This study aims to predict the yield of two strategic crops in Iran; wheat and rice, under climate scenarios that indicate probable changes in climate variables. It implemented by a stochastic model based on the Monte Carlo method. This model were estimated based on historical data from 1988 to 2019 for precipitation and temperature provided possible changes in the pattern of and their probability of occurrence. The results show that rain-fed wheat, irrigated wheat and rice yields decrease by 42%, 29% and 21% respectively in the average scenario. Therefore, policy makers should try to make the right decisions to increase the production of the country's strategic crops. R&D management to introduce drought-tolerant varieties, use of modern irrigation systems and use of low-volume irrigation methods are some of the proposed solutions to mitigate the effects of climate change.

This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be observed. In particular, their high value is often praised in Value-at-Risk. However, the lack of nonlinear structure in most approaches means that conditional variance is not adequately represented in the model. On the contrary, the recent rapid development of deep learning methods is able to describe any nonlinear relationship in a clear way. We propose GARCHNet, a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators in GARCH. The variance distributions considered in the paper are normal, t and skewed t, but the approach allows extension to other distributions. To evaluate our model, we conducted an empirical study on the logarithmic returns of the WIG 20 (Warsaw Stock Exchange Index), S&P 500 (Standard & Poor's 500) and FTSE 100 (Financial Times Stock Exchange) indices over four different time periods from 2005 to 2021 with different levels of observed volatility. Our results confirm the validity of the solution, but we provide some directions for its further development.

Here we have proposed fuzzy portfolio selection model using stochastic correlation (FPSMSC) to overcome limitations both in fuzzy and stochastic world. The newly proposed model not only gets harmonious efficient frontier, but also considers the future movement of stock prices based on fuzzy expertise knowledge. The investment weights of the model have been optimized based on the monthly return data of 18 stocks listed in S&P500 from October 2011 to September 2015. The proposed model has provided higher returns in the whole regime of risk for the period from October 2014 to September 2015, whose monthly return data are used as training data than other available portfolio selection models, i.e., fuzzy portfolio selection models with credibility and possibility and statistic model. Also, the present model has shown the better smoothness of the variations of returns with respect to risk aversion parameter, λ, from the monthly data from October 2015 to September 2016, which is not included to training database. Especially, our model is superior to other models in the regime of 0-0.3 for the risk aversion level. It is demonstrating that the FPSMSC is efficient for the investors who tend to seek the high return in portfolio management.

In the present work, the volatility of the leading cryptocurrencies is predicted through generalised autoregressive conditional heteroskedasticity (GARCH) models, multilayer perceptron (MLP), long short-term memory (LSTM), and hybrid models of the type LSTM and GARCH, where parameters of the GARCH family are included as features of LSTM models. The study period covered the scenario of the World Health Organization pandemic declaration around March 2020 at hourly frequency. We have found that the different variants of deep neural network models outperform those of the GARCH family in the sense of the hetorerocedastic error, and absolute and squared error (HSE). Under the sharpe ratio, the volatility forecasting of a uniform portfolio at long horizons systematically outperforms the stablecoin Tether, which is considered here as the risk-free asset. Also, including transaction volume helps reduce the value at risk or loss probability for the uniform portfolio. Moreover, in a minimum variance portfolio, it is observed that before the pandemic declaration, a large proportion of the capital was allocated to bitcoin (BTC). In contrast, after March 2020, the portfolio is more diversified with short positions for BTC. Moreover, the MLP models give the best predictive results, although not statistically different in accuracy compared to the LSTM and LSTM-GARCH versions under the Diebold-Mariano test. In sum, MLP models outperform most stylised financial models and are less computationally expensive than more complex neural networks. Therefore, simple learning models are suggested in highly non-linear time series volatility forecasts as it is the cryptocurrency market.

