Spurious Memory Research Articles

Spurious Autobiographical Memory of Psychosis: A Mechanistic Hypothesis for the Resolution, Persistence, and Recurrence of Positive Symptoms in Psychotic Disorders.

Psychotic disorders are complex disorders with multiple etiologies. While increased dopamine synthesis capacity has been proposed to underlie psychotic episodes, dopamine-independent processes are also involved (less responsive to dopamine receptor-blocking medications). The underlying mechanism(s) of the reduction in antipsychotic responsiveness over time, especially after repeated relapses, remain unclear. Despite the consistent evidence of dopamine overactivity and hippocampal volume loss in schizophrenia, few accounts have been provided based on the interactive effect of dopamine on hippocampal synapse plasticity mediating autobiographical memory processes. The present hypothesis builds upon previous works showing the potential effects of dopamine overactivity on hippocampal-mediated neuroplasticity underlying autobiographical memory, alongside known patterns of autobiographical memory dysfunction in psychosis. We propose that spurious autobiographical memory of psychosis (SAMP) produced during active psychosis may be a key mechanism mediating relapses and treatment non-responsiveness. In a hyperdopaminergic state, SAMP is expected to be generated at an increased rate during active psychosis. Similar to other memories, it will undergo assimilation, accommodation, and extinction processes. However, if SAMP fails to integrate with existing memory, a discontinuity in autobiographical memory may result. Inadequate exposure to normalizing experiences and hyposalience due to overmedication or negative symptoms may also impede the resolution of SAMP. Residual SAMP is hypothesized to increase the propensity for relapse and treatment non-responsiveness. Based on recent findings on the role of dopamine in facilitating hippocampal synapse plasticity and autobiographical memory formation, the SAMP hypothesis is consistent with clinical observations of DUP effects, including the repetition of contents in psychotic relapses as well as the emergence of treatment non-responsiveness after repeated relapses. Clinical implications of the hypothesis highlight the importance of minimizing active psychosis, integrating psychosis memory, avoiding over-medication, and fostering normalizing experiences.

Robustness and spurious long memory: evidence from the generalized autoregressive score models

Robustness and spurious long memory: evidence from the generalized autoregressive score models

Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure.

Long Memory, Spurious Memory: Persistence in Range-Based Volatility of Exchange Rates

This study considers the long memory and fractional integration in the range-based volatilities across 30 currencies against USD. Graphical analysis of the autocorrelation function at long lags and pole near zero frequencies in the periodogram suggests the existence of fractional integration. We apply semi-parametric methods to measure long-range dependence. We find a decrease in the memory estimates with an increase in the bandwidth, which indicates the presence of spurious memory rather true long memory. The hypothesis of long memory against the alternative of spurious memory is also tested by applying the different semi-parametric methods. Empirical results confirm the presence of spurious memory that may be a result of some shocks to the volatility estimator. Furthermore, the reduced memory estimates obtained by utilising an estimator accounting for level shifts also explains the inconsistency of the Local Whittle estimator. We also estimate the number of breaks for each series.

True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods

True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods

Stock prices' long memory in China and the United States

PurposeThis study aims to examine the long memory as well as the effect of structural breaks in the US and the Chinese stock markets. More importantly, it further explores possible causes of the differences in long memory between these two stock markets.Design/methodology/approachThe authors employ various methods to estimate the memory parameters, including the modified R/S, averaged periodogram, Lagrange multiplier, local Whittle and exact local Whittle estimations.FindingsChina's two stock markets exhibit long memory, whereas the two US markets do not. Furthermore, long memory is robust in Chinese markets even when we test break-adjusted data. The Chinese stock market does not meet the efficient market hypothesis (EMHs), including the efficiency of information disclosure, regulations and supervision, investors' behavior, and trading mechanisms. Therefore, its stock prices' sluggish response to information leads to momentum effects and long memory.Originality/valueThe authors elaborately illustrate how long memory develops by analyzing not only stock market indices but also typical individual stocks in both the emerging China and the developed US, which diversifies the EMH with wider international stylized facts and findings when compared with previous literature. A couple of tests conducted to analyze structural break effects and spurious long memory demonstrate the reliability of the results. The authors’ findings have significant implications for investors and policymakers worldwide.

