Rolling Window Method Research Articles

The adaptive market hypothesis and the return predictability in the cryptocurrency markets

This study employs robust martingale difference hypothesis tests to examine return predictability in a broad sample of the 40 most capitalized cryptocurrency markets in the context of the adaptive market hypothesis. The tests were applied to daily returns using the rolling window method in the research period from May 1, 2013 to September 30, 2022. The results of this study suggest that the returns of the majority of the examined cryptocurrencies were unpredictable most of the time. However, a great part of them also suffered some short periods of weak-form inefficiency. The results obtained validate the adaptive market hypothesis. Additionally, this study allowed the observation of some differences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form efficiency were identified. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relationship between market cap and predictability was observed.

Public finances solvency in the Euro Area

We assess public finances solvency for Euro Area countries, using quarterly data from 1999Q1 to 2020Q4. For most countries: (i) the primary budget balance reacts positively to the lagged debt-to-GDP ratio and past primary budget balances contribute to the reduction of the debt-to-GDP ratio, indicating a Ricardian fiscal regime. In the country-by-country analysis, we confirm (ii) the ”elusive character of fiscal sustainability” (Afonso and Jalles, 2016). Furthermore, in a panel framework: (iii) the response of revenues to government expenditures is higher from 2010 onwards, whereas the response is lower for higher average debt-to-GDP ratios; (iv) the response of the primary budget balance to the lagged debt-to-GDP ratio is lower from 2010 onwards and is higher for higher average debt-to-GDP ratios; (v) past primary budget balances enable the reduction of the debt-to-GDP ratio, especially before 2010 and also in countries whose average debt-to-GDP ratio is between 60% and 90% of GDP. Using a rolling window method, we find that (vi) the higher the lagged debt-to-GDP ratios, fiscal rule indexes, and sovereign ratings, the greater are the fiscal sustainability coefficients. Conversely, (vii) those coefficients are lower both after 2010 and during periods of legislative elections. These results have important implications for public finances management and solvency.

Forecasting the real prices of crude oil: What is the role of parameter instability?

Parameter instability due to potential structural breaks is an important problem affecting out-of-sample forecasting performance of econometric models. This paper uses four types of methods addressing parameter instability, including rolling window, regime switching model, time-varying parameter model, and the time-dependent weighted least squares. The hyperparameters in each method which control the degree of parameter variation are determined via a simple machine learning approach of cross-validation and forecast combination. Our results show significant improvement in predictability of oil prices using these methods accounting for parameter instability except the rolling window method. Forecast combination for models with different hyperparameters produces more robust results than the cross-validation selecting the ex-ante optimal hyperparameters.

Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD)

Attention deficit hyperactivity disorder (ADHD) is a common cognitive disorder affecting children. ADHD can interfere with educational, social, and emotional development, so early detection is essential for obtaining proper care. Standard ADHD diagnostic protocols rely heavily on subjective assessments of perceived behavior. An objective diagnostic measure would be a welcome development and potentially aid in accurately and efficiently diagnosing ADHD. Analysis of pupillary dynamics has been proposed as a promising alternative method of detecting affected individuals effectively. This study proposes a method based on the self-similarity of pupillary dynamics and assesses its strength as a potential diagnostic biomarker. Localized discriminatory features are developed in the wavelet domain and selected via a rolling window method to build classifiers. The application on a task-based pupil diameter time series dataset of children aged 10–12 years shows that the proposed method achieves greater than 78% accuracy in detecting ADHD. Comparing with a recent approach that constructs features in the original data domain, the proposed wavelet-based classifier achieves more accurate ADHD classification with fewer features. The findings suggest that the proposed diagnostic procedure involving interpretable wavelet-based self-similarity features of pupil diameter data can potentially aid in improving the efficacy of ADHD diagnosis.

Forecasting stock price movement direction by machine learning algorithm

<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>

Dynamic Path Planning for Forklift AGV Based on Smoothing A* and Improved DWA Hybrid Algorithm.

FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task requirements of FAGV, this paper proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. The improved A* algorithm can plan the global optimal path more suitable for FAGV. The improved evaluation function of DWA can ensure that the local path of FAGV is closer to the global path. DWA combines the rolling window method for local path planning to avoid sudden unknown static and dynamic obstacles. In addition, this paper verifies the effectiveness of the algorithm through simulation. The simulation results show that the algorithm can avoid obstacles dynamically without being far away from the global optimal path.

Associations of α-linolenic acid dietary intake with very short sleep duration in adults.