This paper assesses capital mobility for a panel of 15 European countries for the period 1970-2019 using dynamic common correlated effects modeling as proposed in Chudik and Pesaran (J Econ 188(2):393-420, 2015). In particular, we account for the existence of cross section dependence, slope heterogeneity, nonstationarity and endogeneity in a multifactor error correction model (ECM) that includes one homogeneous break. The analysis also identifies the heterogeneous structural breaks affecting the relationship for each of the individual countries. The ECM setting allows for a complete assessment of the domestic saving-investment relationship in the long-run as well as two other elements usually neglected: short-run capital mobility and the speed of adjustment. When we account for a single homogeneous break, this is found at the euro inception. We obtain that long-run capital mobility is high but not perfect yet. We also provide empirical evidence for the Ford and Horioka (Appl Econ Lett, 24(2), 95-97, 2017)'s hypothesis, who argue that goods market integration is a necessary condition to obtain zero correlation between domestic saving-investment. Our results stress the role played by the euro as a booster for both financial and real integration. However, a complete degree of economic integration has not been fully achieved. Short-run capital was highly mobile for the whole period, with some exceptions, coinciding with turmoil episodes. Additionally, from the application of the CS-DL threshold analysis proposed by Chudik et al. (Adv Econ, 36, 85-135, 2016), we find that economic risk and openness play a key role in capital mobility.

Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary (i.e., autocorrelation coefficient ). The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data.

This research studies different gap opening price strategies using artificial intelligence and big data analysis to learn how fast new information is absorbed into the stock's price. Our system is designed to optimize trading results of different gap opening investment strategies. Our data consist of ten years of daily trading prices of all the stocks comprising the three major U.S. stocks indices: S&P 500, Nasdaq100, and Russell 2000. The scope of this research, to the best of our knowledge, has never been attempted before, covering most of the U.S.A. economy across various economic conditions and market trends. We found that negative gap openings are much greater than positive gaps opening. This result is stronger for Russell2000 stocks and Nasdaq100 stocks than for S&P500 stocks. Moreover, consistent with the theoretical framework, the price adjustment for bad news was found to be quicker than for good news. We also found that after positive gaps opening price drifts occur, the stock's price rises even stronger, providing profitable trading opportunities.

In this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business's financial efficiency. The results at the country level can be used to track, improve, and reform the country's short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.

This paper seeks to identify the causal impact of educational human capital on social distancing behavior at workplace in Turkey using district-level data for the period of April 2020 - February 2021. We adopt a unified causal framework, predicated on domain knowledge, theory-justified constraints anda data-driven causal structure discovery using causal graphs. We answer our causal query by employing machine learning prediction algorithms; instrumental variables in the presence of latent confounding and Heckman's model in the presence of selection bias. Results show that educated regions are able to distance-work and educational human capital is a key factor in reducing workplace mobility, possibly through its impact on employment. This pattern leads to higher workplace mobility for less educated regions and translates into higher Covid-19 infection rates. The future of the pandemic lies in less educated segments of developing countries and calls for public health action to decrease its unequal and pervasive impact.

This article is dedicated analyzing the interdependence of oil prices and exchange rate movements of oil exporting countries (the Russian ruble, Euro, Canadian dollar, Chinese yuan, Brazil real, Nigerian naira, Algerian dinar). The study also considers risk-based oil market spillovers in global crisis periods with integrated decision recommendation systems. For this purpose, a fuzzy decision-making model is created by considering the bipolar model and imputation of expert evaluations with collaborative filtering. The main contribution of this study is both its econometric analysis and evaluations based on expert opinions. This helps reach more crucial results. All three of the recent shocks (2008, 2012, 2020) in the oil market are transmitted to foreign exchange markets of oil-producing countries. At the same time, the last shock of 2020 caused by the COVID-19 pandemic has not yet been fully reflected on the Russian ruble exchange rate. Correlation parameters became weaker in the last year, as the Russian ruble correlation coefficient fluctuates between - 0.5 and 0.5. However, before 2020 the spillover effect had a higher significance (in the range from - 0.8 to - 0.1). Nigerian naira and Algerian dinar were showing almost the same movements, while the Russian Ruble was in a different trading range.

MCMC algorithm is widely used in parameters' estimation of GARCH-type models. However, the existing algorithms are either not easy to implement or not fast to run. In this paper, Hamiltonian Monte Carlo (HMC) algorithm, which is easy to perform and also efficient to draw samples from posterior distributions, is firstly proposed to estimate for the Gaussian mixed GARCH-type models. And then, based on the estimation of HMC algorithm, the forecasting of volatility prediction is investigated. Through the simulation experiments, the HMC algorithm is more efficient and flexible than the Griddy-Gibbs sampler, and the credibility interval of forecasting for volatility prediction is also more accurate. A real application is given to support the usefulness of the proposed HMC algorithm well.

