Run Trends Research Articles

Stochastic Control Problems with State Reflections Arising from Relaxed Benchmark Tracking

This paper studies stochastic control problems motivated by optimal consumption with wealth benchmark tracking. The benchmark process is modeled by a combination of a geometric Brownian motion and a running maximum process, indicating its increasing trend in the long run. We consider a relaxed tracking formulation such that the wealth compensated by the injected capital always dominates the benchmark process. The stochastic control problem is to maximize the expected utility of consumption deducted by the cost of the capital injection under the dynamic floor constraint. By introducing two auxiliary state processes with reflections, an equivalent auxiliary control problem is formulated and studied, which leads to the Hamilton-Jacobi-Bellman equation with two Neumann boundary conditions. We establish the existence of a unique classical solution to the dual partial differential equation using some novel probabilistic representations involving the local time of some dual processes together with a tailor-made decomposition-homogenization technique. The proof of the verification theorem on the optimal feedback control can be carried out by some stochastic flow analysis and technical estimations of the optimal control. Funding: L. Bo and Y. Huang are supported by the National Natural Science Foundation of China [Grant 12471451], the Natural Science Basic Research Program of Shaanxi [Grant 2023-JC-JQ-05], the Shaanxi Fundamental Science Research Project for Mathematics and Physics [Grant 23JSZ010], and Fundamental Research Funds for the Central Universities [Grant 20199235177]. X. Yu is supported by the Hong Kong RGC General Research Fund (GRF) [Grant 15304122] and the Research Centre for Quantitative Finance at the Hong Kong Polytechnic University [Grant P0042708].

Modeling Asymmetric Effects of Exchange Rate Fluctuations on Agricultural Trade Balance: Evidence from Iran and Iraq

The exchange rate plays a crucial role in foreign trade and has asymmetric effects. This study examines the asymmetric effects of exchange rate volatility on the trade balance in the agricultural sector between Iran and Iraq, using the non-linear ARDL model from 1998 to 2020. The results show that Iraq's GDP and oil price fluctuations positively affect the trade balance. In contrast, Iran's GDP and the U.S. economic sanctions against Iran have negative and significant effects on the trade balance of Iran with Iraq in the agriculture sector. The results do not confirm the existence of the J-curve effect in the trade relations between Iran and Iraq, because an increase in the bilateral exchange rate, in both the long-run and short-run, improves the trade balance of Iran with Iraq in the agriculture sector. Furthermore, the positive and negative fluctuations of the bilateral exchange rate have different effects on the trade balance. While the devaluation of the national currency does not cause a downward trend in the short run, the exchange rate policy can improve the trade balance from the beginning.

An examination of trends in U.S. orange and grapefruit juice consumption

AbstractThe average U.S. consumer has been drinking less orange and less grapefruit juice over time. Per capita consumption is down more than 50% since the mid‐2000s. In this study, we propose and estimate a count data model. The number of times an adult consumes 100% orange or 100% grapefruit juice over a 2‐day period is modeled as a function of income, product prices, and some characteristics of the consumer such as level of education, being on a diet, and taking nutrient supplements that proxy for health concerns and nutrition knowledge. Data used in the study were collected between January 2005 and March 2020 on over 30,000 adult consumers. Tests for overdispersion favor a negative binomial model instead of the more restrictive Poison regression model. Estimation results confirm that rising juice product prices and concern over product healthfulness have been contributing to long run trends in orange and grapefruit juice consumption. Prices for these citrus juices have increased in recent years due in part to the citrus greening disease and hazardous weather in Florida. Trends in orange and grapefruit juice consumption can impact consumer diet quality, fruit growers, and agribusiness firms responsible for transforming raw fruit into juice and juice products. [EconLit Citations: D12, Q13].

Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate

Abstract. Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in ∼ 100 000 existing reservoirs. Here, we use the finest-spatiotemporal-resolution (1 d and 1 km) precipitation dataset from an ensemble of machine learning algorithms to present the spatial distribution of 1 d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find that the national PMP generally decreases from southeast to northwest and is typically dominated by the high variability of precipitation extremes in northern China and high intensity in southern China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing them with satellite and in situ results due to differences in spatial scales and computation methods. Interannual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land–atmosphere coupling effects, leads to a widespread increase (> 20 %) in PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emissions. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

Multidimensional measurement of the High-Quality development of city Clusters: Dynamic Evolution, regional differences and trend forecasting--based on the basic connotation of Chinese-style modernization

Chinese-style modernization has led to new requirements for city clusters strategies, which is highly important for the high-quality development (HQD) of city clusters and new urbanization. Based on the basic connotation of Chinese-style modernization, an evaluation index system for the HQD of city clusters is constructed from five dimensions, namely, demographic advantages, coordinated regional development, material and cultural-ethical advancement, ecological sustainability, and opening-up. The HQD of 19 city clusters was measured from 2011 to 2021 using the entropy weight-CRITIC method, and the dynamic evolution, spatial correlation and long-term trend were analyzed. The study revealed that (1) the overall HQD of Chinese city clusters has shown continuous growth, and the spatial distribution of the gradient of HQD in the 'East, Center and West' has decreased, but the spatial pattern of 'South High and North Low' has further emerged. (2) The overall and intra-regional differences in the HQD of city clusters gradually narrowed, while the differences between city clusters remained significant. (3) The HQD of city clusters shows obvious positive spatial correlation and obvious spatial agglomeration characteristics, with high-value agglomerations mostly in mature-stage city clusters along the east coast. (4) There is an obvious 'club convergence' phenomenon in the HQD of city clusters; the higher the HQD of neighboring regions is, the greater the probability of upward HQD of city clusters, and the higher the quality of Chinese city clusters shows an upward trend in the long run.

The digitalisation, dematerialisation and decarbonisation of the global economy in historical perspective: the relationship between energy and information since 1850

To better understand the processes of digitalisation, dematerialisation and decarbonisation, this paper examines the relationship between energy and information for the global economy since 1850. It presents the long run trends in energy intensity and communication intensity, as a proxy for total information intensity. The evidence suggests that, relative to GDP, global economic production has been reducing energy and increasing information use since 1913. The analysis indicates that it initially required little information to replace energy in production and that the ability to substitute away from energy and towards information has been declining. The result implies that the global economy is now reducing energy and increasing information at a substitution rate of 0.2 kB per kWh of conserved energy or 0.8 GB per tonne of carbon dioxide mitigated. As the price ratio of energy to information is currently higher than this marginal rate of substitution, there are incentives to further substitute information for energy. However, one conclusion is that (without the long run escalation of carbon prices) substitution away from energy and towards information is likely to cease within the next few decades and, beyond that, digitalisation will play a declining role in the decarbonisation process.

The Environmental Sustainability Study of an Airport Building System Based on an Integrated LCA-Embodied Energy (Emergy)-ANN Analysis

From a global perspective, the ecological sustainability of building systems has always been a hot research topic, especially in China, where the annual amount of new construction is nearly half of the world. The difficulty is making a complete and accurate ecological assessment of the building system. This study has designed and adopted the LCA-Emergy-ANN framework to assess and analyze an airport building system for sustainability. The results demonstrate that building material emergy and operational stage emergy play a critical role and account for 92.4% of the entire emergy, which are the primary contributors. As the vital indicator, the emergy sustainability index (ESI) is 0.669, which is unsustainable (The eligibility standard is 1). Simultaneously, to ensure the accuracy of the data results, sensitivity analysis was performed. The artificial neural network (ANN) was used by integrating the LCA method and emergy approach to predict the sustainability trend in the long run. In the end, the optimization strategy is proposed to enhance the sustainability of the building system.

Coronavirus, Vaccination and the Reaction of Consumer Sentiment in The United States: Time Trends and Persistence Analysis

At the beginning of the COVID-19 pandemic, the entire world was waiting for a medical solution (for example, vaccines) in order to return to normality. Sanitary restrictions changed our consumption behaviors and feelings. Therefore, this paper analyzes the stochastic properties of consumer sentiment during the COVID-19 episode and the appearance of vaccines against the virus in December 2020 in the United States of America. This study adds a new dimension to the literature because it is the first research paper that uses advanced methodologies based on fractional integration and fractional cointegration analysis to understand the statistical properties of these time series and their behavior in the long term. The results using fractional integration methodologies exhibit a high degree of persistence, finding behavior of mean reversion during the pandemic episode. Therefore, the shock duration in consumer sentiment will be transitory, recovering to its previous trend in the short run. Focusing on the cointegrating part, we arrive at two main conclusions. First, an increase in total vaccination produces a positive reaction or impact on the behavior of consumers. On the other hand, an increase in new COVID-19 cases negatively affects the behavior of the consumer.

