Global Value Chains in the Era of 4IR: New Paradigm of Business Models for SMEs

This paper presents a brief literature review of previous studies methodologies,models,and contexts in studying firms’upgrading in Global Value Chains(GVCs).The...     

Using Artificial Neural Networks to forecast Exchange Rate,including VAR‐VECM residual analysis and prediction linear combination

The Euro US Dollar rate is one of the most important exchange rates in the world, making the analysis of its behavior fundamental for the global economy and for different decision‐makers at both the public and private level. Furthermore, given the market efficiency of the EUR/USD exchange rate, being able to predict the rate's future short‐term variation represents a great challenge. This study proposes a new framework to improve the forecasting accuracy of EUR/USD exchange rate returns through the use of an Artificial Neural Network (ANN) together with a Vector Auto Regressive (VAR) model, Vector Error Corrective model (VECM), and post‐processing. The motivation lies in the integration of different approaches, which should improve the ability to forecast regarding each separate model. This is especially true given that Artificial Neural Networks are capable of capturing the short and long‐term non‐linear components of a time series, which VECM and VAR models are unable to do. Post‐processing seeks to combine the best forecasts to make one that is better than its components. Model predictive capacity is compared according to the Root Mean Square Error (RMSE) as a loss function and its significance is analyzed using the Model Confidence Set. The results obtained show that the proposed framework outperforms the benchmark models, decreasing the RMSE of the best econometric model by 32.5% and by 19.3% the best hybrid. Thus, it is determined that forecast post‐processing increases forecasting accuracy.     

Network VAR models to measure financial contagion

Financial contagion among countries can arise from different channels, the most important of which are financial markets and bank lending. The paper aims to build an econometric network approach to understand the extent to which contagion spillovers (from one country to another) aris from financial markets, from bank lending, or from both. To achieve this aim we consider a model specification strategy which combines Vector Autoregressive models with network models. The paper contributes to the contagion literature with a model that can consider bank exposures and financial market prices, jointly and not only separately. From an empirical viewpoint, our results show that both bilateral exposures and market prices act as contagion channels in the transmission of shocks arising from a country to other countries.     

Current account and credit growth: The role of household credit and financial depth

Understanding the impact of financial variables on the current account balance is one of the priorities of academic literature and policymakers. Evidence from a broad panel of advanced and emerging countries shows that an increase in credit growth is associated with a significant deterioration in the current account balance. When we examine the roles of the components of credit, we find that an increase in household credit causes a significant decline in the current account balance, whereas an increase in business loans has no significant effect. Therefore, our findings indicate that the significant negative impact of credit growth on the current account balance is driven by household credit. Furthermore, we show that total and household credit growth rates have a stronger negative effect on the current account balance for lower levels of financial depth. Our results suggest that targeted policy measures that curb household credit growth might be more effective to reduce external imbalances particularly at the early stages of financial deepening.     

Modelling short- and long-term effects in the aggregate demand for soft drinks

A dynamic regression model for non-durable commodity demand is specified based on the additive unobserved components seasonal model with causal variables. The seasonal component which includes retailer stock effects is modelled as a seasonal ARMA process with fixed temperature effects. The non-seasonal component incorporates both short-run consumer responses and long-run adaptation to steady-state growth paths. Aggregation effects on per capita demand caused by the increase of new consumers as real income grows are investigated and the resulting growth trends accounted for. It is shown that neglecting these trends in the long-run adaptation process is likely to produce biased predictions and misleading estimates of crucial response parameters.     

基于量价关系原理改进RSI指标的量化投资策略研究--以震荡行情的A股市场证券为例

要掌握市场先机,投资者可以采用数学建模和计算机编程相结合的投资策略,以量价关系原理改进的 RSI 指标的量化投资策略,是投资者可以加以借鉴的一种。在改进后的双指标策略的指引下,加上合适的资金管理和风险控制,投资者有望在较低的风险下获得较平稳的收益。     

Demand for improved fish feed in the presence of a subsidy: a double hurdle application in Kenya

Fish farming households’ demand for improved fish feed from the private market in Kenya is potentially influenced by the government's feed subsidy program. This article applies the double‐hurdle model to a cross‐section of fish farms to analyze demand for improved fish feed from private markets, and whether the government feed subsidy program has an effect on private demand for improved feed. The results indicate that households’ decisions to participate in the improved feed market are affected by the quantity of improved feed received from the government. Once the participation decision has been made, we find evidence of crowding‐in of the private improved feed sector; that is, the government's allocations of subsidized feed appear to increase private sector demand. In addition, the price of improved feed negatively affects the quantity purchased as expected. Education, extension contacts, and ease of marketing matured fish increase household propensity to purchase improved feed commercially. Policies that help reduce the price of improved feed such as reduction in tariffs on imported feeds and feed ingredients will foster demand for the feed, as will policies that facilitate marketing of fish at reasonable prices by households.     

Regime switches and commonalities of the cryptocurrencies asset class

In this paper we test for regime changes and possible regime commonalities in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Several parametric models are considered for the joint dynamics of the basket price where parameters are modulated through a Hidden Markov Chain with finite state space. Best specifications within Gaussian and Autoregressive models for price differences are selected by means of the AIC and BIC information criteria and through an out-of-sample forecasting performance. The empirical results, within the period January 2016 to October 2019, suggest that three or four states may be relevant to describe the dynamics of each individual cryptocurrency, depending on the selection criteria, while the entire basket displays at most three common states. Finally, we show how the identification of appropriate models may be exploited in order to build profitable investment strategies on the considered cryptocurrencies.     

Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors

Inflation in the European Monetary Union is measured by the Harmonized Indices of Consumer Prices (HICP) and it can be analysed by breaking down the aggregate index in two different ways. One refers to the breakdown into price indexes corresponding to big groups of markets throughout the European countries and another considers the HICP by countries. Both disaggregations are of interest because in each one, the component prices are not fully cointegrated, having more than one common factor in their trends. The paper shows that the breakdown by group of markets improves the European inflation forecasts and constitutes a framework in which general and specific indicators can be introduced for further improvements.     

Organizational context and human resource management strategy: a structural equation analysis of Hong Kong firms

Previous studies to explain why companies utilize particular human resource management (HRM) strategies have not adequately addressed the influence of contextual variables such as size, location, ownership, competitive pressure, technological change, age and growth. In this study, we investigate the extent to which these contextual variables are related to HRM strategy in seventy-six private-sector firms located in Hong Kong. Our analysis uses structural equations to examine the relationships among contextual variables and HRM strategy to develop and retain managers. The results show that contextual variables have both direct and indirect effects on an organization's HRM strategy. The indirect effects occur through the top management involvement of the HR function within an organization. Use of a human capital development HRM strategy reduces organizational uncertainty about having an adequate supply of managers to meet firm objectives. Contrary to our expectation, in Hong Kong firms, greater reliance on internal development and promotion tends to increase uncertainty and greater competition tends to reduce training investment. Both of these unanticipated relationships may reflect the high mobility of managers peculiar to the Hong Kong labour market.