Based on examining the origin of Clean Development Mechanism (CDM) from a view of ecological conservation, this paper describes CER time series with GED distribution and discovers its ...heteroskedasticity. The TGARCH and EGARCH models both reflect significant volatility of CER price with GARCH model analysis. In our estimation of Chinese carbon market risk with econometric TGARCH-VaR and EGARCH-VaR models, we find that TGARCH-VaR and EGARCH-VaR models increase the accuracy in measuring the carbon market risk. To achieve the sound and increasing growth of China’s carbon market, integration with the international market, and competition in the international carbon market, we argue the technical methods to reduce the market risk for the benefit of investors in the market competition. Our study provides more methods for China’s carbon market risk measurement, so as to China’s being well-prepared on the way to ecological economic development.
This paper develops a time‐varying parameter vector autoregressive model to examine the dynamic effects of crude oil prices and monetary policy on China's economy during January 1996 to June 2017. ...The empirical results indicate that (a) in general, international crude oil price shocks have positive effect on China's economic growth and inflation in the short run, but the long‐run effect appears diverse; (b) China's monetary policy shocks have positive effect on the economic growth and inflation overall; specifically, an increase in monetary supply can partly offset crude oil prices' negative effect on China's economic growth; (c) China's monetary policy has positive effect on crude oil prices and plays an important role in the relationship between crude oil price shocks and economy; and (d) during the recent global financial crisis, crude oil price shocks produce greater negative effect on China's economic growth, whereas the long‐run effect of monetary policy on China's economic growth proves weaker, compared with other periods.
This paper first analyzes the international financial systemic risk transmission mechanism in the big data environment, constructs an international financial systemic risk index system, and measures ...the index based on principal component analysis. Then, the existing international financial and macroeconomic literature is sorted out, a macroeconomic resilience index system is constructed, and the index is measured using the entropy value method. Finally, MS-VAR and TVP-VAR models are constructed based on the VAR model to analyze the non-linear effects of international financial systemic financial risks on macroeconomic impacts. The results show that a shock of 1 unit of CFSI causes a positive response of MR of about 0.23 in the long run, and CFSI all produce a negative shock to MI until this negative shock reaches a maximum of -0.023 in the 5th period.
English is one of the basic courses in college classroom education, but the current model of college English education gradually reveals some problems. For this reason, this paper researches and ...analyzes the strategies of English innovation education in colleges and universities based on the VAR model. Firstly, this paper constructs a VAR model, on which a TVP-VAR hybrid model is constructed, and four estimation methods of the TVP-VAR model are analyzed in detail. Secondly, we designed the English teaching method based on the TVP-VAR model and the “5C” model of innovative teaching. Based on these models and methods, this paper investigates English majors’ learning status and teachers’ teaching status in several universities in different regions. The analysis shows that the overall attitude of English majors toward learning English is very good, and those who like learning English account for 83.58% of the total number of students surveyed, but 93.6% of the students learn English to deal with exams go to higher education and going abroad, and only 6.4% really like English. Although multimedia in teachers’ teaching methods has exceeded 50% in all four grades, it is still far from matching the information age. Finally, based on the various problems analyzed in the survey, this paper proposes strategies for innovative teaching English in colleges and universities in China.
This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. ...Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference.
The spillover effect is a significant factor impacting the volatility of commodity prices. Unlike earlier studies, this research uses the rolling window-based Quantile VAR (QVAR) model to describe ...the conditional volatility spillover between energy, biofuel and agricultural commodity markets. Since the magnitude of connectedness and spillover effects may switch between bearish and bullish market states over time, a QVAR model is a relatively realistic and appropriate approach to capture the connectedness as compared to the mean-based approaches of Diebold and Yilmaz (DY; 2009, 2012, & 2014) which are mostly used in the literature. To this end, we employ volatility estimates by using the realized variance advanced by Parkinson (1980). Specifically, we investigate the time-varying volatility spillovers and connectedness among agricultural markets (wheat, corn, sugar, soyabean, coffee, and cotton), energy markets (gasoline, crude oil, natural gas) and biofuel (ethanol) markets from January 12, 2012 to May 10, 2021. By comparing our empirical analysis with results from the DY spillover model, we establish that connectedness is stronger in the left and right quantiles than those in the mean and median of the conditional distribution, emphasizing the importance of systematic risk spillovers during extreme market movements. Furthermore, results find that volatility spillovers and connectedness in the right tail is higher than in the left tail. In particular, we document significant volatility spillovers from agricultural markets to energy markets during extreme markets conditions and observe the dominance of agricultural markets over energy markets. To ascertain the impact of COVID-19 on the volatility of markets examined, we divide our sample into sub-samples and observe significant variation in the level of volatility spillovers and connectedness across the markets before and during the outbreak of COVID-19. Finally, some useful implications are summarized for investors' portfolios and risk avoidance.