Twin transitions of decarbonisation and digitalisation: A historical perspective on energy and information in European economies

This paper investigates the structural transformation associated with the ‘twin transition’ of decarbonisation and digitalisation in European economies by placing it in a broader historical perspective. With this in mind, this paper analyses the long run trends in energy intensity and communication intensity since 1850. The evidence indicates that these economies experienced a coevolution of energy and communication intensities during their industrialisation phase, followed by a divergence in the energy and communication intensities associated with the development of high tech and ICT. Overall, this reflects the dematerialisation of these European economies. The paper also analyses the speed of historical energy transitions and communication technology transitions in these economies, finding that communication transitions appear to be substantially faster than energy transitions. The evidence suggests that twin transitions of the decarbonisation and digitalisation of economies are likely to experience a process of imbalanced structural transformation (with ICT continuing to forge ahead). This expectation should guide policy recommendations – increasing the need for low carbon industry to develop and create synergies between the two industries in order to avoid the new industrial revolution being high‑carbon.

Asymmetric effects of economic policy uncertainty on Bitcoin’s hedging power

PurposeEven though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns.Design/methodology/approachThe authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019.FindingsFirst, the authors found a strong evidence of Bitcoin’s mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification.Originality/valueThe study significantly extends the empirical literature of Bitcoin’s portfolio diversification by taking EPU into consideration. To the best of authors’ knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin’s hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil.

Research on fault prediction of marine diesel engine based on attention-LSTM

The marine diesel engine is an important guarantee for the safe operation of the ship. It is of great significance to predict the running trend of the diesel engine and infer whether the diesel engine is in an abnormal working state in advance to ensure the safe operation of the ship. In this paper, combining the feature extraction ability of the attention mechanism (Attention) and the time-series memory ability of the long short-term neural network (LSTM), a prediction model of the exhaust gas temperature of the marine diesel engine is constructed. Meanwhile, in order to improve the prediction accuracy of the model, the improved particle swarm algorithm is used to optimize the structural parameters of the model, and a model with higher prediction performance is obtained. Then, according to the residual value distribution between the predicted value and the actual value of the model, the process control method is used to set the fault threshold, and the experimental data of normal and abnormal conditions are used for verification. The results show that the proposed model can accurately predict developing failures and provide a new method for ship condition maintenance.

Precipitation Trend Analysis of India - A Climate Change Study

Objectives: To identify the trend and variability of precipitation in the Indian subcontinent. Methods: To identify the trend component the non-parametric test statistic Mann - Kendall and Statistical Process Control (SPC) methodologies are used to conduct a statistical study. Findings: Precipitation of India has shown decreasing trend throughout the winter, monsoon, and on an annual basis, and an increasing tendency was observed during the rest of the seasons. During the winter season, there was more variation in precipitation with Coefficient of Variance (CV) is 33.7919%. Novelty: The long period (120 years) average data from 1901 - 2020 is used to know the long run trend and distribution pattern variation of precipitation in India with the proposed statistical methods. Applications: This mechanism is suitable to change the crop pattern and manage the adverse effects in associated sectors. Keywords: Statistical Process Control; India; Mann Kendall Statistic; Precipitation; Trend

Characterization of interannual and seasonal variability of hydro-climatic trends in the Upper Indus Basin

High-resolution seasonal and annual precipitation climatologies for the Upper Indus Basin were developed on the basis of 1995–2017 precipitation normals obtained from four-gridded datasets (APHRODITE, CHIRPS, PERSIANN-CDR, and ERA5) and the quality-controlled high- and mid-elevation station observations. Monthly precipitation is estimated through the anomaly method at the catchment scale, and then, it is compared with the observed discharges over the 1975–2017 period for verification and detection of changes in the hydrological cycle. Running trends and spectral analysis on the precipitation gridded dataset were performed. The Mann–Kendall test was employed to detect the significance of trends whereas the Pettitt test was used to identify change points in precipitation and discharge time series. The results indicate that the bias corrected CHIRPS precipitation, followed by the ERA5, performed better in terms of RMSE, MAE, MAPE, and BIAS against the rain gauge observations. The running trend analysis exhibits a slight increase in annual precipitation, but it shows significant increase in winter precipitation. A runoff coefficient greater than one, especially in the glacierized sub-catchments of Shigar, Shyok, Astore, and Gilgit, indicates that precipitation is likely to be underestimated and glacial melt provides excess runoff volumes in a warming climate. Streamflow variability is found to be pronounced at the seasonal rather than at the annual scale. The annual discharges at Shyok, Gilgit, and Indus at Kachura gauges are slightly significantly increasing. Seasonal discharge analysis reveals more complex regimes, varying in different catchments, and its comparison with precipitation variability favors a deeper understanding of precipitation, snow-, and ice-melt runoff dynamics, addressing the hydroclimatic behavior of the Karakoram region and some weaknesses in the monitoring network at high altitude.