ObjectivesThis study aimed to investigate the association of α-linolenic acid (ALA; 18:3 ω-3) dietary intake with very short sleep duration (<5 h) in adults based on the CDC's National Health and Nutrition Examination Survey data.MethodsMultinomial logistic regression was used to explore the association of ALA intake with very short sleep. To make the estimation more robust, bootstrap methods of 1,000 replications were performed. Rolling window method was used to investigate the trend of the odds ratios of very short sleep with age. A Kruskal–Wallis test was applied to estimate the differences in the ORs of very short sleep between genders and different age groups.ResultsCompared with the first tertile, the ORs of very short sleep and the corresponding 95% CIs for the second and the third tertile of dietary ALA intake in males were 0.618 (0.612, 0.624) and 0.544 (0.538, 0.551), respectively, and in females were 0.575 (0.612, 0.624) and 0.432 (0.427, 0.437). In most cases, the differences between different ages were more significant than those between different sexes. Men's very short sleep odds ratios for the second tertile of ALA intake increased linearly with age before 60.ConclusionsThe risk of a very short sleep duration was negatively related to the dietary intake of ALA. The effect of ALA on very short sleep is significantly different among groups of different genders and ages.

Is technological innovation a driver of renewable energy?

This study unveils the causality between technology innovations and renewable energy in Germany. The discussion about technology innovations and renewable energy is beneficial because the country is most innovative and pursuing ambitious energy transition goals. The results of complete sample causality show that technology innovations significantly impact renewable energy. The rolling window method accounts for structural changes when examining the causal connection between technology innovations and renewable energy. The findings show that technology innovation drives renewable energy positively and negatively across multiple sub-samples. Similarly, renewable energy has a considerable effect on technology innovations in sub-samples, implying that advancement in renewable energy translates into increased spending on technology innovation. The future cornerstones of energy supply comprise fluctuating sources caused by weather which must be balanced out.

Predicting Exchange Rate under UIRP Framework with Support Vector Regression

This study aimed to forecast the exchange rate between the Vietnamese dong and the US dollar for the following month in the context of the COVID-19 pandemic. It used the Support Vector Regression (SVR) algorithm under the Uncovered Interest Rate Parity (UIRP) theoretical framework; the results are compared with the Ordinary Least Square (OLS) regression model and the Random Walk (RW) model under the rolling window method. The data included the VND/USD exchange rate, the bank interest rate for the 1-month term, and the 1-month T-bill from January 01, 2020, to September 11, 2021. The research discovered a linear link between the two nations' exchange rates and interest rate differentials. Interest rate differentials are input variables to forecast interest rate differentials. Furthermore, the connection between the exchange rate and interest rate differentials during this era does not support the UIRP hypothesis; hence, the error for OLS predictions remains large. The study provided a model to forecast future exchange rates by combining the UIRP theoretical framework and the SVR algorithm. The UIRP theoretical framework can anticipate exchange rate differentials using the input variable and the interest rates between two nations. Meanwhile, the SVR algorithm is a robust machine learning technique that enhances prediction accuracy. Doi: 10.28991/ESJ-2022-06-03-014 Full Text: PDF

Portfolio Constraints: An Empirical Analysis

Mean-variance optimization often leads to unreasonable asset allocations. This problem has forced scholars and practitioners alike to introduce portfolio constraints. The scope of our study is to verify which type of constraint is more suitable for achieving efficient performance. We have applied the main techniques developed by the financial community, including classical weight, flexible, norm-based, variance-based, tracking error volatility, and beta constraints. We employed panel data on the monthly returns of the sector indices forming the MSCI All Country World Index from January 1995 to December 2020. The assessment of each strategy was based on out-of-sample performance, measured using a rolling window method with annual rebalancing. We observed that the best strategies are those subject to constraints derived from the equal-weighted model. If the goal is the best compromise between absolute return, efficiency, total risk, economic sustainability, diversification, and ease of implementation, the best solution is a portfolio subject to no short selling and bound either to the equal weighting or to TEV limits. Overall, we found that constrained optimization models represent an efficient alternative to classic investment strategies that provide substantial advantages to investors.

Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods

The present paper examines the relative out-of-sample predictive ability of GARCH, GARCH-M, EGARCH, TGARCH and PGARCH models for ten Asian markets by using three different time frames and two different methods, considering the features of volatility clustering, leverage effect and volatility persistence phenomena, for which the evidence of existence is found in the data. Five measures of comparison are employed in this research, and a further dimension is investigated based on the classification of the selected models, in order to identify the existence or lack of any differences between the recursive and rolling window methods. The empirical results reveal that asymmetric models, led by the EGARCH model, provide better forecasts compared to symmetric models in higher time frames. However, when it comes to lower time frames, symmetric GARCH models tend to outperform their asymmetric counterparts. Furthermore, linear GARCH models are penalized more by the rolling window method, while recursive method places them amongst the best performers, highlighting the importance of choosing a proper approach. In addition, this study reveals an important controversy: that one error statistic may suggest a particular model is the best, while another suggests the same model to be the worst, indicating that the performance of the model heavily depends on which loss function is used. Finally, it is proved that GARCH-type models can appropriately adapt to the volatility of Asian stock indices and provide a satisfactory degree of forecast accuracy in all selected time frames. These results are also supported by the Diebold-Mariano (DM) pairwise comparison test.

