Original link:tecdat.cn/?p=5413
Water is essential to life and has been studied to some extent, but not so much in terms of major investments and the risks associated with them. Problems related to climate change increase the risk that more people around the world will lack adequate drinking water. To end this crisis, there is an urgent need to invest in water resources in several areas. This study provides impetus for further research into the financial aspects of the water industry and its associated risks. In particular, this paper contributes by increasing investors’ understanding of the particular risks of water investment. The results and survey results will guide investors’ decisions.
Data and Methods
Data This study uses PHO, PIO, First Trust ISE FIWand Guggenheim S&P Global Water Index ETF (GGW), all from the Thomson Reuters DataStream database. Specific water etFs were selected based on data integrity from the same date as June 15, 2007. The sample period was from June 15, 2007 to August 31, 2015. The daily data used is indexed in the form of price returns; Calculate the value as follows: Rt=ln(Pricet/Pricet−1 x 1002
Rt = ln (Pricet/Pricet – 1 x 1002; Returns are expressed in US dollars (Figure 1).
Figure 1. PHO, PIO, FIW and GGW water exchange-traded fund movements from 2007 to 2015
methods
The estimation of linear regression system parameters obeys two independent systems. , a method to estimate the position of a single switch point is introduced for linear regression systems that follow both schemes. Markov model for switching regression. The journal econometrics presents a particularly useful version of these models, called the Markov switching model. Nonstationary time series and new methods for business cycle economic analysis. Multivariate generalization of the proposed univariate Markov transfer model. In particular, we use Calice, Mio, š těrba and Vaš I ček. Short-term Determinants of special sovereign risk premiums: a policy dependence analysis of European Credit default Swaps.
The empirical results
Data preparation
As can be seen from Table 1, except for PIO, the stock return rate is positive. The highest positive average return (0.013%) was for FIW, while the lowest average return (-0.009) was for PIO. All water ETFs have kurtosis of returns above 3, so the distribution of returns could be fat. Since the skewness value is usually negative, the skewness value is an asymmetric tail. Because of the statistical significance of the Jacque-Bera result, the null hypothesis of the normal distribution of all stock returns is rejected. Nonetheless, our analysis is robust because models are usually robust under abnormal conditions.
Figure 2. Probability of the administration predicted one step ahead.
Figure 3. Filtered regime probabilities.
Figure 4. Smooth regime probability.
conclusion
The purpose of this paper is to investigate the particular risks and returns of water investment. In particular, we use the Markov switching model to examine the time-varying transition probability. The study draws on the daily returns of four water ETFs in a data stream database based on time series data (June 15, 2004 to August 31, 2015). In doing so, the study takes into account regime effects.
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