Modelling Macroeconomic and Bank-Specific Determinants of Credit Risk in the Nigerian Banking Sector: Evidence from Bounds Test Approach to Co-Integration

Credit risk, represented in this study by the ratio of non-performing loans to total loan (NPL), is considered as one of the critical factors that causes bank distress and failure. This study examines the macroeconomic and bankspecific determinants of credit risk in the Nigerian Banking sector from the period 1998Q1 to 2018Q4 using the bounds test approach to co-integration. Literature survey in this subject area using Google Scholar resources reveals that there seems to be a consensus of findings in terms of the negative relationship between credit risk and Gross Domestic Product (GDP) growth rate, while other macroeconomic and bank-specific factors tend to have a random pattern relationship with credit risk attributable to various countries’ economic peculiarities. This study shows that GDP growth rate, return on asset, return on equity, interest rate, unemployment rate, and real exchange rate have a negative relationship with NPL. On the other hand, inflation rate, loan deposit ratio, and ratio of bank capital to asset have positive relationship with NPL. The relationships between the three variables and NPL were found to be individually insignificant to explain credit risk trends in the long run. Moreover, the Wald short-run causality test reveals that the macroeconomic and bank specific indicators jointly influence credit risk in the Nigeria banking sector in the short run. This study, however, recommends that since the macroeconomic and bank specific factors were found to be individually insignificant to explain credit risk trend in the long run, consideration should be accorded to some psychological, political, and socioeconomic factors such as the borrower’s attitude, business climate, social dislocations and distortions, availability of good infrastructural facilities, and the direction of government policies. These factors can affect borrowers’ ability to honor their debt obligations and, thus, determine the level of credit risk in the Nigerian economy.

Machine learned hybrid Gaussian analysis of COVID-19 pandemic in India

This article discusses short term forecasting of the Novel Corona Virus (COVID −19) data for infected, recovered and active cases using the Machine learned hybrid Gaussian and ARIMA method for the spread in India. The Covid-19 data is obtained from the World meter and MOH (Ministry of Health, India). The data is analyzed for the period from January 30, 2020 (the first case reported) to October 15, 2020. Using ARIMA (2, 1, 0), we obtain the short forecast up to October 31, 2020. The several statistics parameters have tested for the goodness of fit to evaluate the forecasting methods but the results show that ARIMA (2, 1, 0) gives better forecast for the data system. It is observed that COVID 19 data follows quadratic behavior and in long run it spreads with high peak roughly estimated in September 18, 2020. Also, using nonlinear regression it is observed that the trend in long run follows the Gaussian mixture model. It is concluded that COVID 19 will follow secondary shock wave in the month of November 2020. In India we are approaching towards herd immunity. Also, it is observed that the impact of pandemic will be about 441 to 465 days and the pandemic will end in between April-May 2021. It is concluded that primary peak observed in September 2020 and the secondary shock wave to be around November 2020 with sharp peak. Thus, it is concluded that the people should follow precautionary measures and it is better to maintain social distancing with all safety measures as the pandemic situation is not in control due to non-availability of medicines.

