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This paper is focused on two particular issues related to the stochastic structure of stock prices: linear long‐memory and nonlinearity.

The purpose of this paper is to analyze the main differences in the cybernetic structures necessary for elementary anticipation, understood as anticipation of the repetition of one known pattern, and complex anticipation, understood as anticipation of the repetition of known sequences of patterns.

A functional cybernetic approach is used to develop the necessary additions to an elementary anticipatory system, so that it can provide standards for anticipated sequences containing seven single patterns or “chunks”.

A subsystem for the anticipation of sequences is developed that is able to: identify the beginning of known sequences; search for different known sequences containing that beginning; and decide to use later patterns of such a sequence as standards for anticipated patterns. Deciding to actually use such patterns for anticipation requires an additional subsystem to switch between the feedback pattern recognition and the feedforward anticipation mode.

The paper shows how complex anticipation can be developed from elementary forms by adding highly parallel structures that apply the same underlying principles; and it emphasizes epistemological demands for the structure and the data organization that have to be fulfilled, so that anticipation of the repetition of sequences becomes possible.

The paper illustrates the complexity of the anticipation of sequences and it provides the base to analyze more complex forms of specifically human thinking.

The purpose of this paper is to investigate the time series behavior of co‐movements among 11 European real estate securities markets, with each other as well as between country‐averages, over the sample period from January 1999 to January 2010 by utilizing the asymmetric dynamic conditional correlation (ADCC) technique, long‐memory tests and multiple structural break methodology.

First the ADCC from the multivariate GJR‐GARCH model is used to estimate the pair‐wise conditional correlations between the 11 securitized real estate markets. Then, the 11 country‐average conditional correlation series is subject to a battery of four long‐memory tests to form an “on the balance of evidence” picture; the semi‐parametric Geweke and Porter‐Hudak procedure and Robinson test, as well as the non‐parametric Hurst‐Mandelbrot R/S and Lo's modified R/S tests. Finally, the Bai and Perron's multiple structural break methodology seeks to test whether the average conditional correlations are subject to regime switching via the detection of breaks in the co‐movements of real estate securities returns.

Low to moderate conditional correlations are found for these European real estate securities market and a higher level of correlation in the aftermath of the global financial crisis. The long‐memory correlation effect is present for nine European real estate securities markets. In addition, the conditional correlations are subject to regime switching with two structural breaks in four country‐average correlation series. Across the regimes, a higher level of correlation is linked to a higher level of volatility and a lower level of return, and this happened around the global financial crisis period.

The findings that national real estate securities correlations exhibit time‐varying and asymmetric behavior can help investors understand how real estate securities will co‐move in different market scenarios (e.g. “crisis” and “non‐crisis” times). Moreover, the process of dynamic covariance analysis and forecasting (the ultimate objective in portfolio management) should not rely too much on short‐term autoregressive moving average models. Instead, a combination of some appropriate long‐range dependence models and regime‐switching specifications is needed.

This paper offers useful insights into the time series behavior of average dynamic conditional correlations in European public property markets.

Reviews previous research on the nature of beta and investigates the stochastic structure of time‐varying beta in Hong Kong, Malaysia and Singapore using the bi‐variate GARCH‐in‐mean model and fractional tests. Develops mathematical models and applies them to 1989‐1998 daily data from all three stock markets. Presents the results, which suggest, in contrast to other findings, that all three time‐varying betas are slowly mean‐reverting (long memory).

The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d≥12. Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.

The purpose of this paper is to determine whether foreign exchange markets in India have become more efficient over time. There were two major developments in India’s foreign exchange market since the 1980s: first, a shift in foreign exchange management regime from a basket peg to a free float; and second, a rapid phase of economic liberalization since the mid-1990s. The paper attempts to find out whether the market efficiency of foreign exchange markets is affected by these developments. The paper mainly uses the well-known Hurst exponent calculated through corrected empirical R over S analysis to determine whether the exchange rates possess long memory. The robustness of the method is tested by calculating the Hurst exponent through two other prevalent methods in the literature.

The authors apply the corrected empirical Hurst exponent which employs the Anis Lloyd correction with the modification suggested by Weron. The sensitivity of the results is then tested by replicating the calculations using the detrended fluctuation analysis and Robinson’s method.

All the methods show that: first, there is no significant change in the overall efficiency of the foreign exchange market vis a vis the US$ for the time period from 1980 to 2017. Second, neither regime shifts nor calculations over sub-time periods is able to identify significant change in the efficiency level of the market for the US$ exchange rate. Third, efficiency of different exchange rate markets are different over the time period 1999–2017. The US$ market has unequivocally more long run memory compared to the GBP, Yen and EURO markets. Fourth, the results are robust to the method used for calculations.

Does the efficiency of asset markets evolve over time? This paper attempts to answer this question. In the process, the paper studies the effect of regime shifts and progressive globalization on the ability of the market to internalize information.

The paper aims to model multiple-period market risk forecasts under long memory persistence in market volatility.

The paper proposes volatility forecasts based on a combination of the GARCH(1,1)-model with potentially fat-tailed and skewed innovations and a long memory specification of the slowly declining influence of past volatility shocks. As the square-root-of-time rule is known to be mis-specified, the GARCH setting of Drost and Nijman is used as benchmark model. The empirical study of equity market risk is based on daily returns during the period January 1975 to December 2010. The out-of-sample accuracy of VaR predictions is studied for 5, 10, 20 and 60 trading days.

The long memory scaling approach remarkably improves VaR forecasts for the longer horizons. This result is only in part due to higher predicted risk levels. Ex post calibration to equal unconditional VaR levels illustrates that the approach also enhances efficiency in allocating VaR capital through time.

The improved VaR forecasts show that one should account for long memory when calibrating risk models.

The paper models single-period returns rather than choosing the simpler approach of modeling lower-frequency multiple-period returns for long-run volatility forecasting. The approach considers long memory in volatility and has two main advantages: it yields a consistent set of volatility predictions for various horizons and VaR forecasting accuracy is improved.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

We investigate the long‐term dependency behavior of Asian foreign exchange markets by using rescaled range analysis. Emerging markets in Korea, Taiwan, India, and Thailand, show evidences of long memory in the exchange rate return series, while the exchange rate return persistence is not found in more developed and mature markets in Japan, Australia, Hong Kong, and Singapore. Our results suggest that the return‐generating processes and presence of long memory depends on the degree of market development. In addition, the findings suggest that Asian financial crisis affects long‐term dependences of Korean won and Thai baht in which their economies and currency were hard hit by the crisis.

The purpose of this paper is to explore the association between memory (short- and long-term), a foundational cognition in learning and face-to-face, video-based and flipped instructional modalities.

This study used a one-way analysis of variance and linear regression analyses to compare students’ aggregated answers on multiple-choice questions over two different periods, including a repeat question from an earlier examination. Also, student-level answers were subjected to a binary logistic regression.

Face-to-face unambiguously was associated with superior short-term memory including ethics. Video-based performance was associated with a superior long-term memory, and flipped’s performance lay in between for both memory types.

This study does not account for students’ learning styles, instructors’ preferred teaching approach and computer-aided virtual simulations.

The findings of this study may serve as a reference point for optimally blending multiple instruction modalities to leverage its association with memory for learning matched to instructors’ styles, students’ curricular pathway and coping with institutional imperatives.

This paper provides a way for higher education institutions to match instructional modalities to memory needs, including business ethics as students’ progress on their pathways towards graduation.

This study illuminates the association between memory, a widely accepted foundational cognition in learning that has been under researched compared to critical thinking and reasoning, and three instructional modalities: face-to-face, video-based and flipped classroom.