1,721,017 research outputs found
Long memory and periodicity in intraday volatilities of stock index futures
Full text linkThis paper investigates the intraday volatility pattern of the E-mini SP500, quoted at the Chicago Mercantile Exchange, one of the most traded American Stock Index futures. The data set consists of round-the-clock hourly returns. The squared (and absolute) returns are characterized by long memory and periodicity. In order to jointly model the long memory and the periodic components in the returns volatility we introduce two new parameterizations. The Fractionally Integrated Periodic EGARCH (FI-PEGARCH) and the Seasonal Fractional Integrated Periodic EGARCH (SFI-PEGARCH). For both models we compute the population kurtosis and the autocorrelation function of power transformations of absolute returns. We find that during the Asian and European trading time the volatility is lower than during the American trading time when we observe a sharp increase. The results seem to confirm the fact that hourly returns sampled over the 24 hours across different markets are characterized by a strong seasonal pattern with a statistically significant persistence. Finally we present the in-sample and out-of-sample forecasts results of unrestricted and restricted long memory periodic volatility models
Long memory and Periodicity in Intraday Volatilities of Stock Index Futures
No full textThis paper investigates the intraday volatility pattern of the E-mini SP500, quoted at the Chicago Mercantile Exchange, one of the most traded American Stock Index futures. The data set consists of round-the-clock hourly returns. The squared (and absolute) returns are characterized by long memory and periodicity. In order to jointly model the long memory and the periodic components in the returns volatility we introduce two new parameterizations. The Fractionally Integrated Periodic EGARCH (FI-PEGARCH) and the Seasonal Fractional Integrated Periodic EGARCH (SFI-PEGARCH). For both models we compute the population kurtosis and the autocorrelation function of power transformations of absolute returns. We find that during the Asian and European trading time the volatility is lower than during the American trading time when we observe a sharp increase. The results seem to confirm the fact that hourly returns sampled over the 24 hours across different markets are characterized by a strong seasonal pattern with a statistically significant persistence. Finally we present the in-sample and out-of-sample forecasts results of unrestricted and restricted long memory periodic volatility models
Long memory and Periodicity in Intraday Volatility
No full textIntraday return volatilities are characterized by the contemporaneous presence of periodicity and long memory. This paper proposes two new parameterizations of the intraday volatility: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional Integrated Periodic EGARCH, which provide the required flexibility to account for both features. The periodic kurtosis and periodic autocorrelations of power transformations of the absolute returns are computed for both models. The empirical application shows that volatility of the hourly Emini S&P 500 futures returns are characterized by a periodic leverage effect coupled with a statistically significant long-range dependence. An out-of-sample forecasting comparison with alternative models shows that a constrained version of the FI-PEGARCH provides superior forecasts. A simulation experiment is carried out to investigate the effects
that sample frequency has on the fractional differencing parameter estimate
Discretete-time affine term structure models: an econometric formulation
No full textDiscrete-time Affine Term Structure Models can be expressed in
AR (1)-ARCH form, but it is not possible to get a non-negative vari-
ance equations simply by restricting the parameters. In this paper
we resort to a distribution assumption in order to assure the variance
to be non-negative. We present a complete formulation for one-factor
and multi-factor models with Gamma conditional noise distribution.
This way we get a well defined volatility process that avoids any prob-
lem both in generating processes and in computing the conditional
likelihoods of observations. The log-likelihood function is derived
for one- and multi-factor specifications. Moreover, we implement a
one-factor estimation both with simulated and US interest rate data.
Finally, we compare the estimation results with a standard ATSM
with Gaussian disturbances
Intraday and Day-of-the-Week Effects in Returns and Volatilities of Stock Index Futures.
No full textProposed Coal Power Plants and Coal-To-Liquids Plants: Which Ones Survive and Why?
No full textThe increase of oil and natural gas prices since the year 2000 stimulated the planning and construction of new coal-fired electricity generating plants and coal-to-liquids (CTL) plants in the US. However, many of these projects have been canceled or abandoned since 2007. Using a set of 145 proposed coal power plants and 25 CTL plants, the determinants that influence the decision to abandon a project or to proceed with it are examined using binary data models and 20 regressors. In the case of coal power plants, the number of searches performed on Google relating to coal power plants, the project duration and the prices of alternative fuels for electricity generation are found to be statistically significant at the 5% level. As for CTL plants, the political affiliation of the state governor is the only variable significant at the 5% level across several model specifications. An out-of-sample exercise confirms these findings. These results also hold with robustness checks considering alternative Google search keywords, the potential effects of the recession between 2008 and 2009 and the inclusion of the two dimensions of the Dynamic-Weighted Nominate (DWN) database
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