Is gold a hedge or safe haven against oil and currency market movements? A revisit using multifractal approach.

We investigate gold’s role as a hedge or safe haven against oil price and currency movements across calm and extreme market conditions. For the empirical analysis, we extend the intraday multifractal correlation measure developed by Madani et al. (Bankers, Markets & Investors, 163:2–13, 2020) to consider the dependence for calm and extreme movement periods across different time scales. Interestingly, we employ the rolling window method to examine the time-varying dependence between gold-oil and gold-currency in terms of calm and turmoil market conditions. Based on high frequency (5-min intervals) across the period 2017–2019, our analysis shows three interesting findings. First, gold acts as a weak (strong) hedge for oil (currency) market movements, across all agent types. Second, gold has strong safe-haven capability against extreme currency movements, and against only short time scales of oil price movements. Third, hedging strategies confirm the scale-dependent gold's role in reducing portfolio risk as a hedge or safe haven. Implications for investors, financial institutions, and policymakers are discussed.

Impact of COVID-19 outbreak on multi-scale asymmetric spillovers between food and oil prices

This paper analyzes the time-frequency spillover effects between food and crude oil markets, two particularly important commodity markets, under the impact of the pandemic. Using the BK frequency domain spillover index and the rolling window method, we explore the spillover effects between the food and crude oil markets under the influence of COVID-19, and compare the changes of spillover effects in each market before and during the pandemic. Based the network connectedness method and the Bayesian structural time series method, we further reveal the changes of the pairwise spillover effects between markets on different time scales. Our study shows that the food-oil market system has the strongest spillover effect in the short term, and the spillovers during the pandemic are significantly weaker than that under the financial crisis. In addition, the pandemic has significantly increased the impact of corn on the crude oil market, but reduced its spillovers on soybeans and rice. Finally, during the COVID-19 period, the wheat market is likely to receive more spillovers from other markets, particularly corn and soybeans. These findings are of great significance for market participants with different horizons to understand the spillover effects of food and oil markets under the impact of the pandemic and to avoid the risk transmission across markets or assets.

Does renewable energy redefine geopolitical risks?

For the purpose of this research, we use the rolling window method in order to examine the relationship between global geopolitics risks and renewable energy. The results display a two-way causality between geopolitics risks and renewable energy that are spread across various sub-samples. This shows that geopolitical risks play an important role in the advent of renewable energy primarily because of the energy security, rare metal competition, and trade disputes that are put forth, which stimulate the transition to renewable energy. On the other hand, renewable energy has a significant impact on geopolitical risks that are driven by global economic growth, rising fossil fuels prices and technological innovations. The findings also support the classical production model, which reveals that geopolitical risks and renewable energy have a mutual relationship with one another. Further in this regard, renewable energy is capable of accommodating new powers in the international political system. This aspect is more useful for international peace, and may also reduce geopolitical risks. Therefore, the transition to renewable energy is beneficial to the energy supply and security. Therefore, the governments of the world should encourage the private sector to participate in renewable energy projects, by providing special incentives for this purpose.

Revisiting Bitcoin Price Behavior Under Global Economic Uncertainty

This study examines the relationship between global economic policy uncertainty (GEPU) and bitcoin prices (BCP) employing the rolling window method. The full sample test shows that there is no causality between GEPU and BCP. However, the full sample causal relationship between the variables can be different when considering structural changes. The finding of the rolling window test indicates that there is causality in different subsamples. It has found both positive and negative bidirectional causalities between GEPU and BCP across various subsamples. The decision makers need to accelerate the development of blockchain technology that can be used for hedging and portfolio diversification. Moreover, enacting laws and regulations on state interventions and prohibitions ensures investor confidence. Information about policy changes should be incorporated into portfolio selection to avoid random market fluctuations. Its unregulated nature makes it more turbulent in the short term and has undergone sudden changes, so investors should be able to obtain comprehensive information about global economic and policy changes. Policy makers should ensure investor confidence by making legal regulations on state interventions and prohibitions.

