179 research outputs found

    Memory in returns and volatilities of commodity futures’ contracts

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    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

    The Local Whittle Estimator of Long Memory Stochastic Volatility

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    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

    Get PDF
    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

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    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

    The Local Whittle Estimator of Long Memory Stochastic Volatility

    Get PDF
    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
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