The impact of the quality of democracy on the economic growth of provinces in Indonesia

Abstract Purpose: The impact of democracy on economic growth is an interesting study of economic institutions and there is still debate about the impact on economic growth. One side of the research finds that democracy has a significant and positive impact on economic growth, but the other side states that the improvement of the country's democracy causes economic growth to decline. This study aims to examine the impact of the quality of democracy on economic growth at the provincial level in Indonesia. Research methodology: The data used in this study use panel data using the Eviews 9.0 analysis tool, so that the best method named the Random Effect Model is obtained. Result: The results show that democracy in Indonesia has a significant impact on economic growth and there is a positive trend in the long run. Other variables used are labor and foreign investment, which statistically, if these variables occur, can increase economic growth in Indonesia and increase employment and data on foreign investment play a role in driving economic growth. Economic growth in Indonesia is already in good condition and the economic growth that occurs is convergence growth which shows that some provinces that are poor/underdeveloped can catch up with developed provinces. Limitations: This study uses fairly short time-series data, so that the addition of a longer time-series will of course give better results. Contribution: Improvements in democracy in Indonesia should also strengthen democratic norms that apply in society, such as reducing corrupt behaviors, especially political corruption and money politics to get public office because if this behavior cannot be corrected, then democracy will have little impact on the economy.

Universal pause of the human-perceived winter warming in the 21st century over China

Low-temperature weather accompanied by strong chill wind is considered as a great risk factor for human health in winter, especially in some extreme weather conditions. Based on the observation data and the NCEP/NCAR reanalysis data of air temperature and wind velocity in 1961–2019, the warming pause of wind chill temperature (WCT) in the 21st century in China is first revealed in this paper. A significant increasing trend of WCT is found during 1961–1999 (P1), and a slight decreasing trend in 1999–2019 (P2) is detected by a 21 year running trend analysis. The extreme cold WCT day (WCD) with the WCT index below the 10th percentile also shows a decreasing trend in P1 but a slight increasing trend in P2. Both the WCT and the extreme WCD consistently display the warming pause in seven climatic regions in China. That means the slowdown or even decrease of human bioclimatic temperature in recent two decades may lead to an increasing risk of frostbite and other cold-related diseases in the country. Both the decreasing trend of mean temperature and the increasing trend of the wind speed contribute to the slowdown of the human-perceived warming in the 21st century, and this conjoint contribution could be linked to the East Asian winter monsoon circulations over Siberia, i.e. the Siberian high. The variation of averaged sea level pressure over the central region of the Siberian high shows high consistency with both the WCT and the extreme WCD in the whole study period, by a decreasing rate of −1.28 hPa per decade in P1 and an increasing rate of 1.26 hPa per decade in P2.

Deep Learning Algorithm-Based Financial Prediction Models

In this paper, a new FEPA portfolio forecasting model is based on the EMD decomposition method. The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, multiscale complex financial time series to predict stock market indices and foreign exchange rates and empirically investigate this hot area in financial market research. The combined forecasting model proposed in this paper is based on the idea of decomposition-reconstruction synthesis, which effectively improves the model’s prediction of internal financial time series. In this paper, we select the CSI 300 Index and foreign exchange rate as the empirical market and data and establish seven forecasting models to make predictions about the short-term running trend of the closing price. The interval EMD decomposition algorithm is introduced in this paper, considering both high and low prices to be contained in the input and output. By analyzing the closing price, high and low prices of the stock index at the same time, the volatility of this interval time series of the index and its trend can be better captured.

Infection Is the Cycle: Unemployment, Output and Economic Policies in the COVID-19 Pandemic

ABSTRACT This paper seeks to capture the dynamic interaction between the epidemiological evolution of COVID-19 and its effect on the macroeconomy, in absence of widespread vaccination. We do that by building a stylized two-equations dynamical system in the COVID-19 positivity rate and the unemployment rate. The solution of the system makes the case for an endemic equilibrium of COVID-19 infections, thus producing waves in the two variables in the absence of widespread immunity through vaccination. Furthermore, we model the impact of the pandemic-driven unemployment shock on output, showing how the emergence of cyclical downswings could determine a L-shaped recession in the medium run, in absence of adequate stimulus policies. Moreover, we simulate the model, calibrating it for the US. The simulation highlights the effects on unemployment and on overall economic activity produced by recurrent waves of COVID-19, which risk to jeopardize the coming back to the pre-crisis trend in the medium run.

The Trump Depression

The Trump Depression in 2020 resulted from the long run trends in U.S. capitalism toward enormous inequality and resulting lack of consumer demand. Trump added incredible mistakes toward the coronavirus in order to help his political popularity. The resulting depression had the sharpest and deepest trough of any in U.S. history in such a short time.