China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

Economics,fundamentals,technology,finance,speculation and geopolitics of crude oil prices:an econometric analysis and forecast based on data from 1990 to 2017

It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017-2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017-2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.

Soft measurement for component content based on adaptive model of Pr/Nd color features

For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

The Connection of Vegetation with Tourism Development and Economic Growth： A Case Study for Aruba

Vegetation is an important ecosystem on earth. It influences the earth system in many ways. Any influences on this fragile variable should be investigated, especially in a changing climate. Humans can have a positive or a negative influence on plants. This paper investigates the possible impact of tourism development and economic growth on vegetation health using cointegration and causality for Aruba. The proposed framework contributes to a better understanding on the use of remote sensing of vegetation response to tourism development and economic growth. Thereby, provide opportunities for improving the overall strategy for achieving sustainable development on a small island state. The calculations showed that there were relationships between the tourism demand and economic growth on the vegetation health on Aruba for the western part of the island. On the other hand, for the central part of the island, no relationships were found.

Analysis of the Bovespa Futures and Spot Indexes With High Frequency Data

Data from the World Federation of Exchanges show that Brazil＇s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariate forecasting models based on intraday data from the futures and spot markets of the BOVESPA index. The interest is to verify if there exist arbitrage opportunities in Brazilian financial market. To this end, three econometric forecasting models were built： ARFIMA, vector autoregressive （VAR）, and vector error correction （VEC）. Furthermore, it presents the results of a Granger causality test for the aforementioned series. This type of study shows that it is important to identify arbitrage opportunities in financial markets and, in particular, in the application of these models on data of this nature. In terms of the forecasts made with these models, VEC showed better results. The causality test shows that futures BOVESPA index Granger causes spot BOVESPA index. This result may indicate arbitrage opportunities in Brazil.

Financial Development, Trade Openness, and Economic Growth in Zambia

This study investigates the causal relationship among financial development, trade openness, and economic growth in Zambia from 1965 to 2011. Two measures of financial development were used： broad money and domestic credit to the private sector, each as a ratio of gross domestic product （GDP）. In this regard, two models were developed for each indicator. The Augmented Dickey-Fuller （ADF） and Phillips-Perron （PP） tests were used to determine stationarity of all the variables. Furthermore, Johansen test was employed to ascertain possible cointegration among variables. The vector error correction model （VECM） was employed to examine the short-run and long-run dynamics among the variables in each model. The results indicate that the relationship among financial development, trade openness, and economic growth is sensitive to the financial development indicator chosen.

Price Discovery Function of Index Futures in China: Evidence from Daily Closing Prices

Price discovery is one of the main functions of stock index futures.Using the daily closing prices of the CSI 300 index and its index futures from April 2010 to April 2012,this paper applies a vector error correction model(VECM)and an impulse response function to conduct an empirical analysis on the price discovery function of index futures in China.This paper has the following four findings:(1)a solid cointegration relationship between the CSI 300 index and its index futures exists in the long run;(2)when prices deviate from the longterm equilibrium,the stock index reverses weakly,while the reversal of index futures is much stronger;(3)the daily lead-lag relationship between the prices of the CSI 300 index and its index futures contracts is not significant in the short run;()shocks from the spot market have a lasting impact upon the futures market,but not vice versa,due to the limited short-term adjustment ability of the spot market.