Today I just went quicky through a book by Augen about technical trading with options. This seems to be a quite interesting book, which contains not only interesting options trading strategies (not to be used blindly, but to be studied further and incorporated into a general investing strategy), but also information about the markets and mathematical models.
The book can be bought from any serious online book store, or downloaded from the internet. I’ll copy here some tidbits from the book.
From the introduction:
Jeff Augen, currently a private investor and writer, has spent more than a decade building a unique intellectual property portfolio of algorithms and software for technical analysis of derivatives prices. His work includes more than one million lines ofcomputer code reflecting powerful new strategies for trading equity, index, and futures options.
Augen has a 25-year history in information technology. As a co-founding executive of IBM’s Life Sciences Computing business, he defined a growth strategy that resulted in $1.2 billion of new revenue, and he managed a large portfolio of venture capital investments. From 2002 to 2005, Augen was President and CEO of TurboWorx, Inc., a technical computing software company founded by the chairman of the Department of Computer Science at Yale University. He is author of Bioinformatics in the Post-Genomic Era: Genome, Transcriptome, Proteome, and Information-Based Medicine (Addison-Wesley, 2004). Much of his current work on options pricing is built on algorithms for predicting molecular structures that he developed as a graduate student.
From the preface:
This book is designed to bridge the gap by marrying pricing theory to the realities of the market. Our discussion will include many topics not covered elsewhere:
– Strategies for trading the monthly options expiration cycle
-The effects of earnings announcements on options volatility and pricing
– The complex relationship between market drawdowns, volatility, and disruptions to put-call parity
-Weekend/end-of-month effects on bid-ask spreads and volatility
A cornerstone of our discussion will be a new set of analytical tools designed to classify equities according to their historic price-change behavior. I have successfully used these tools to trade accounts as small as $80,000 and as large as $20M. … Today I can comfortably generate a return that would make any investment bank or hedge fund proud
From Chapter 1:
… My goal was to develop an investment strategy based on the fundamental mathematical properties that describe financial markets …
… The Chairman of the U.S. Federal Reserve, the largest central bank on Earth, declared publicly that he could not explain why the yield on ten-year treasury notes had fallen 80 basis points during a time frame marked by eight consecutive quarter-point increases in the Federal Funds rate (the interest rate charged on overnight loans between banks). He used the word “conundrum” to describe the phenomenon that continued, to the surprise of many, for another year as rates continued rising … [ntzung: maybe it’s because long-terms prospects for the economy are not that good ?!]
… Unfortunately, the lack of well-defined mathematical models that describe the world’s economy is more than an academic problem. In
June 2005, for example, GLG Partners, the largest hedge fund in Europe, admitted that flaws in the mathematical model it used to price
complex credit derivative products caused a 14.5% drop in its Credit Fund over the span of a single month.
… During the past several years, many billions of dollars have been lost in self-destructing hedge funds with faulty trading models. The risk is enormous. The U.S. gross domestic product (GDP) is approximately $13 trillion, the world’s GDP is $48 trillion, and the world’s derivatives markets are generally estimated to be worth more than $300 trillion. It’s no longer possible to recover from a true crash.
… it is often more important to have an accurate view of the potential change in a stock’s implied volatility than to be able to predict short-term changes in its price. Volatility is also much easier to predict than price.
From Chapter 5:
[ntzung: On page 112 of the book there is a chart which shows how much the price of an option can vary intraday. The variation can be really enormous (e.g. low = $0.7 and high = $3.5, for the same option, during a day). This very high variation makes options trading strategies very diffrent from stocks trading strategies].
… For stocks that exhibit occasional large spikes followed by calm periods or trend reversals, it often makes sense to use the spikes as selling (or
buying) points [for options: if for example an options varies 25%/day on the average, then when it’s up > 100% on a day may consider it as a selling oppertunity ?]
… Short straddles and strangles profit from time decay.
… the VIX rose from 11 to 19 in less than one hour on February 27, 2007. … Research in Motion stock, which fell approximately $7.00 during the February 27 decline while its call option implied volatility rose from 33% to 44%. Already-significant at-the-money call losses nearly doubled the very next day, when implied volatility returned to 33%.
[Chapter 6 discusses complex options strategies & hedging]
From Chapter 7:
[Chapter 7: exploits earnings-related rising volatility & post-earnings volatility collapse, etc.]
… Although earnings-related price spikes for SLB are generally moderate, implied volatility has repeatedly doubled to nearly 70%. This increase is large enough, in most cases, to protect against a 2.5 standard deviation price spike. Schlumberger has an unusual reporting strategy—each earnings announcement depicted in the table was just before the market open on the final trading day (Friday) of an
… Because the options market is open for 6.5 hours each day and closed for 17.5 hours, the percentage of value lost each evening while the market is closed rapidly becomes a driving force that underlies option pricing.