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We examine the stability of market prices for 35 technology and 35 industrial stocks for the period December 31, 1993 to October 31, 2002. A phase portrait plot of the detrended log prices and de‐meaned returns of the two sectors shows a chaotic pattern in the stock prices indicating the presence of nonlinearity. However, when we compute the Lyapunov exponents, negative values are obtained. This shows that the price fluctuations for the 70 stocks result primarily from diffusion processes rather than from nonlinear dynamics. We evaluate forecast errors from a naïve model, a neural network, and ARMA models and find that the forecast errors are correlated with average changes in closed‐end fund discounts and other sentiment indexes. These results support an investor sentiment explanation for the closed‐end fund puzzle and behavioral theories of investor overreaction.

The purpose of this paper is to address the issue of optimal management of ecosystems by developing a dynamic model of strategic behavior by users/communities of an ecosystem such as a lake, which is subject to pollution resulting from the users. More specifically, it builds a model of two ecosystems that are spatially connected.

The paper uses the techniques of optimal control theory and game theory.

The paper uncovers sufficient conditions under which the analysis of the dynamic game can be converted to an optimal problem for a pseudo authority. It is shown that if the discount rate on the future is high enough relative to ecological self‐restoration parameters then multiple stable states appear. In this case, if the pollution level is high enough it is too costly in terms of what must be given up today to restore the damaged system. By using computational methods, the paper evaluates the relative strengths of lack of coordination, strength of ecosystem self‐cleaning forces, size of discount rates, etc.

The methodology as well as findings can help to devise an optimal management strategy over time for ecosystems.

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

In this paper chaos is viewed as an alternative approach to modeling complex and random appearing behavior. The spatial (static) characteristics of weekly returns and price levels for eleven International Indices are quantified. We find evidence that all countries exhibit similar static characteristics. Evidence presented supports the examination of price series instead of returns.

The equity premium of the S&P 500 index is explained in this paper by several variables that can be grouped into fundamental, behavioral, and macroeconomic factors. We hypothesize that the statistical significance of these variables changes across economic regimes. The three regimes we consider are the low‐volatility, medium‐volatility, and high‐volatility regimes in contrast to previous studies that do not differentiate across economic regimes. By using the three‐state Markov switching regime econometric methodology, we confirm that the statistical significance of the independent variables representing fundamentals, macroeconomic conditions, and a behavioral variable changes across economic regimes. Our findings offer an improved understanding of what moves the equity premium across economic regimes than what we can learn from single‐equation estimation. Our results also confirm the significance of momentum as a behavioral variable across all economic regimes

Female labour force participation (FLFP) is undeniably critical for both developing and developed countries. This study aims to investigate the impact of economic risk on FLFP, controlling economic well-being, fertility rate and education, considering the asymmetric relationship among the indicators in Turkey.

Time series data covering years from 1988Q1 to 2019Q4 is deployed for the empirical analysis to identify the long-run asymmetric link. Empirical analysis of the study starts with the employment of the Augmented Dickey-Fuller unit root test with the breakpoint to test for the order of integration of time series and to capture the breakpoints. The Brock-Dechert-Scheibkman test is applied to determine if or not the econometric model is correctly identified. Nonlinear autoregressive distributed lag (NARDL) bounds test is used to examine the existence of an asymmetric link between FLFP and economic well-being. The empirical analysis follows the investigation of the determinants of FLFP through the employment of the NARDL model.

The existence of long-run link among the time series is confirmed through the results obtained from the NARDL bounds test. Furthermore, long-run NARDL estimations confirm that (i) positive shocks in economic well-being increases FLFP; (ii) positive shock in education negatively impacts FLFP; (iii) FLFP is negatively affected by economic risk; and (iv) finally, increased fertility rate increases FLFP in Turkey.

This paper is checked from turnitin for the plagiarism which is estimated to be less than 20%. It is an original paper that fills the gap in literature and provides meaningful insight both for the policymakers and academics.

The purpose of this paper is to examine the price–volume relationship in the bitcoin market to validate near-stock properties of bitcoin.

Daily data of bitcoin returns, returns volatility and trading volume (TV) are utilized for the period August 17, 2010–April 16, 2017. Linear and non-linear causality tests are employed to examine price–volume relationship in the bitcoin market.

The linear causality analysis indicates that the bitcoin TV cannot be used to predict return; however, the reverse causality is significant. In contrast, the non-linear causality analysis shows that there are non-linear feedbacks between the bitcoin TV and returns. The bitcoin TV, which represents new information, leads to price changes, and large positive price changes lead to increased trading activity. Similarly, in recent periods (post-break period), the results of the non-linear causality test show a unidirectional causality from TV to the volatility of returns.

This study uses the average index value of major bitcoin exchanges. But further research on this relationship using data from different bitcoin exchanges may provide further insights into the price–volume relationship of bitcoin and its near-stock properties.

These findings from the non-linear causality analysis, therefore, suggest that investors cannot simply base their decisions on the linear dynamics of the bitcoin market. This is because new information in terms of the TV is neither linearly related to the price nor it is a one-to-one kind of relationship as most investors commonly understand it to be. Rather, investors’ decisions should be based on non-linear models, in general, and the best-fitting non-linear model, in particular.

The study examines bitcoin’s near-stock properties in a price–volume relationship framework with the help of both linear and non-linear causality tests, which to the best of the authors’ knowledge remains unexplored.

This paper aims to examine the relationship between exchange rate and oil prices in Algeria over the period 2004Q1–2019Q4.

The nonlinear autoregressive distributed lag method is used to capture the potential asymmetric relationship among oil prices and the exchange rate. Frequency domain spectral Granger causality test is also applied to investigate the causal linkage between the two variables. The wavelet coherence is applied to analyze the evolution of this relationship both in time and frequency domains.

The empirical results reveal evidence of long-run asymmetric effects of oil price on Algeria’s real effective exchange rate (REER), implying that an increase in oil price causes a real exchange rate to appreciate, while a decrease in oil price leads to a real exchange rate to depreciate. More specifically, it is found that the impact of negative oil price shocks is higher than the one associated with positive shocks. The spectral Granger causality results further indicate that there is unidirectional causality running from oil price to REER in both medium and long run. The wavelet coherence findings provide evidence of some co-movement between the REER and oil price and point out that the oil price is leading real exchange rate in the medium and long terms.

This study contributes to the literature by investigating the asymmetric impact and the time domain causal linkage between oil price fluctuations and real exchange rate in Algeria.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

This paper aims to empirically examine the effect of Coronavirus disease 2019 (COVID-19) pandemic on cryptocurrency market returns with particular attention to top five cryptocurrencies and COVID-19 confirmed and death cases.

The study applies the linear Toda and Yamamoto and nonlinear Diks and Panchenko Granger causality test to know the causal relationship of cryptocurrencies with COVID-19 pandemic. The study also uses the Narayan and Popp endogenous two structural break tests to capture the break period of the sample.

The findings of the study confirm the existence of unidirectional causal relation from COVID-19 confirmed and death cases to cryptocurrency price returns. While examining the break periods, the post-break period result indicates the presence of unidirectional linear causality from COVID-19 confirmed cases to Bitcoin and Ethereum price returns. This shows that prior knowledge of COVID-19 pandemic growth helps to predict the return of cryptocurrencies.

The study suggests the investors or crypto lovers to observe the growth of COVID-19 situations during their investment in cryptocurrency markets.