Noise is an important factor affecting portfolio performance, how to construct an effective denoising strategy is becoming increasingly important for investors. In this study, we theoretically explain the impact of noise on portfolio and argue the necessity of denoising. Next, the empirical mode decomposition (EMD) denoising strategy based on the correlation coefficient test criterion is proposed to improve portfolio performance. In detail, EMD is used to decompose the noisy price, then, a series of correlation coefficient tests are performed to determine which intrinsic mode functions (IMFs) are noise. In the empirical analysis, we apply the proposed method to denoise the SSE 50 index's constituents, and further test the out-of-sample performance under the mean-variance framework. The empirical results show that the proposed denoising method outperforms four common EMD, Ensemble EMD (EEMD) and wavelet denoising methods in return-risk ratio. The proposed method is the optimal denoising strategy, which can help investors improve portfolio performance to the greatest extent.

Research on the exchange rate volatility and dynamic conditional correlation of African currencies/financial markets interdependence appears to be limited. In this paper, we employ GARCH models to characterize the exchange rate volatility of eight major African currencies. The variation of interdependence with respect to time is described using the DCC-GARCH model. From the results of the DCC, remarkable variations in correlations through time across these countries are observed with the correlations varying from low to moderate, suggesting that African economies are generally governed by certain economic factors and are vastly regulated. These regulations, including exchange rate misalignment led to sluggish and negative growth in most of the African countries. For instance, persistent misalignment can cause high levels of inflation, for example, undervaluation. Overvaluation can lead to trade imbalances and they can in turn create macroeconomic instability and balance of payment problems. Given these results, we suggest that policy makers should revamp and adopt state resilience so as to reduce the negative effect of exchange rate misalignment on economic growth.

Assessing the financial stability of the banking industry, particularly in credit risk management, has become extremely crucial in times of uncertainty. Given that, this paper aims to investigate the determinants of the interconnectedness of sectoral credit risk default for developing countries. To that purpose, we employ a dynamic credit risk model that considers a variety of macroeconomic indicators, bank-specific variables, and household characteristics. Moreover, the SURE model is used to analyze empirical data. We find the connection between macroeconomic, bank-specific, and household characteristics, and sectoral default risk. The outcomes of macroeconomic factors demonstrate that few macroeconomic determinants significantly influence the sector's default risk. The empirical results of household components reveal that educated households play a substantial role in decreasing sectoral loan defaults interconnectedness and vice versa. While for bank-specific characteristic, we find that greater bank profitability and specialization have substantially reduced loan defaults.

We investigate the performance of VaR (Value at Risk) forecasts, considering different multivariate models: HS (Historical Simulation), DCC-GARCH (Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity) with normal and Student's distribution, GO-GARCH (Generalized Orthogonal-Generalized Autoregressive Conditional Heteroskedasticity), and copulas Vine (C-Vine, D-Vine, and R-Vine). For copula models, we consider that marginal distribution follow normal, Student's and skewed Student's distribution. We assessed the performance of the models using stocks belonging to the Ibovespa index during the period from January 2012 to April 2022. We build portfolios with 6 and 12 stocks considering two strategies to form the portfolio weights. We use a rolling estimation window of 500 and 1000 observations and 1%, 2.5%, and 5% as significance levels for the risk estimation. To evaluate the quality of the risk forecasts, we compute the realized loss and cost. Our results show that the performance of the models is sensitive to the use of different significance levels, rolling windows, and strategies to determine portfolio weights. Furthermore, we find that the model that presents the best trade-off between the costs from risk overestimation and underestimation does not coincide with the model suggested by the realized loss.