Long memory and regime switching in the stochastic volatility modelling

This paper studies the confusion between the long memory and regime switching in the second moment via the stochastic volatility (SV) methodology. An illustrative proposition is firstly presented with simulation evidence to demonstrate that spurious long memory can be caused by a Markov regime-switching SV (MRS-SV) process, when a long memory SV (LMSV) model is employed. To address this, an MRS-LMSV model is developed using a simulation-based optimization method, namely the Markov-Chain Monte Carlo algorithm. Via systematically constructed simulation studies, the proposed model can effectively distinguish between LMSV and MRS-SV processes with consistent estimators of the long-memory parameter. An empirical study of the S&P 500 daily returns is then conducted which demonstrates the superiority of the MRS-LMSV model over LMSV and MRS-SV counterparties. It is verified that significant long memory only exists in the high-volatility state. Important financial implications can be made to improve the risk management operations in practice.

True versus Spurious Long Memory in Cryptocurrencies

We test whether the selected cryptocurrencies exhibit long memory behavior in returns and volatility. We use data on five most traded cryptocurrencies: Bitcoin, Litecoin, Ethereum, Bitcoin Cash, and XRP. Using recent tests of long memory developed against persistent and nonlinear alternatives, this paper finds that long memory is mostly rejected in returns. The tests fail to reject the null hypothesis of long memory in most cases across different volatility proxies and cryptocurrencies. The estimated memory parameters show that volatility is persistent, and when volatility is measured by log range, it is borderline nonstationary.

Seasonality robust local whittle estimation

ABSTRACT Time series that have seasonal effects with long periods relative to the observation frequency can exhibit spurious long memory. The effect of these seasonalities on the periodogram is similar to that of structural breaks and non-periodic trends, but it only influences the seasonal frequencies and their harmonics. Still, the effect causes a sizable bias of popular estimators such as the local Whittle estimator. To overcome this, we propose a robust local Whittle estimator based on the omission of the affected periodogram ordinates. In a Monte Carlo study, we compare this estimator with a robust log-periodogram regression-based estimator known in the literature. An application to electricity load series demonstrates the potential of robust estimators for empirical research.

Discussions on the spurious hyperbolic memory in the conditional variance and a new model

Abstract This paper studies the spurious hyperbolic memory in the conditional variance caused by the Markov Regime-Switching GARCH (MRS-GARCH) process. We firstly propose an illustrative cause of this spuriousness and provide simulation evidence. An MRS Hyperbolic GARCH (MRS-HGARCH) model is then developed to successfully address it. Related statistical properties including the stationarity conditions and asymptotic behaviours of the maximum likelihood estimators of the MRS-HGARCH process are also investigated. An empirical study of the S&P 500 and TOPIX indexes returns is then conducted which demonstrates that our MRS-HGARCH model can provide a more reliable estimator of the hyperbolic-memory parameter and outperform both the HGARCH and MRS-GARCH models.

Bessel-like birth–death process

We consider models of the population or opinion dynamics which result in the non-linear stochastic differential equations (SDEs) exhibiting the spurious long-range memory. In this context, the correspondence between the description of the birth–death processes as the continuous-time Markov chains and the continuous SDEs is of high importance for the alternatives of modeling. We propose and generalize the Bessel-like birth–death process having clear representation by the SDEs. The new process helps to integrate the alternatives of description and to derive the equations for the probability density function (PDF) of the burst and inter-burst duration of the proposed continuous time birth–death processes.

Approximation of the first passage time distribution for the birth–death processes

We propose a general method to obtain approximation of the first passage time distribution for the birth–death processes. We rely on the general properties of birth–death processes, Keilson’s theorem and the concept of Riemann sum to obtain closed-form expressions. We apply the method to the three selected birth–death processes and the sophisticated order-book model exhibiting long-range memory. We discuss how our approach contributes to the competition between spurious and true long-range memory models.

Synthesization of Multi-valued Associative High-Capacity Memory Based on Continuous Networks with a Class of Non-smooth Linear Nondecreasing Activation Functions

This paper presents a novel design method for multi-valued auto-associative and hetero-associative memories based on a continuous neural network (CNN) with a class of non-smooth linear nondecreasing activation functions. The proposed CNN is robust in terms of the design parameter selection, which is dependent on a set of inequalities rather than the learning procedure. Some globally exponentially stable criteria are obtained to ensure multi-valued associative patterns to be retrieved accurately. The methodology, by generating CNN where the input data are fed via external inputs, avoids spurious memory patterns and achieves $$(2r)^n$$ storage capacity. These analytic results are applied to the associative memory of images. The fault-tolerant capability and the effectiveness are validated by illustrative experiments.

