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Search Results: 1 - 10 of 7705 matches for " Long Memory "
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Utility of Tracing as a Memory Storage Method  [PDF]
Makana Tsutsui, Masako Notoya, Daisuke Kimura, Ken Nakatani, Takashi Fujita, Nobuyuki Sunahara, Katsumi Inoue
World Journal of Neuroscience (WJNS) , 2017, DOI: 10.4236/wjns.2017.72017
Abstract: Although many studies have explored the utility of tracing as a rehabilitation approach for patients with aphasia and alexia and for Japanese patients with various disabilities, this may be the first study to demonstrate the superiority of tracing over copying for enhancing long-term memory. We investigated the utility of tracing as a memory storage method. Young and elderly participants learned a figure from the Rey-Osterrieth complex figure test by copying or tracing its outline. They were asked to reproduce the figure after 3 min and 3 days. Although the copying group performed better than the tracing group in immediate recall after 3 min, the performance of the tracing and copying groups after 3 days was similar. Among younger participants, the tracing group achieved higher scores than the copying group after 3 days; however, the difference was not statistically significant. Copying as a learning strategy has a substantial temporal gradient of memory loss; tracing may be more appropriate for improving long-term memory. This result could have considerable practical usefulness, e.g., among professionals who provide memory training for the elderly. Tracing, which uses visuomotor memory, is acquired earlier than transcription. Tracing may be effective for rehabilitation because it is a developmentally appropriate approach to early instruction.
Intraday Periodicity and Long Memory Volatility in Hong Kong Stock Market  [PDF]
Wei Dai, Dejun Xie, Bianxia Sun
Open Journal of Social Sciences (JSS) , 2015, DOI: 10.4236/jss.2015.37011
Abstract:

This paper characterizes the volatility in Hong Kong Stock Market based on a 2-year sample of 5-min Heng Seng Index. By using the method of Flexible Fourier Form Filtering, we have successful removed the periodicity and have built a model of ARMA (1,1)-FIAPARCH (2, 0.300165,1). Further, the intraday volatility exists with long memory and asymmetry; the negative shock from the market will give rise to a higher volatility than the positive ones.

