIX)“.  In that posting, I pointed out how over relatively short time intervals, percentage changes in VIX and SP500 indices move inversely.

VIX measures market expectations for stock market (S&P500) volatility over the coming 30 days.  It is commonly referred to as a “fear index”, and as such, it is indicative of the near-term degree of overall investor risk aversion.

This article mostly focuses on how investor fears of more aggressive Fed rate hikes and a possible recession are causing prices of options to be bid up, as investors “scurry for protection”.

Investors Are Bracing for Surge in Market Volatility
Bets on a rise in Wall Street’s fear gauge swell to most since March 2020

Finance 4366 Grades on Canvas

Here is a “heads-up” about the Finance 4366 grade book on Canvas.  There, you will find grade averages that reflect 1) attendance grades for the 12 class meetings which have occurred to date, 2) quizzes 1-7, and 3) problem sets 1-5.  Thus, your current (February 25) course numeric grade is based on the following equation:

(1) Current Course Numeric Grade = (.10(Class Attendance) +.10(Quizzes) +.20(Problem Sets))/.4

Note that equation (1) is a special case of the final course numeric grade equation (equation (2) below) which also appears in the “Grade Determination” section of the course syllabus:

(2) Final Course Numeric Grade =.10(Class Attendance) +.10(Quizzes) +.20(Problem Sets) + Max{.20(Midterm Exam 1) +.20(Midterm Exam 2) +.20(Final Exam), .20(Midterm Exam 1) +.40(Final Exam), .20(Midterm Exam 2) +.40(Final Exam)}

Going forward, my goal is for the Finance 4366 grade book to dynamically incorporate new grade information on a timely basis for each student, in a manner consistent with the final course numeric grade equation.  For example, after midterm 1 grading is complete, equation (3) will be used to determine your numeric course grade:

(3) Course Numeric Grade after Midterm 1 = (.10(Class Attendance) +.10(Quizzes) +.20(Problem Sets) +.20(Midterm 1))/.6

After midterm 2 grades are recorded, equation (4) will be used to determine your numeric course grade at that point in time:

(4) Course Numeric Grade after Midterm 2 = (.10(Class Attendance) +.10(Quizzes) +.20(Problem Sets) +.20(Midterm 1) +.20(Midterm 2))/.8

After the spring semester and the final exam period are over, all Finance 4366-related grades will have been collected, and I will use equation 2 above to calculate your final course numeric grade.  At that time, your final course letter grade will be based on the following schedule (which appears in the “Grade Determination” section of the course syllabus):

A 93-100% C 73-77%
A- 90-93% C- 70-73%
B+ 87-90% D+ 67-70%
B 83-87% D 63-67%
B- 80-83% D- 60-63%
C+ 77-80% F <60%


Midterm 1 Exam study hints

A Finance 4366 student asked me for midterm 1 exam study tips.  I recommend reviewing problem sets 3-5 and the sample midterm – solutions for which are available at http://derivatives.garven.com/category/problem-set-solutions/.  It also wouldn’t hurt to review the assigned readings and lecture notes on which the exam is based:

Assigned Readings:

January 31 1. Hull Chapters 1 (“(Introduction”), 2 (“Mechanics of Futures Markets”), 10 (“Mechanics of Options Markets”)
2. Futures and Options Markets (Optional), by Gregory J. Millman
February 2 1. Hull Chapter 5 (“Determination of Forward and Futures Prices”)
2. A Simple Model of a Financial Market, by James R. Garven
February 9 1. Hull Chapter 11 (“Properties of Stock Options”)
2. Properties of Stock Options Chapter synopsis, by James R. Garven
February 14 Hull Chapter 12 (“Trading Strategies Involving Options”)

Lecture Notes:

A Random Walk Down Wall Street – one of many coming attractions in Finance 4366

If you were to read only one book about finance, it would have to be “A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing” by Burton G. Malkiel.  Malkiel’s book (now in its 12th edition) provides a compelling argument in favor of efficient markets theory and investing in (passively managed) index funds.

The efficient market theory implies that stock prices follow a random walk. These ideas were originally conceived by Professors Paul Samuelson (MIT) and Eugene Fama (Chicago) in the 1960s and subsequently popularized by John Bogle (founder of Vanguard), Professor Malkiel (Princeton), and others. In Finance 4366, we rely extensively on the notion that prices of speculative assets (e.g., stocks, bonds, commodities, foreign exchange, etc.) follow random walks as we consider the technical details associated with pricing and hedging risk using financial derivatives.