“The Volatility Exchange™ (VolX®) plans to launch futures and options contracts based upon the realized volatility of U.S. equity indices. The futures version is named VolContract™ futures (VCs), which settle to the VolX indices known generically as RVOL™. The concept is both similar and dissimilar to the popular VIX® index and products marketed by the CBOE®. The two versions are similar in the notion that both VolX and CBOE are trying to provide volatility products to the marketplace. They are dissimilar because the VIX index and consequently VIX futures are based on implied volatility (the relative cost of options) while the RVOL index and consequently VCs are based on realized volatility (the actual, historical movement of the underlying index). VolContract futures are exchange‐tradable instruments that function similarly to a forward‐starting over‐the-counter volatility swap. They are expected to be launched on U.S. equity indices in 2013 and will come in two varieties: a 1‐month calculation period of realized volatility (1Vol™) and a 3‐month calculation period of realized volatility (3Vol™). For a detailed description of how these new instruments work, please visit the web site of The Volatility Exchange at www.volx.us. The goal of this paper is to demonstrate how a VC overlay can enhance the return and/or reduce the standard deviation of an equity portfolio. We chose the S&P 500 Total Return Index on the assumption that VolX will roll out products based upon this index.”

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“Since understanding and quantifying the evolution of security co-movements is critical for asset pricing and portfolio allocation, we investigate patterns and trends in correlations over time using weekly returns for large systems of developed markets (DMs) and emerging markets (EMs) during the period 1973-2009. We use the DECO, DCC, and BEKK correlation models, and develop a novel dynamic t-copula which generalizes the normal copula, to allow for dynamic tail dependence. We demonstrate that it is possible to overcome the well known dimensionality problems and compute correlation and tail dependence in international markets using large samples, without relying on factor models. Our results suggest that correlations have been significantly trending upward for both the DMs and EMs. Further, the evidence clearly contradicts the decoupling hypothesis. Although the tail dependence is increasing through time for both EMs and DMs, the level of the tail dependence is still very low at the end of our sample period for EMs as compared to DMs. Therefore, while the correlation analysis suggests that the diversification potential of EMs has largely disappeared, this is contradicted by our findings on tail dependence. Thus, even though diversification benefits might have lessened in the case of DMs, the case for EMs remains intact.”

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“This paper documents predictable time-variation in stock market Sharpe ratios. Predetermined
nancial variables are used to estimate both the conditional mean and volatility of equity returns,
and these moments are combined to estimate the conditional Sharpe ratio. In sample, estimated
conditional Sharpe ratios show substantial time-variation that coincides with the variation in ex
post Sharpe ratios and with the phases of the business cycle. Generally, Sharpe ratios are low
at the peak of the cycle and high at the trough. In out-of-sample analysis, using 10-year rolling
regressions, we can identify periods in which the ex post Sharpe ratio is approximately three times
larger than its full-sample value. Moreover, relatively naive market-timing strategies that exploit
this predictability can generate Sharpe ratios more than 70% larger than a buy-and-hold strategy.”

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“When the stock market dives 300 points, pundits start talking about increased volatility. By that reasoning, a broken arm could be referred to as increased flexibility.

“Thanks to the ever-inventive exchange-traded fund industry, volatility is not just a concept: It’s an investment opportunity. Why you would want to invest in volatility is another question — particularly, since not all the current offerings track volatility that well.

“Wall Street’s main measure of volatility is the CBOE Volatility Index, known as the VIX. Some people also call it the Fear Index, because most people associate big sharp market moves with scary down moves.

“Properly speaking, however, volatility refers to both up and down movements.”

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“Why are we interested to forecast the volatility of S&P 500, a proxy for the stock market? If
stock market volatility remained constant over time, forecasting volatility would have been
an easy task. If this were true, volatility measured through a measure such as standard
deviation in the current period could have been applied in the future. There are often wide
swings in the market followed by larger swings. This implies that volatility is not constant
over time and is often referred to as heteroscedasticity. Stock market volatility is important
for several reasons. Detection of volatility-trends would provide insight for designing
investment strategies and for portfolio management. The volatility of S&P 500 is important
to derive the price of an option on S&P 500 index for the remaining life of the option. The
stock market volatility forecast is also an important input for dynamic portfolio insurance
strategies.

“Forecasting stock market volatility would be useful for holders and writers of options on the
S&P 500 index. Gains on straddles or spreads depend on the volatility of underlying security.
The more volatile a security is, the larger the gain to the straddle-trader or the spread-trader.
The spread-trader and the straddle-trader are not concerned about the direction of change;
rather they are concerned about the fluctuations in prices.”

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“An investor considering an allocation to managed futures recently asked me if the Lehman Brothers’ bankruptcy and the collapse of Bernard Madoff’s fraudulent investment firm had been good or bad for the futures industry. There is no single or easy answer to that question.

“In 2008, these events shocked the global markets in different ways. Lehman and Madoff were “bad” in terms of the damage they inflicted on people’s portfolios and how they eroded investor confidence. The fallout from these losses will likely be felt for years to come.

“The important lesson learned—namely the need for a well-diversified, transparent, reasonably liquid portfolio—is the “good” thing that sprang from these events and the market turmoil of recent years.”

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“We propose using the price range, a recently-neglected volatility proxy with a long history in finance, in the estimation of stochastic volatility models. We show both theoretically and empirically that the log range is approximately Gaussian, in sharp contrast to popular volatility proxies, such as log absolute or squared returns. Hence Gaussian quasi-maximum likelihood estimation based on the range is not only simple, but also highly efficient. We illustrate and enrich our theoretical results with a Monte Carlo study and a substantive empirical application to daily exchange rate volatility. Our empirical work produces sharp conclusions. In particular, the evidence points strongly to the inadequacy of one-factor volatility models, favoring instead two-factor models with one highly persistent factor and one quickly mean reverting factor.”

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“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.”

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“Assessing risk is an important part of investing. One commonly
used measure of risk is volatility, which measures
the variability of a security’s return through time. If
Security A and Security B have the same expected return
but Security B has greater variability of return, Security B
is more volatile than Security A. Given an equal return
most investor’s would prefer a security with less volatility,
which means that investors expect a higher return on an
investment when it carries a higher level of volatility.
This paper takes a close look at the basics of volatility, discusses
why it matters in relation to portfolio management,
and suggests some methods for managing and controlling
the impact of volatility. In highly volatile markets,
heightened emotions can lead to clouded judgment.
Controlling the amount of volatility within your portfolio
can allow for more prudent decisions.”

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“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.”

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