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In this research, the U.S. investor sentiment effect on cryptocurrency returns and volatility is examined by separating it into irrational and rational parts. According to the data, an unforeseen rise in the rational part of U.S. individual investor attitude influences cryptocurrency returns statistically and positively. In other words, rational sentiment can result in rising cryptocurrency returns. Additionally, a positive significant association exists between cryptocurrency volatility and the rational part of the individual U.S. investor sentiment. The findings confirm the hypothesis that the behavior of rational investors who utilize and study the impact of economic factors on asset prices reduces cryptocurrency volatility.

The study aims to examine the relationships between variables from different perspectives by using Turkey’s Real exchange rate (TL/USD), Real interest rate and Consumer price index data. Data from 2012M7 to 2021M12 were used in the study. In order to examine the relationships between the variables, seasonality tests and stationarity studies, which are among the time series analysis methods, were performed. Then, the model was estimated within the scope of VAR Analysis, the compatibility of the model with the real data was checked, the validity and reliability tests of the model were made and the residuals were examined. Inter-variable Impact Response Function and Variance Decomposition statistics are discussed for the model that meets all assumptions. The use of current data in the study and the use of graphics for qualitative evaluation contributed to the literature. As a result of this study, it has been determined that the consumer price index moves independently of other variables, and there is a limited relationship between exchange rate and real interest in every respect. In the first part of the study, the introduction and the theoretical framework are discussed. In the second part, the literature is examined, and in the third part, the methods and applications used in the study are given. The last part is the conclusion and discussion.

We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.

On July 27, the Federal Reserve announced that it would raise the benchmark interest rate by 75 basis points to the range of 2.25%-2.50%, which is 75 basis points for two consecutive interest rate hikes. The interest rate returned to a high level in 2019, which is near the peak of interest rates. However, the Fed’s rate hike is not over, and the market expects another wave of rate hikes in September. The impact of interest rate hikes has been significant in many areas, including the stock markets in China. This paper is based on Stata to analyze data, selecting the stock indexes in China (Shanghai Composite Index, Shenzhen Component Index) and the U.S. (Nasdaq Index, S&P 500 Index) and intercepting their yields after June 2021. The VAR model and ARMA-GARCH model are used to analyze the data, studying how the Chinese and U.S. stock indexes have been affected by the U.S. monetary policy, and making suggestions for the future development of the Chinese stock market based on data analysis.

To analyze the price discovery function of the rapeseed oil futures market is not only helps to improve the price formation mechanism of the oil futures market, but also constructive to the real economy, and promotes the development of the rapeseed oil industry. In this paper, we take the daily price data of the main contract of rapeseed oil futures of Zhengzhou Commodity Exchange from January 2019 to December 2021 and daily spot price as samples. The empirical analysis was undertaken by Stata with the VAR model, Granger model, VECM model, impulse response, and variance decomposition. It was found that the spot price of rapeseed oil played a dominant role in price discovery, while the futures market does not have the capacity for price discovery. According to the conclusion, this paper shows four suggestions to promote the price discovery function in rapeseed oil futures.