As I noted in my February 1st blog posting entitled “Finance 4335 Grades on Canvas”, as the fall 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 as follows:

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

There are n = 33 students enrolled in Finance 4335. Here are the current grade statistics:

As you can see from this table, over 50% of students have the mean or higher in each category (since in all cases, the median is higher than the mean). I base the GPA calculation on comparing each student’s current course grade to the course letter grade schedule that also appears on the syllabus:

If you are disappointed by your performance so far in Finance 4335, keep in mind that the final exam grade automatically double counts in place of a lower midterm exam grade. In case if 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, by all means, then set up a Zoom appointment with me.

A new study from Stanford University communications expert Jeremy Bailenson is investigating the very modern phenomenon of “Zoom Fatigue.” Bailenson suggests there are four key factors that make videoconferencing so uniquely tiring, and he recommends some simple solutions to reduce exhaustion.

I reexamined my analysis of the ad hoc exact and approximation methods from the review session today, and here is a screenshot from Excel of this problem:The fair coin toss involved payoffs of $25 and $100; the expected value of wealth under this coin toss is $62.50, and the standard deviation is half of the dispersion between $25 and $100, or $37.50. Applying the “exact” method, we get a risk premium of $6.25. In calculating the risk premium under the Arrow-Pratt (approximation) method, I incorrectly calculated variance as 75^2 = 5,625. Since variance is actually 37.5^2 = 1,406.25, the Arrow-Pratt equation produces a risk premium of $5.63, not $22.50. I knew that right away that one of the model inputs had to be wrong, and it was the variance input, which was 4x the actual variance of the coin toss.

I just posted the formula sheet for the Midterm 1 exam, which will be administered during class on Thursday, February 25. Actually, the formula sheet consists of two pages; the first page is a formula sheet, and the second page is a standard normal distribution table.

The exam consists of 3 problems worth 32 points each; I add 4 points to everyone’s scores so that that the maximum number of points possible is 100. On Thursday, plan on allocating no more than 80 minutes to complete the exam, and no more than 10 minutes to upload a single PDF of your written work which clearly demonstrates your conceptual grasp and ability to clearly explain, in plain English, how you arrived at all of your answers on the exam.

Risk-averse utility functions are characterized by diminishing marginal utility; thus, E(U(W)) < U(E(W));

Risk-neutral utility functions are characterized by constant marginal utility; thus, E(U(W)) = U(E(W)); and

Risk-loving utility functions are characterized by increasing marginal utility; thus, E(U(W)) > U(E(W)).

2. Full coverage insurance: Under a “full coverage” policy, the insured pays a premium that transfers all risk to the insurer; if the premium charged for such coverage is actuarially fair, then the optimal choice for all arbitrarily risk-averse decision-makers is to purchase a full-coverage policy; this result is commonly referred to as “Bernoulli Principle” (see http://risk.garven.com/2021/02/22/actuarially-fair-price-of-insurance-policy/).

3. Degree of risk aversion. For logarithmic and power utilities, we saw that all such utilities feature decreasing absolute risk aversion, which means that as one’s initial wealth increases, the one’s degree of aversion to a given risk declines; resulting in a lower risk premium at higher levels of initial wealth (see pp. 9-12 of http://fin4335.garven.com/spring2021/lecture6.pdf).

On Wednesday, I plan to be available in my virtual Zoom office from 3-5 pm CT in case if any students would like to stop by for a pre-exam chat.

A Finance 4335 student asked the following question earlier today:

Q: “How do you find the actuarially fair price (premium) for an insurance policy?”

Here’s the answer I provided, which I now share with all Finance 4335 students:

A: “The actuarially fair price (premium) corresponds to the expected value of the insurance indemnity; the indemnity is the amount of coverage offered by an insurance policy. Under “full coverage”, 100% of the loss is indemnified, and in such a case, the actuarially fair premium is equal to the expected value of the loss distribution.

For what it’s worth, the concept of “actuarially fair” insurance prices/premiums, along with implications for the demand for insurance, is explained in two previously assigned readings; e.g.,

on page 4 of the Supply of Insurance reading (just prior to the section entitled “Example 2: Correlated Identically Distributed Losses), the following sentence appears, “A premium that is equal to the expected outcome is called an actuarially fair premium”;

on page 30 of the Basic Economics: How Individuals Deal with Risk (Doherty, Chapter 2) reading, consider the following excerpt: “Ignoring transaction costs, an insurer charging a premium equal to expected loss would break even if it held a large portfolio of such policies. This premium could be called a fair premium or an actuarially fair premium, denoting that the premium is equal to the expected value of loss (sometimes called the actuarial value of the policy). The term fair is not construed in a normative sense; rather it is simply a reference point”; and

on page 43 of Doherty, Chapter 2, in the first sentence of the first full paragraph: “We know from the Bernoulli principle that a risk averter will choose to fully insure at an actuarially fair premium.””

In Finance 4335, students may use either Excel or hand-held calculators to calculate answers for problems that appear in problem sets and exams. However, in order to earn credit, students must show their work by providing logical explanations of how they get their answers, using properly formed English grammar, coupled with references to the appropriate theoretical concepts. The best examples I can think of how to “show work” appear in most of the various linked PDF documents consisting of solutions for class problems, problem sets, and sample exams @ http://risk.garven.com/?s=solutions.

Keeping this calculator policy in mind, do not upload Excel spreadsheets to Canvas related to exams and problem sets. Your grades are based solely on how well you explain and support the answers you provide in the PDF documents which you upload to Canvas.

The Electric Reliability Council of Texas (AKA “ERCOT”) issued a press release earlier today (see http://www.ercot.com/news/releases/show/225151) warning of possible “rotating outages” statewide for today, Monday, and Tuesday. Rather than roll the dice that we (students and faculty alike) will all have online access on Tuesday, from 2-3:15 pm, I have substituted my recorded lecture entitled “Finance 4335 – Decision Making under Risk and Uncertainty, part 4” in place of a real-time, synchronous class meeting that day. This recorded lecture is 72 minutes in length, and it is available from the Media Gallery section of the Finance 4335 Course Canvas page.

In the meantime, if you have questions about any aspect of Finance 4335, point your device’s browser to “appointment.garven.com” and set up a Zoom appointment with me there. Given the current statewide climate event (which I have nicknamed “Icepocalypse 2021”), I figure that the odds of just two people having a successful synchronous Zoom meeting (particularly on Tuesday) are much better than the odds of a few dozen people doing so.