Tourism, terrorism and political violence in Tunisia: Evidence from Markov-switching models

This study investigates the impact of terrorist attacks and political violence on the number of tourist arrivals and overnight stays in Tunisia. The dataset employed consists of monthly data that covers the period from January 2000 to September 2016, which includes several political and terrorist attacks in Tunisia and the region. Empirically, we investigate the true data generating process (DGP) of these two proxies of tourism activity by accounting for four statistical properties that characterize these series: (1) seasonality, (2) unit roots, (3) breaks, and (4) long memory behavior.Our empirical findings show strong evidence of stationarity, five breaks in the tourist arrival time series and spurious long memory behavior. By estimating a 3-state Markov switching model consisting of the mean, trend, and variance, we find that the Tunisian Jasmine revolution and two recent terrorist attacks, one at the Bardo National Museum on March 18, 2015 and the other at the tourist resort at Port El Kantaoui, Sousse on June 26, 2015, played an important role in influencing the tourism activity of the country. Our empirical findings show also that local shocks have a more important impact than international shocks in influencing tourism activity. Interestingly, we find that the effects of terrorist shocks have a long duration compared to political violence shocks. Several security, marketing, and economic policies have been proposed and discussed in the paper.

A multivariate test against spurious long memory

This paper provides a multivariate score-type test against spurious long memory. We prove the consistency of the test against the alternatives of random level shifts and smooth trends. The test statistic is based on the weighted sum of the partial derivatives of the multivariate local Whittle likelihood function. To apply the test to fractionally cointegrated series, the test statistic is calculated for the linearly transformed system after estimating the cointegrating matrix. We derive the limiting distribution and show consistency of this procedure. The test is applied to log-absolute returns and log-realized volatilities of the S&P 500, DAX, FTSE, and NIKKEI.

A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.

Spurious Memory in Non-Equilibrium Stochastic Models of Imitative Behavior

The origin of the long-range memory in non-equilibrium systems is still an open problem as the phenomenon can be reproduced using models based on Markov processes. In these cases, the notion of spurious memory is introduced. A good example of Markov processes with spurious memory is a stochastic process driven by a non-linear stochastic differential equation (SDE). This example is at odds with models built using fractional Brownian motion (fBm). We analyze the differences between these two cases seeking to establish possible empirical tests of the origin of the observed long-range memory. We investigate probability density functions (PDFs) of burst and inter-burst duration in numerically-obtained time series and compare with the results of fBm. Our analysis confirms that the characteristic feature of the processes described by a one-dimensional SDE is the power-law exponent 3 / 2 of the burst or inter-burst duration PDF. This property of stochastic processes might be used to detect spurious memory in various non-equilibrium systems, where observed macroscopic behavior can be derived from the imitative interactions of agents.

Modelling long memory in volatility in sub-Saharan African equity markets

This study examines the long memory properties in the second moments of the return series in the equity markets of Ghana, Kenya, Nigeria and South Africa. Using 5219 daily observations of thin-trading adjusted total equity market return data, we find the presence of long-range dependency in the second moments of the return innovations in all the four countries’ equity markets in the full sample. To isolate spurious long memory, we perform structural breaks analysis to guide us in splitting the data for further examination. We find that all the four countries exhibited two structural breaks each during the sample period, and long memory identified in these two markets were not influenced by the structural breaks. This finding may have an influence on portfolio diversification and risk management. The long memory characteristics in the conditional volatility may also provide useful information to market participants in pricing long-term derivative contracts.

True or spurious long memory in European non-EMU currencies

We examine the Croatian Kuna, the Czech Koruna, the Hungarian Forint, the Polish Złoty, the Romanian Leu, and the Swedish Krona whether their Euro exchange rates volatility exhibits true or spurious long memory. Recent research reveals long memory in foreign exchange rate volatility and we confirm this finding for these currency pairs by examining the long memory behavior of squared residuals by means of the V/S test. However, by using the ICSS approach we also find structural breaks in the unconditional variance. Literature suggests that structural breaks might lead to spurious long memory behavior. In a refined test strategy, we distinguish true from spurious long memory for the six exchange rates. Our findings suggest that Czech Koruna and Hungarian Forint only feature spurious long memory, while the rest of the series have both structural breaks and true long memory. Lastly, we demonstrate how to extend existing models to jointly model both properties yielding superior fit and better Value-at-Risk forecasts. The results of our work help to avoid misspecification and provide a better understanding of the properties of the foreign exchange rate volatility.

True or Spurious Long Memory?: Evidence from Russian Precious Metals Market

We examine the long memory property and structural break in the spot and futures gold volatility in Russia from 2008 through 2013. We find strong evidence of long memory in the volatility of both spot and futures gold series. The break dates are associated with the recent global financial crisis. Moreover, we investigate the volatility spillover effect between the Russian spot and futures gold markets using the corrected Dynamic Conditional Correlation model (cDCC). The findings show relatively high level of conditional correlation between spot and futures gold returns. This outcome decreases the portfolio diversification benefits for gold investors.