•Use the rolling window-based Quantile VAR (QVAR) model to describe the conditional volatility spillover.•Investigate the time-varying volatility spillovers and connectedness among agricultural markets, energy markets and biofuel markets.•Results find that volatility spillovers and connectedness in the right tail is higher than in the left tail.•Document significant volatility spillovers from agricultural markets to energy markets during extreme markets conditions•Observe significant variation in the level of volatility spillovers and connectedness across the markets before and during the outbreak of COVID-19.
This study compares the dynamic spillover effects of gold and Bitcoin prices on the oil and stock market during the COVID-19 pandemic via time-varying parameter vector autoregression. Both ...time-varying and time-point results indicate that gold is a safe haven for oil and stock markets during the COVID-19 pandemic. However, unlike gold, Bitcoin's response is the opposite, rejecting the safe haven property. Further analysis shows that the safe-haven effects of gold on the stock market become stronger when the pandemic critically spreads.
•Investigating whether gold or Bitcoin can be considered as a safe haven during the COVID-19 pandemic.•Comparing the dynamic spillover effects of both gold and Bitcoin prices on oil and stock market shocks via TVP-VAR model.•Gold is a safe haven for oil and stock markets during the COVID-19 pandemic. However, Bitcoin is not.•The safe haven effects of gold on the stock market become stronger when the pandemic critically spreads.•Gold can be considered as a safe haven under the impact of most events, whereas Bitcoin is the opposite.
•Researched asymmetric spillover effects between cryptocurrencies and China's financial markets.•The spillover effects of positive volatility and negative volatility are compared.•Using TVP-VAR to ...construct spillover index to study dynamic connectedness characteristics.
In this paper, we constructed a volatility spillover index based on the time-varying parameter vector autoregressions (TVP-VAR) model to study the asymmetric volatility spillover effect between cryptocurrency and China's financial market. Our results show that the impact of cryptocurrency on China's financial market is relatively strong, but the impact of China's financial market on cryptocurrency is very weak. Furthermore, negative spillovers are stronger than positive spillovers. The average negative volatility spillover is dominant for Bitcoin and Ethereum, but the average positive volatility spillover is dominant for Ripple. This study has implications for investors and policymakers.
Imputing missing data from a multivariate time series dataset remains a challenging problem. There is an abundance of research on using various techniques to impute missing, biased, or corrupted ...values to a dataset. While a great amount of work has been done in this field, most imputing methodologies are centered about a specific application, typically involving static data analysis and simple time series modelling. However, these approaches fall short of desired goals when the data originates from a multivariate time series. The objective of this paper is to introduce a new algorithm for handling missing data from multivariate time series datasets. This new approach is based on a vector autoregressive (VAR) model by combining an expectation and minimization (EM) algorithm with the prediction error minimization (PEM) method. The new algorithm is called a vector autoregressive imputation method (VAR-IM). A description of the algorithm is presented and a case study was accomplished using the VAR-IM. The case study was applied to a real-world data set involving electrocardiogram (ECG) data. The VAR-IM method was compared with both traditional methods list wise deletion and linear regression substitution; and modern methods Multivariate Auto-Regressive State-Space (MARSS) and expectation maximization algorithm (EM). Generally, the VAR-IM method achieved significant improvement of the imputation tasks as compared with the other two methods. Although an improvement, a summary of the limitations and restrictions when using VAR-IM is presented.
This paper explores the heterogeneous and dynamic connectedness between the oil and stock markets of emerging economies under various market conditions by introducing a novel quantile regression ...TVP-VAR network method. Moreover, a semiparametric model is used to analyze the impact of interest rates on the connectedness. The results show that (1) the total connectedness between the oil and stock markets of emerging economies in bull and bear markets is significantly larger than under normal market conditions. Moreover, the total connectedness is time-varying and crisis-sensitive in all market scenarios. (2) The total connectedness has asymmetric characteristics in bull and bear markets. The net information spillover from oil markets to stock markets of emerging economies shows heterogeneity under different market backgrounds. (3) The impact of interest rates on the total connectedness exhibits a “U-shaped” curve pattern for all market statuses. This study can serve as a reference for regulators aiming to formulate monetary policies for different market environments, especially extreme markets, and for investors aiming to adjust their investment strategies and optimize their investment portfolios according to market conditions.
•We explore the connectedness between the oil and stock markets under different market status.•A quantile regression TVP-VAR network technology is suggested.•The oil-stock connectednesses in bull and bear markets are larger than those in normal markets.•The total connectednesses of bull and bear markets are asymmetric.•Interest rates have a “U-shaped” effect on the total spillover index for all market periods.
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