Bitcoin is a volatile financial asset that runs on a decentralized peer-to-peer Blockchain network. Investors need accurate price forecasts to minimize losses and maximize profits. Extreme volatility, speculative nature, and dependence on intrinsic and external factors make Bitcoin price forecast challenging. This research proposes a reliable forecasting framework by reducing the inherent noise in Bitcoin time series and by examining the predictive power of three distinct types of predictors, namely fundamental indicators, technical indicators, and univariate lagged prices. We begin with a three-step hybrid feature selection procedure to identify the variables with the highest predictive ability, then use Hampel and Savitzky-Golay filters to impute outliers and remove signal noise from the Bitcoin time series. Next, we use several deep neural networks tuned by Bayesian Optimization to forecast short-term prices for the next day, three days, five days, and seven days ahead intervals. We found that the Deep Artificial Neural Network model created using technical indicators as input data outperformed other benchmark models like Long Short Term Memory, Bi-directional LSTM (BiLSTM), and Convolutional Neural Network (CNN)-BiLSTM. The presented results record a high accuracy and outperform all existing models available in the past literature with an absolute percentage error as low as 0.28% for the next day forecast and 2.25% for the seventh day for the latest out of sample period ranging from Jan 1, 2021, to Nov 1, 2021. With contributions in feature selection, data-preprocessing, and hybridizing deep learning models, this work contributes to researchers and traders in fundamental and technical domains.

Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20% drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S&P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes.

Contemporary debates about scientific institutions and practice feature many proposed reforms. Most of these require increased efforts from scientists. But how do scientists' incentives for effort interact? How can scientific institutions encourage scientists to invest effort in research? We explore these questions using a game-theoretic model of publication markets. We employ a base game between authors and reviewers, before assessing some of its tendencies by means of analysis and simulations. We compare how the effort expenditures of these groups interact in our model under a variety of settings, such as double-blind and open review systems. We make a number of findings, including that open review can increase the effort of authors in a range of circumstances and that these effects can manifest in a policy-relevant period of time. However, we find that open review's impact on authors' efforts is sensitive to the strength of several other influences.

Customs unions enable countries to freely access each other's markets, which is thought to increase intra-regional trade and economic growth. However, accession to a customs union also comes with the condition that all members need to consent to a common external trade policy. Especially if countries feature different economic structures, this may act as a force against the creation of large customs unions. In this paper, we propose a new mathematical approach to model the optimal size and composition of customs unions in the form of a bi-objective combinatorial non-linear problem. We also use a multi-objective evolutionary algorithm (NSGA-II) to search for the best (non-dominated) configurations using data on the trade flows and economic characteristics of 200 countries. Our algorithm identifies 445 different configurations that are strictly preferable, from a global perspective, to the real-world landscape of customs unions. However, many of these non-dominated configurations have the feature that they improve outcomes for the world as a whole, on average, but not for all individual countries. The best configurations tend to favour the creation of a few large customs unions and several smaller ones.

The domestic substitution of the IC (the Integrated Circuit) industry improves economic efficiency and is significant in ensuring national security, which has gradually become an essential strategy for countries worldwide. Based on the background of domestic substitution of integrated circuits, we select a typical component Micro Controller Unit) as the research object, construct a three-level supply chain game model under different scenarios in a dynamic architecture, and analyze the game problem of collaborative innovation of the MCU supply chain. We fully consider the impact of factors such as time, cost and the innovation and collaborative innovation efforts of various supply chain members on the level of domestic substitution. Moreover, we put forward a to achieve supply chain coordination. We found that: (1) Collaborative innovation of the supply chain in the centralized decision-making scenario achieves the highest level, followed by the cost-sharing scenario; (2) The can help achieve supply chain coordination; (3) The trend of the MCU domestic substitution level with manufacturing cost is -shaped, which means the increase of manufacturing cost may have a positive impact on the process of domestic substitution.

We test the performance of deep deterministic policy gradient-a deep reinforcement learning algorithm, able to handle continuous state and action spaces-to find Nash equilibria in a setting where firms compete in offer prices through a uniform price auction. These algorithms are typically considered "model-free" although a large set of parameters is utilized by the algorithm. These parameters may include learning rates, memory buffers, state space dimensioning, normalizations, or noise decay rates, and the purpose of this work is to systematically test the effect of these parameter configurations on convergence to the analytically derived Bertrand equilibrium. We find parameter choices that can reach convergence rates of up to 99%. We show that the algorithm also converges in more complex settings with multiple players and different cost structures. Its reliable convergence may make the method a useful tool to studying strategic behavior of firms even in more complex settings.