"Best New Quantitative Finance Book of the Year" (Wilmott Awards 2006)
With Inside Volatility Arbitrage: The Secrets of Skewness, Alireza Javaheri provides one of the most comprehensive looks at this important topic. Divided into three informative sections, this guide focuses on developing methodologies for estimating stochastic volatility (SV) parameters from the stock-price time-series under a classical framework.
In Section 1: The Volatility Problem, Javaheri introduces the concept of various parametric SV models and examines literature on the subject of non-deterministic volatility. Here, you'll receive in-depth information on the relationship between volatility and the stock and derivatives markets, detailed insights on Brownian motion for stock price returns, and option pricing techniques such as inversion of the Fourier transform and mixing Monte Carlo. You'll also gain invaluable knowledge on a variety of models, from local volatility and stochastic volatility models to pure-jump models.
In Section 2: The Inference Problem, Javaheri tackles the notion of inference (or parameter estimation) for parametric SV modelsbriefly analyzing cross-sectional inference and then focusing on time-series inference. Here you'll discover how to estimate model parameters using two possible sets of data: options prices and historic stock prices.
Finally, in Section 3: The Consistency Problem, Javaheri shows you how to apply parametric inference methodologies to a few assets. He also reveals why you should question the consistency of information contained in the options markets and the stock market.
Filled with in-depth insights, proven models, and illustrative charts, Inside Volatility Arbitrage will help you realize when "skewness" may present valuable trading opportunities, as well as why it can be so profitable.
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Most Helpful Customer Reviews
19 of 19 people found the following review helpful:
3.0 out of 5 stars
The Good, The Bad, and The Ugly,
By A Rational Investor (New York, NY) - See all my reviews
This review is from: Inside Volatility Arbitrage : The Secrets of Skewness (Hardcover)
The Good: Very good review of stochastic volatility models, Heston, SVJ, etc. I say review because if you've never seen SV models before, this is most definitely not the best place to learn about them for the first time. I would instead recommend online sources (Nimalin Moodley's paper is a great introduction to the Heston Model), the Gatheral lectures at NYU and the corresponding book "The Volatility Surface" by Jim Gatheral - in fact, I recommend working through the problems from his course notes while working through the book, it will improve your experience dramatically. What Javaheri does better than Gatheral is dive into the nitty gritty of applied model calibration for stochastic volatility models, i.e. the content of Chapter 2.
The Bad: The title stinks. This book is not a sneak peek into volatility arbitrage strategies. There is a very small amount of content related to practical trading strategies, and none (zero) related to options arbitrage. Don't look here for useful trading strategies, don't expect this to show you how to run an options trading desk. The Ugly: Chapter 3. This is just not pretty. I'm not a domain expert - I am a physicist and computer scientist with an MBA, and I am still learning when it comes to financial engineering. But I know enough finance and math to shake my head when I read this chapter. This guy looks at a few year's worth of data at a time and concludes that the options market is over-estimating skew relative to historical time series. Umm, data sufficiency issues here? He then acknowledges this on pages 197/198 on the Peso Theory before going on to vaguely describe a few trades, i.e. going short skew or long kurtosis. Now in addition to the data sufficiency issues, I won't say the trades he describes are *bad* arbitrage trades, since they aren't even arbitrage trades *at all*. An inconsistency between two markets is only an arbitrage if the parameters in question are tradable in both markets. The trades aren't even well fleshed out - not surprising, given the brevity of this section. Finally, on page 219 in the "Word of Caution" section, he throws in the comment that "the skewness transaction described in this chapter is more similar to selling insurance than to an arbitrage." Oh. Gee. Might have told your editors that before they named the book. Quick note on skew trading: if you had been in the skewness trade described in Chapter 3, I'm fairly certain you would have gotten wiped out in late 2008. There are other ideas out there of safer ways to extract some rents from the skewness discrepancy Javaheri observes. These trades are also long kurtosis to mitigate crash risk, though they still bear volatility term structure risk, and deserve a much more robust analysis than Javaheri gives to his strategies. Search around on the Wilmott forums if you are curious. Summary: Chapter 2 on the Inference Problem and model fitting has lots of good material, and the review in Chapter 1 of volatility models covers all the bases, while being overly dense and terse unless you are already basically familiar with most of the models described. Chapter 3 should be read as an observation of potential discrepancies in the market, with a sketch of some very basic options trading ideas, nothing more. Wiley Finance editors should be sacked for the title of this book.
16 of 19 people found the following review helpful:
1.0 out of 5 stars
Title is very misleading,
By
This review is from: Inside Volatility Arbitrage : The Secrets of Skewness (Hardcover)
DO NOT buy this book based on the title - it has little to do with skew and how to take advantage of it. Do buy it if you want a review of mathematical volatility models.
60 of 80 people found the following review helpful:
3.0 out of 5 stars
Practitioners Beware,
By SR (NYC) - See all my reviews
Amazon Verified Purchase(What's this?)
This review is from: Inside Volatility Arbitrage : The Secrets of Skewness (Hardcover)
I've ordered dozens of books from Amazon over the years and this is the 1st one I'm returning. The title is nonsense -- it should read Stochastic Volatility Models for Phds.
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