Trade the following stock option spreads. You can either long or short each spread. Record what contracts you traded, the prices, whether you bought/sold them, and ultimately whether you are long/short the underlying stock or the stock's volatility. Upload this to dropbox. We'll close out of the trades next week and record our profit/loss.
Make sure you use a different underlying stock for each spread trade. Lastly, pay attention to the bid/ask (and the volume of trading) in your options. You generally don't want to trade very illiquid options.
1. Trade a butterfly spread.
2. Trade one of the following: bull call, bear call, bull put, or bear put spread.
3. Trade a straddle.
4. Trade a covered call.
5. Trade a protective put.
1. Trade a 3:2:1 crack spread.
2. Trade a 1:1:1 crush spread.
3. Trade an NG calendar spread (where you are trading the storage spread and convenience yield.)
Record the prices at which you traded and calculate the refining margin for the crack and crush spread. After a week or so we'll close the trade and calculate the closing refining margin (and our profit/loss). However, upload the prices at which you traded and the refining margins to the D2L dropbox today, and we'll upload the prices at which you closed the contracts later.
Also be sure to trade liquid contracts (look at volume and open interest). You'll probably want to use front-month contracts.
You can buy or sell the 3:2:1 crack spread, meaning either:
- buy 3 crude oil, and sell 2 gasoline and 1 heating oil
- sell 3 crude oil, and buy 2 gasoline and 1 heating oil
Similarly for the 1:1:1 crush spread you can either:
- buy 1 soybean contract, and sell 1 soy meal and soy oil contract
- sell 1 soybean contract, and buy 1 soy meal and soy oil contract
Be sure to read this post about the crush spread: http://www.complete-markets.com/2012/11/fin-376-trading-crush-spread.html
For the calendar spread, you buy NG for delivery one month and sell NG for delivery in another month.
Complete the following trades and answer the questions below. Upload your answers to the D2L dropbox.
1. Sell 3000 barrels of oil for February 2015 delivery. How many contracts is this (and for what ticker)?
2. Buy 2 Eurodollar futures contracts. Will your contracts gain in value when LIBOR rates increase or decrease?
3. Trade (buy or sell) May 2015 Soybeans. How many bushels have you traded, and when is the last trade data and delivery point?
4. Trade April 2015 copper. When you enter into the contract what is the open interest, and what does open interest mean?
5. Sell 2 January 10-year Treasury note futures contracts. For each contract, what is the contract size, what must be delivered, and what is the minimum tick size?
6. Buy 4 December E-mini S&P 500 futures contracts. What is the multiplier on each contract, and how much of the S&P 5000 have you bought (in $)?
See this web page on the empirical relationship between an option and its underlying stock's price. The page contains an assignment. To complete the assignment the original .Rmd file is likely useful.
I have been paying around with the R package twitteR. It is an R interface to the Twitter web API. I used it to search for the 1000 latest tweets containing $TSLA (Tesla's stock ticker). I then removed tweets from outside North America, geolocated them, and plotted them on a map. The map is below.
As we would expect, most tweets are from urban areas -- particularly from Washington D.C to Boston, Florida, and California. Surprisingly there is only one tweet from Seattle, but quite a few from Atlanta. Also, there are many more tweets from the southern states compared to the upper midwest.
When I get a chance (hopefully soon), I'll post the code/packages I used to create the map.
Here is a spreadsheet I created to test the effect of various stock prices and market caps on price and value weighted indices. You should create a similar spreadsheet (though formatted better -- the first sheet should be a summary with a description of what you are doing and the important results, i.e. the correlations). You can also add in interesting charts and possibly an equally-weighted index.
Note, by pressing 'F9' you'll recalculate the '=RAND()' functions, which will give you entirely new correlations. You could hit 'F9' many times, recording the resulting correlations, and get a Monte Carlo estimate of the correlation distributions. A macro may be effective in doing this.
I have posted a new working paper of mine to the 'Working Paper' section. The paper, Parameter Variation & the Components of Natural Gas Price Volatility, hypothesizes that parameters linking natural gas returns to fundamental variables will tend to change as market participants learn. I therefore estimate the parameters using the Kalman filter. I also decompose conditional natural gas volatility into portions attributable to each variable. The abstract is below.
Estimating a static coefficient for a deseasoned gas storage or
weather variable implicitly assumes that market participants react
identically throughout the year (and over each year) to that variable.
In this analysis we model natural gas returns as a linear function of
gas storage and weather variables, and we allow the coefficients of this
function to vary continuously over time. This formulation takes into
account that market participants continuously try to improve their
forecasts of market prices, and this likely means they continuously
change the scale of their reaction to changes in underlying variables.
We use this model to also calculate conditional natural gas volatility
and the proportion of volatility attributable to each factor. We find
that return volatility is higher in the winter, and this increase is at-
tributable to increases in the proportion of volatility due to weather
and natural gas storage. We provide time series estimates of the chang-
ing proportion of volatility attributable to each factor, which is useful
for hedging and derivatives trading in natural gas markets.