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The Extreme Value Method for Estimating the Variance of the Rate of Return

16 Dec 2011

Article by: Michael Parkinson
Published by: The Journal of Business
Date: 26 Feb 2009

“The random walk problem has a long history. In fact, its application to the movement of security prices predates the application to Brownian motion. And now it is generally accepted that, at least to a good approximation, ln (S), where S is the price of a common stock, follows a random walk. The diffusion constant characterizing that walk for each stock thus becomes an important quantity to calculate. In Section II, we describe the general random walk problem and show how the diffusion constant is traditionally estimated. In Section III, we discuss another way to estimate the diffusion constant, the extreme value method. In Section IV, we compare the traditional and extreme value methods and conclude that the extreme value method is about 21/2-5 times better, depending on how you choose to measure the difference. In Section V, we discuss the use of this method for the estimation of the variance of the rate of return of a common stock.”

Full article (PDF): Link

 
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Posted in Realized volatility

 

VIX ETN: Ineffective as Both Short-Term, Long-Term Play

28 Nov 2011

Article by: Bill Luby
Published by: Seeking Alpha
Date: 2 Oct 2009

“During the last month, the iPath S&P 500 VIX Short-Term Futures ETN (VXX) has been turning over an average of 1.3 million shares per day. I am certain that a fair portion of the purchases of VXX have come from investors who have sought to protect their portfolios from an increase in volatility and/or downturn in stocks.

“Unfortunately, VXX has considerable shortcomings, both as a short-term and a long-term play.”

Full article: Link

 
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Posted in Implied volatility

 

FORECASTING VOLATILITY

03 Nov 2011

Article by: Stephen Figlewski
Published by: New York University Stern School of Business
Date: 24 Apr 2004

“This monograph puts together results from several lines of research that I have pursued
over a period of years, on the general topic of volatility forecasting for option pricing
applications. It is not meant to be a complete survey of the extensive literature on the subject,
nor is it a definitive set of prescriptions on how to get the best volatility forecast. While at the
outset, I had hoped to find the Best Method to obtain a volatility input for use in pricing options,
as the reader will quickly determine, it seems that I have been more successful in uncovering the
flaws and difficulties in the methods that are widely used than I have been in determining a
single optimal strategy myself.

“Since I am not revealing the optimal approach to volatility forecasting, the major value of
this work, if any, is more to share with the reader a variety of observations and thoughts about
volatility prediction, that I have arrived at after investigating the problem from a number of
different angles. Two major themes emerge, both having to do with the connection, or perhaps
more correctly, the possibility of a disconnection between theory and practice in dealing with
volatility prediction and its role in option valuation. Two general classes of theories are
involved.

“First, there is the statistical theory involved in modeling price behavior in financial markets. In
Chapter I we bring out the distinction between a physical process and an economic process in
terms of the stability of their internal structure and the prospects for making accurate predictions
about them. We argue that simply applying the theoretical estimation methodology appropriate
for physical processes to the economic process of price behavior in a financial market can lead
one to build models that are too complex and hold inappropriately high expectations about the
potential accuracy of volatility forecasts from those models.

“The second area where conflict between theory and practice arises is in the use of implied
volatility from option market prices. The conflict comes from the disparity between the trading
strategies arbitrage-based derivatives valuation models assume investors follow and what actual
market participants do. In theory, the implied volatility is the market=s well-informed prediction
of future volatility. In practice, however, the arbitrage trading that is supposed to force option
prices into conformance with the market=s volatility expectations may be very hard to execute. It
will also be less profitable and entail more risk than simple market making that maximizes order
flow and earns profits from the bid-ask spread. The latter, however, does little to enforce
theoretical pricing in the face of the forces of supply and demand in the market.
In both cases, I try to point out important implications for estimating volatility that tend to be
overlooked by those following the more traditional lines of thought. I hope the reader will find
some of these insights to be of value.”

Full article (Large PDF): Link

 
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Posted in Implied volatility, Realized volatility

 

Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure

29 Oct 2011

Article by: Marcel P. Visser
Published by: Korteweg-de Vries Instute for Mathematics, University of Amsterdam
Date: 14 Oct 2008

“This paper decomposes volatility proxies according to upward and downward price
movements in high-frequency financial data, and uses this decomposition for forecasting
volatility. The paper introduces a simple Garch-type discrete time model that incor-
porates such high-frequency based statistics into a forecast equation for daily volatil-
ity. Analysis of S&P 500 index tick data over the years 1988–2006 shows that taking
into account the downward movements improves forecast accuracy significantly. The
R2 statistic for evaluating daily volatility forecasts attains a value of 0.80, both for
in-sample and out-of-sample prediction.”

Full article (PDF): Link

 
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Posted in Implied volatility, Realized volatility

 
 
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