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Archive for the ‘Realized volatility’ Category

VolX contemplates rates, metals and stock volatility contracts

13 Dec

Article by: Siân Williams
Published by: Futures and Options Intelligence
Date: 13 Dec 2010

“The Volatility Exchange (VolX) is considering launching contracts on the volatility of metals, rates and stock indices, its chief executive told FOi.

“The exchange has a patented methodology which calculates realised volatility over a specific time period. It uses closing prices of an asset over a defined period of one, three or twelve months to calculate the asset’s volatility over that period. It contrasts with the VIX methodology, which uses options to calculate implied volatility. Implied volatility is based on perceived volatility and realised volatility is actual volatility.

“The products are similar to volatility swaps and variance swaps, which are…”

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Posted in Hedging, Realized volatility, Trading ideas

 

On the informational content of implied volatility

10 Dec

Article by: Juan Carlos Sosa
Published by: Boston College
Date: 1 Jan 2000

“Previous literature examines whether the volatility estimates implicit in option prices constitute accurate forecasts of future volatility for the underlying asset. Most studies address two questions. Do Black-Scholes implied volatilities predict future volatilities? Do they incorporate all past time series information? The empirical answers to both questions are conflicting. However, previous studies typically find that implied volatilities overestimate future volatilities. Christensen & Prabhala (98) provide evidence of a structural shift in the pricing mechanism of OEX options around the Crash of ’87. In contrast with the pre-Crash results of Canina & Figlewski (93), CP show that after the Crash implied volatilities predict future volatilities and dominate moving average forecasts. They also show that implied volatilities are unbiased, and that errors-in-variables is responsible for previous bias findings. The objective of this dissertation is fourfold. First, we implement encompassing regressions on a recent sample of OEX options. Our full-sample findings are consistent with other studies that support the informational efficiency of implied volatilities in recent years. However, the regressions are very sensitive to the degree of moneyness of implied volatility. Furthermore, we find that the degree of predictive power of implied volatility is strongly time-frame dependent. Second, we show that implied volatilities do overestimate realized volatilities and that CP’s unbiasedness finding is due to an ill choice of instrument, in addition to a low test power. Third, we attempt to correct for potential misspecification in the encompassing regressions and find that lagged implied volatility is important. Yet, we show that the significance of lagged implied volatility is not a symptom of market inefficiency, but of skewness-related model error in implied volatility estimates. Finally, we assess the economic value of informational efficiency via a trading analysis, and find it to be quite limited. In summary, we find that at-the-money implied volatilities are biased estimators of realized volatility, that they suffer from substantial model error, that their predictive power is time-frame dependent, and that moving average estimators perform just as well from an economic point of view.”

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Posted in Implied volatility, Realized volatility

 

Forecasting Daily Volatility Using Range-based Data

23 Nov

Article by: Yuanfang Wang, Matthew C. Roberts
Published by: The Ohio State University
Date: 1 Aug 2004

“Users of agricultural markets frequently need to establish accurate representations of expected future volatility. The fact that range-based volatility estimators are highly efficient has been acknowledged in the literature. However, it is not clear whether using range-based data leads to better risk management decisions. This paper compares the performance of GARCH models, range-based GARCH models, and log-range based ARMA models in terms of their forecasting abilities. The realized volatility will be used as the forecasting evaluation criteria. The conclusion helps establish an efficient forecasting framework for volatility models.”

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Posted in Realized volatility

 

Range-Based Estimation of Stochastic Volatility Models or Exchange Rate Dynamics are More Interesting Than You Think

16 Nov

Article by: Sassan Alizadeh, Michael W. Brandt, Francis X. Diebold
Published by: University of Pennsylvania, The Wharton School
Date: 20 Dec 1999

“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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Posted in Investing ideas, Realized volatility

 
 
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