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The article describes the characteristics of the types of pastures of the Kamashi district in the Kashkadarya region, the degree of degradation and ways of their rational use. Materials on the species composition of vegetation cover and the improvement of degraded pastures by the introduction of long-term drought-resistant forage species within the framework of the SATREPS BLUE project are presented.

The paper provides a comparative empirical study of predictability of cryptocurrency returns and prices using econometrically justified robust inference methods. We present robust econometric analysis of predictive regressions incorporating factors, which were suggested by Liu, Y., & Tsyvinski, A. (2018). Risks and returns of cryptocurrency. NBER working paper no. 24877; Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689–2727, as useful predictors for cryptocurrency returns, including cryptocurrency momentum, stock market factors, acceptance of Bitcoin, and Google trends measure of investors’ attention. Due to inherent heterogeneity and dependence properties of returns and other time series in financial and crypto markets, we provide the analysis of the predictive regressions using both heteroskedasticity and autocorrelation consistent (HAC) standard-errors and also the recently developed t t -statistic robust inference approaches, Ibragimov, R., & Müller, U. K. (2010). t-statistic based correlation and heterogeneity robust inference. Journal of Business and Economic Statistics, 28, 453–468; Ibragimov, R., & Müller, U. K. (2016). Inference with few heterogeneous clusters. Review of Economics and Statistics, 98, 83–96. We provide comparisons of robust predictive regression estimates between different cryptocurrencies and their corresponding risk and factor exposures. In general, the number of significant factors decreases as we use more robust t-tests, and the t-statistic robust inference approaches appear to perform better than the t-tests based on HAC standard errors in terms of pointing out interpretable economic conclusions. The results in this paper emphasize the importance of the use of robust inference approaches in the analysis of economic and financial data affected by the problems of heterogeneity and dependence.

Rapid stock market growth without real economic back-up has led to the 2015 Chinese Stock Market Crash with thousands of stocks hitting the down limit simultaneously multiple times. We provide a detailed analysis of structural breaks in heavy-tailedness and asymmetry properties of returns in Chinese A-share markets due to the crash using recently proposed robust approaches to tail index inference. The empirical analysis points out to heavy-tailedness properties often implying possibly infinite second moments and gain/loss asymmetry for daily returns on individual stocks. We further present an analysis of the main determinants of heavy-tailedness in Chinese financial markets. It points out to liquidity and company size as being the most important factors affecting the returns’ heavy-tailedness properties. At the same time, we do not observe statistically significant differences in tail indices of the returns on A-shares and the coefficients on factors affecting them in the pre-crisis and post-crisis periods.