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Archive for the ‘Implied volatility’ Category

Volatility Exposure for Strategic Asset Allocation

24 Oct

Article by: Marie Brière, Alexandre Burgues, and Ombretta Signori
Published by: Universite Libre de Bruxelles
Date: 2008

“This paper examines the advantages of incorporating strategic exposure to equity volatility into the investment-opportunity set of a long-term equity investor. We consider two standard volatility investments: implied volatility and volatility risk premium strategies. To calibrate and assess the risk/return profile of the portfolio, we present an analytical framework offering pragmatic solutions for long-term investors seeking exposure to volatility. The benefit of volatility exposure for a conventional portfolio is shown through a mean / modified Value-at-Risk portfolio optimization. Pure volatility investment makes it possible to partially hedge downside equity risk, thus reducing the risk profile of the portfolio. Investing in the volatility risk premium substantially increases returns for a given level of risk. A well calibrated combination of the two strategies enhances the absolute and risk-adjusted returns of the portfolio.”

Full article: Link

 
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Posted in Implied volatility, Investing ideas

 

Long-Memory versus Option-Implied Volatility Predictions

17 Oct

Article by: Kai Li
Published by: The Journal of Derivatives
Date: Spring 2002

“Volatility is a critical parameter in virtually all option pricing models. But the closer we look at volatility, the harder it seems to be to model it correctly. The constant volatility assumption of early pricing models is clearly inadequate. GARCH family models make volatility a function of the asset price process, and stochastic volatility models bring in a second stochastic factor that affects volatility movements. These approaches make sense in theory, but empirically volatility shocks seem to be too persistent to be consistent with them. A further confounding factor is that implied volatilities extracted from option prices in the market are widely thought to give more accurate predictions of future realized volatility, but don’t obey any of these models exactly. In this article, Li examines a volatility model with “long memory,” meaning that it can be made to fit the slow-decay feature of market volatilities. He also introduces a much more extensive data series, with intraday observations every five minutes. Using such a dense price series, realized volatility becomes observable, and the vast number of data points allows precise estimation of model parameters. For exchange rates on the deutsche mark, the yen and the British pound, the ARFIMA (“Autoregressive Fractionally Integrated Moving Average”) model is shown to give a better fit to volatility behavior than the alternative model, and it beats implied volatility substantially in the standard tests of forecasting performance.”

Full article (This article requires a subscription or payment) : Link

 
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Posted in Implied volatility, Realized volatility

 

Risk and Volatility: Econometric Models and Financial Practice

14 Oct

Article by: Robert F. Engle III
Nobel lecture
Date: 8 Dec 2003

“The advantage of knowing about risks is that we can change our behavior to
avoid them. Of course, it is easily observed that to avoid all risks would be impossible;
it might entail no flying, no driving, no walking, eating and drinking
only healthy foods and never being touched by sunshine. Even a bath could
be dangerous. I could not receive this prize if I sought to avoid all risks. There
are some risks we choose to take because the benefits from taking them exceed
the possible costs. Optimal behavior takes risks that are worthwhile. This
is the central paradigm of finance; we must take risks to achieve rewards but
not all risks are equally rewarded. Both the risks and the rewards are in the future,
so it is the expectation of loss that is balanced against the expectation of
reward. Thus we optimize our behavior, and in particular our portfolio, to
maximize rewards and minimize risks.

“This simple concept has a long history in economics and in Nobel citations.
Markowitz (1952) and Tobin (1958) associated risk with the variance in
the value of a portfolio. From the avoidance of risk they derived optimizing
portfolio and banking behavior. Sharpe (1964) developed the implications
when all investors follow the same objectives with the same information. This
theory is called the Capital Asset Pricing Model or CAPM, and shows that
there is a natural relation between expected returns and variance.”

Full article (PDF): Link

 
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Posted in Hedging, Implied volatility, Investing ideas, Trading ideas

 

The VIX as a Fix: Equity Volatility as a Lifelong Investment Enhancer

03 Oct

Article by: Michael Sloyer and Ryan Tolkin
Published by: Duke University
Date: 2008

“The VIX, a measure of the implied volatility of S&P 500 index options, is the
premier gauge of investor sentiment and market volatility. This analysis examines the
effectiveness of adding the VIX to passively managed equity-bond portfolios.
Furthermore, this study extends the existing literature by examining the efficacy of the
VIX in a life-cycle investing context. Due to the large negative correlation between the
VIX and the major equity indices, we find that a relatively small allocation to the VIX
would have significantly improved the risk-return profile of standard equity-bond
portfolios from 1986 through 2007. Additionally, we find that younger investors (i.e.
investors with higher risk tolerances and thus more exposure to equities rather than fixed
income) will benefit from having greater exposure to the VIX.”

Full article (PDF): Link

 
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Posted in Implied volatility, Investing ideas

 
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