Long-Memory and Spurious Breaks in Ecological Experiments  [PDF]
Thomas R. Boucher
Open Journal of Statistics (OJS) , 2017, DOI: 10.4236/ojs.2017.75054
Abstract: The impact of long-memory on the Before-After-Control-Impact (BACI) design and a commonly used nonparametric alternative, Randomized Intervention Analysis (RIA), is examined. It is shown the corrections used based on short-memory processes are not adequate. Long-memory series are also known to exhibit spurious structural breaks that can be mistakenly attributed to an intervention. Two examples from the literature are used as illustrations.
Further Results on Convergence for Nonlinear Transformations of Fractionally Integrated Time Series  [PDF]
Chien-Ho Wang
Theoretical Economics Letters (TEL) , 2012, DOI: 10.4236/tel.2012.24075
Abstract: This paper presents some new results for the nonlinear transformations of the fractional integration process. Specifically, this paper reviews the weight fractional integration process with the Hurst parameter, 3/2 > d > 5/6 , and investigates the asymptotics of asymptotically homogeneous functional transformations of weight fractional integration process. These new results improve upon the earlier research of Tyurin and Phillips [1].
Testing the Long-Memory Features in Return and Volatility of NSE Index  [PDF]
Naseem Ahamed, Mamoni Kalita, Aviral Kumar Tiwari
Theoretical Economics Letters (TEL) , 2015, DOI: 10.4236/tel.2015.53050
Abstract: Long-term memory of stock markets is a topic that has not received its due attention from academics. Posting the assertion made by Fama, 1970 [1] about markets being efficient, no one can consistently outrun it for a longer duration. Handful of papers checked the efficiency in emerging markets to see if the efficiency proposition held true. Furthering the literature in this study we test for the long-term memory of National Stock Exchange (NSE) index, Nifty and NSE_500 which are a collection of 50 and 500 listed firms respectively in India. The duration of the data for study is roughly eight years over the period from 2006-06-29 to 2012-09-13, a total of 1545 observations. We observe that long-term memory does exist in the context of Indian stock market index.
On Detecting Sudden Changes in the Unconditional Volatility of a Time Series  [PDF]
Dilip Kumar
Theoretical Economics Letters (TEL) , 2016, DOI: 10.4236/tel.2016.62028
Abstract: The present study highlights the drawback of using Sanso, Arago and Carrion’s (2004) AIT-ICSS algorithm in detecting sudden changes in the unconditional volatility when long memory is present in volatility. Simulation experiments show that the AIT-ICSS test is severely oversized and exhibits low power when long memory is present in volatility.
A Study on the Impact the Shanghai-Hong Kong Stock Connect Making on the Long-Term Memory of Chinese Stock Market  [PDF]
Yongyue Zhang
Open Journal of Social Sciences (JSS) , 2017, DOI: 10.4236/jss.2017.54009
Abstract: Based on the log return of Shanghai composite index, this paper constructs Hurst index by R/S analysis method to measure the long memory in stock market. The results show that there is strong long memory in China’s stock market. After the opening of Shanghai-Hong Kong stock connection, the long memory in China’s stock market has been reduced, so does the memory cycle. It means that the Shanghai-Hong Kong stock connection has a positive effect on enhancing the effectiveness of China’s stock market.
Forecasting High-Frequency Long Memory Series with Long Periods Using the SARFIMA Model  [PDF]
Handong Li, Xunyu Ye
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.51009
Abstract: This paper evaluates the efficiency of the SARFIMA model at forecasting high-frequency long memory series with especially long periods. Three other models, the ARFIMA, ARMA and PAR models, are also included to compare their forecasting performances with that of the SARFIMA model. For the artificial SARFIMA series, if the correct parameters are used for estimating and forecasting, the model performs as well as the other three models. However, if the parameters obtained by the WHI estimation are used, the performance of the SARFIMA model falls far behind that of the other models. For the empirical intraday volume series, the SARFIMA model produces the worst performance of all of the models, and the ARFIMA model performs best. The ARMA and PAR models perform very well both for the artificial series and for the intraday volume series. This result indicates that short memory models are competent in forecasting periodic long memory series.
The Effects of Long Memory in Price Volatility of Inventories Pledged on Portfolio Optimization of Supply Chain Finance  [PDF]
Juan He, Jian Wang, Xianglin Jiang
Journal of Mathematical Finance (JMF) , 2016, DOI: 10.4236/jmf.2016.61014
Abstract:

Due to the illiquidity of inventories pledged, the essential of price risk management of supply chain finance is to long-term price risk measure. Long memory in volatility, which attests a slower than exponential decay in the autocorrelation function of standard proxies of volatility, yields an additional improvement in specification of multi-period volatility models and further impact on the term structure of risk. Thus, long memory is indispensable to model and measure long-term risk. This paper sheds new light on the impact of the existence and persistence of long memory in volatility on inventory portfolio optimization. Firstly, we investigate the existence of long memory in volatility of the inventory returns, and examine the impact of long memory on the modeling and forecasting of multi-period volatility, the dependence structure between inventory returns and portfolio optimization. Secondly, we further explore the impact of the persistence of long memory in volatility on the efficient frontier of inventory portfolio via a data generation process with different long memory parameter in the FIGARCH model. The extensive Monte Carlo evidence reveals that both GARCH and IGARCH models without accounting for long memory will misestimate the actual long-term risk of the inventory portfolio and further bias the efficient frontier; besides, through A sensitive analysis of long memory parameter d, it is proved that the portfolio with higher long memory parameter possesses higher expected return and lower risk level. In conclusion, banks and other participants will benefit from the long memory taken into the long-term price risk measure and portfolio optimization in supply chain finance.

Intentional Memory Instructions do not Improve Visual Memory
International Journal of Brain and Cognitive Sciences , 2012, DOI: 10.5923/j.ijbcs.20120103.02
Abstract: The current experiment examines whether intentional encoding instructions improve long-term recognition memory for visual appearance. Past experiments suffer from various methodological flaws, such as inadequate statistical power or confounding of variables such as attention and task relevance withintentional memory instructions.In the current experiment, the effect of memory instructions was examined using a factorial design, so that attention to/task relevance of objects could be manipulated independently of memory instructions. The sample size was large enough to achieve power equal to .80 for medium effect sizes (f = .25). There was no effect of intentional memory instructions. These results suggest that observers cannot easily enhance encoding and storage of visual information in long-term memory.
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