Memory in returns and volatilities of commodity futures’ contracts

Various authors claim to have found evidence of stochastic long memory behavior in futures’ contract returns using the Hurst statistic. This paper reexamines futures’ returns for evidence of persistent behavior using a biased-corrected version of the Hurst statistic and an estimate of the long-memory parameter based on the process spectrum. Results based on these new methods provide no evidence for persistent behavior in futures’ returns. However, it finds overwhelming evidence of long memory behavior for the volatility of futures’ returns. This finding adds to the emerging literature on persistent volatility in financial markets and suggests the use of new methods of forecasting volatility, assessing risk, and optimizing portfolios in futures’ markets.info:eu-repo/semantics/publishedVersio

Model selection and forecasting of long-range dependent processes

Fractionally integrated autoregressive moving-average (ARFIMA) models have proved useful tools in the analysis of time series with long-range dependence. However, little is known about various practical issues regarding model selection and estimation methods, and the impact of selection and estimation methods on forecasts. By means of a large-scale simulation study, we compare three different estimation procedures and three automatic model-selection criteria on the basis of their impact on forecast accuracy. Our results endorse the use of both the frequency domain Whittle estimation procedure and the time-domain approximate MLE procedure of Haslett and Raftery in conjunction with the AIC and SIC selection criteria, but indicate that considerable care should be exercised when using ARFIMA models. In general, we find that simple ARMA models provide competitive forecasts. Only a large number of observations and a strongly persistent time series seem to justify the use of ARFIMA models for forecasting purposes..info:eu-repo/semantics/publishedVersio

The Local Whittle Estimator of Long Memory Stochastic Volatility

We propose a new semiparametric estimator of the degree of persistence in volatility for long memory stochastic volatility (LMSV) models. The estimator uses the periodogram of the log squared returns in a local Whittle criterion which explicitly accounts for the noise term in the LMSV model. Finite-sample and asymptotic standard errors for the estimator are provided. An extensive simulation study reveals that the local Whittle estimator is much less biased and yields more accurate confidence intervals than the widely-used GPH estimator. In an empirical analysis of the daily Deutschemark/Dollar exchange rate, the new estimator indicates stronger persistence in volatility than the GPH estimator, provided that a large number of frequencies is used.Statistics Working Papers Serie

The Local Whittle Estimator of Long Memory Stochastic Volatility

We propose a new semiparametric estimator of the degree of persistence in volatility for long memory stochastic volatility (LMSV) models. The estimator uses the periodogram of the log squared returns in a local Whittle criterion which explicitly accounts for the noise term in the LMSV model. Finite-sample and asymptotic standard errors for the estimator are provided. An extensive simulation study reveals that the local Whittle estimator is much less biased and that the finite-sample standard errors yield more accurate confidence intervals than the widely-used GPH estimator. The estimator is also found to be robust against possible leverage effects. In an empirical analysis of the daily Deutsche Mark/US Dollar exchange rate, the new estimator indicates stronger persistence in volatility than the GPH estimator, provided that a large number of frequencies is used.Statistics Working Papers Serie

Structure strategy interventions: Increasing reading comprehension of expository text

In this review of the literature we examine empirical studies designed to teach the structure strategy to increase reading comprehension of expository texts. First, we review the research that has served as a foundation for many of the studies examining the effects of text structure instruction. Text structures generally can be grouped into six categories: comparison, problem-and solution, causation, sequence, collection, and description. Next, we provide a historical look at research of structure strategyinterventions. Strategy interventions employ modeling, practice, and feedback to teach students how to use text structure strategically and eventually automatically. Finally, we review recent text structure interventions for elementary school students. We present similarities and differences among these studies and applications for instruction. Our review of intervention research suggests that direct instruction, modeling, scaffolding, elaborated feedback, and adaptation of instruction to student performance are keys in teaching students to strategically use knowledge about text structure

A note on moving average forecasts of long memory processes with an application to quality control

Standard quality control chart interpretation assumes that the observed data are uncorrelated. The presence of autocorrelation in process data has adverse effects on the performance of control charts. The objective of this paper is to assess the behavior of moving average forecast-based control charts on data having correlation that is persistent over very long time horizons, i.e., long-range dependent. We show that charts based on exponentially weighted moving average (EWMA) prediction do not perform well at detecting process shifts in long-range dependent data. We then introduce a new type of control chart, the hyperbolically weighted moving average (HWMA) chart, designed specifically for long-range dependent data. The HWMA charts perform better than the EWMA charts at detecting changes in the level of a long-memory process and also provide competitive performance for process data having only short-range dependence.info:eu-repo/semantics/publishedVersio