The impact of economic uncertainty caused by COVID-19 on renewable energy stocks

By employing time–frequency-domain frameworks, this study analyzes the spillover effects of news-based economic uncertainty caused by the pandemic on three renewable energy stock indices in the USA, Europe, and the world. The empirical results reveal that the total spillover from economic uncertainty to the three renewable energy stock returns was concentrated at a high frequency, whereas those to volatilities appeared at low frequencies. Utilizing a rolling-window method, we observed that the impact of uncertainty caused by COVID-19 on three renewable energy stock returns and volatilities is more significant than that resulting from the global financial crisis (GFC). During COVID-19, the majority of the spillover effects from economic uncertainty to returns and volatilities of the three indices focused on the long term.

Economic Dependence Relationship and the Coordinated &amp; Sustainable Development among the Provinces in the Yellow River Economic Belt of China

This study uses the mutual information method to study economic dependence among the provinces in the Yellow River Economic Belt, constructs the core dependence structure through the maximum spanning tree method, and uses the rolling window method to observe the changes in the dependence structure from a dynamic point of view. It has been found that there are extensive economic links among the nine provinces in the Yellow River Economic Belt, but that the degree of economic dependence varies greatly in different time periods. When economic development and the capital market are overheated, the interregional dependence is stronger, while the dependence decreases when economic development is in a state of contraction or when the total demand is relatively reduced. In addition, the phenomenon of geographical clustering of economic dependence is not obvious among provinces in the Yellow River Economic Belt. Most of the provinces maintain strong economic dependence with the economically developed provinces, and the economically developed provinces also maintain strong economic ties with one another. Finally, the implementation of the Yellow River Economic Belt strategy strengthens the economic links between the less developed provinces and the other provinces in the region, and promotes coordinated and sustainable development in the region.

Sustainability of Global Economic Policy and Stock Market Returns in Indonesia

Interdependence in trade and financial globalization has increased the vulnerability of developed and developing countries to external shocks alike, whereas emerging markets are more vulnerable to the shocks originating from the world’s leading economies. This paper investigates the impact of the uncertainty from the global economic policy on the return of the Indonesian stock market by using the time-varying correlation based on the rolling window method and time-varying built dynamic conditional correlation method. Both the rolling window and condition correlation estimates indicate that the correlation between global policy uncertainty and Indonesian stock returns is time-varying. The results of the autoregressive distributed lag-based regression indicate that inflation, global crude oil prices, gross domestic product, and world crude oil production have significant impacts on the dynamic conditional correlation. The average negative estimate of time-varying correlation suggests that investors when faced with liquidity constraints in one country may sell off their assets in another country to raise funds in order to meet their future financial needs. This also indicates that the rise in the uncertainty of economic policy in developed markets has a negative impact on the shocks faced by the Indonesian stock market. Based on our empirical findings, it is recommended that Indonesian policymakers should place more focus on the sustainability of the economic growth, pay close attention to volatile crude oil prices, world crude oil production, and inflation so as to avoid dynamic interaction between the uncertainty of economic policy in the developed markets and the return of the Indonesian stock market.

Cryptocurrencies, gold, and WTI crude oil market efficiency: a dynamic analysis based on the adaptive market hypothesis

This study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies (Bitcoin, Ethereum, and Ripple), gold, and West Texas Intermediate (WTI) crude oil. The data coverage of daily returns was from August 2015 to April 2019. We applied two alternative tests to examine linear and nonlinear dependency, i.e., automatic portmanteau and generalized spectral tests. The analysis of observed results validated the adaptive market hypothesis (AMH) in all markets, but the degree of adaptability between the data was different. In this study, we also analyzed the existence of evolutionary behavior in the market. To achieve this goal, we checked the results by applying the rolling-window method with three different window lengths (50, 100, and 150 days) on the test statistics, which was consistent with the findings of AMH.

Time-Varying Convergences of Environmental Footprint Levels between European Countries

This study proposes the time-varying nonlinear panel unit root test to investigate the convergence of ecological foot prints between the EU and candidate countries. Sixteen European countries (such as Albania, Austria, Belgium, Denmark, France, Germany, Greece, Italy, Luxembourg, Netherlands, Poland, Portugal, Romania, Spain, Sweden and Turkey) and analysis periods are selected according to data availability. This study proposes a cross-sectional Panel KSS with Fourier to test the convergence of the ecological footprints. Then, we combine this methodology with the rolling window method to take into account the time-varying stationarity of series. This study evaluated sub-components of ecological footprints separately and provided more comprehensive findings for the ecological footprint. According to empirical findings, this study proves that convergence or divergence does not show continuity over time. On the other side, this study points out the presence of divergence draws attention when considering the properties of the sub-components in general. As a result, this study shows that international policies by EU countries are generally accepted as successful to reduce ecological footprint, but these are not sufficient as expected. In this point, it is suggested to keep national policies to support international policies in the long term.