A “silver economy” can drive economic growth. The key condition is effective demand, determined by the number of financially secure members of the elderly population. The aim of this study is to assess the conditions of the Russian “silver economy”, identify the constraints on its growth, and develop recommendations for their elimination to achieve Sustainable Development Goals. We applied multivariate statistical analysis methods. The absolute and structural numbers of elderly people in Russia were found to not differ much from those in the developed countries of Europe. Their financial support exhibits several important features. A state pension plays a key role in financing the needs of Russian pensioners. Income from labor occupies the second position. Asset-based reallocations are negligible. Public programs will improve the standard of living of current pensioners. For future pensioners, it is important to increase the income received from asset-based reallocations. Russian pensioners were found to have had a negative experience of participation in the funded pension system. It is necessary to stimulate the voluntary participation of future pensioners in the funded pension system and to change the regulation of the investment activities of pension managers. In general, the formation of conditions favorable to the “silver economy” may turn it into a driver of sustainable development in Russia.

Global institutional changes (GICs), having influenced energy prices, led to a steady upward trend in carbon prices on the EU ETS. The aim of the article is to assess the changes in the relationship between carbon prices and energy prices under GICs. The Bai–Perron tests for structural breaks identified two dates as the breakpoint, 21 April 2016 and 21 September 2020. We test the hypothesis that powerful external factors (GIC) are changing the trend pattern of the carbon price time series. New pricing rules of the carbon price are being formed after the breakpoint. We use daily observations from 4 January 2010 to 1 September 2022. We use GARCH models with multiple stationary time series to discover a relationship energy price with the carbon price before and after the break points. We found that three models for two breakpoints better describe the relationship between carbon prices and energy prices than two models for one breakpoint, much less one model for the entire period. We find that the carbon price depends on energy prices, especially on the price of oil, in a statistically significant way, but the gas price is not statistically significant after 21 September 2020.

Russia is taking the first steps in the formation of an emissions trading system. In this article, we studied the impact of carbon risk on Russian stock returns. We link carbon risk to CO₂ emissions and air protection costs. We suggest that carbon firms are exposed to carbon risk and hence require a premium in stock returns. We use an approach based on the asset pricing methodology for carbon, carbon-free, and “carbon-minus-carbon-free” portfolios. Based on the Newey–West estimate, we perform a linear regression analysis for the period from January 2014 to December 2021. We find a positive and statistically significant carbon premium. This means that carbon firms show higher expected returns. Carbon risk does not have a statistically significant impact on the carbon premium. The carbon firms’ stock returns are not sensitive to CO₂ emissions and air protection costs. Our analysis shows that a quarter of the carbon premium is explained by the market premium and is not sensitive to size, value, and momentum premiums. Our results inform policymakers and investors about the implications of environmental regulation. Policymakers should take into account the results obtained in the development of national climate and, in general, environmental policies.

This article examines reasons for the low efficiency of investment activity by pension asset managers, and pension investment rules are formulated. These rules are based on the Asset Allocation strategy, taking into account the long-term pension investments and the life-cycle investment strategy. All pension portfolios of Russiаn managers have weak diversification by asset classes, a high share of fixed income financial instruments, and a mismatch of the portfolio structure with the risk profile of the beneficiary. The pension industry has high costs. We evaluated the real efficiency of investment activity by pension asset managers according to the classical theory of investments, and compared it with the risk-return benchmarks of the Russian financial market. The real cumulative return by pension asset managers is negative for the period 2008–2018. At the same time, the Russian financial market provided opportunities for real growth of pension savings. Bank deposits allowed to defend capital from depreciation. Modeling of index pension portfolios (conservative, balanced, and aggressive) in the Russian financial market, according to pension investment rules, showed a positive impact on investment management efficiency of regular rebalancing of the portfolio containing stocks. The management of index pension portfolios by the proposed rules protect pension savings against inflation. Pension asset managers improve the investment policy efficiency following the pension investment rules.

Reasonable investing is impossible without assessing risk premiums. In finance, the following set of risk premiums is commonly used: market risk premium, size premium, value premium, profitability premium, investments premium and momentum premium. In addition, in the context of the current trend in the development of the global financial system towards responsible investment, a responsibility premium can be distinguished. The importance of studying responsibility premium in the Russian stock market is due to the desire of the Bank of Russia to transform it towards greater responsibility and introduce ESG factors into the activities of institutional and retail investors. The purpose of this article is to identify risk premiums that are statistically significant for the Russian stock market. The study period: from 01.12.2011 to 31.12.2020. As a result of applying the two-step econometric Fama-MacBeth procedure, which includes time series regression and panel regression, it is concluded that market risk premium and momentum premium (at the level of 1%), as well as profitability premium (at the level of 5%) showed statistical significance during the period under consideration. In addition, these premiums are positive. The remaining premiums, including responsibility premium, did not show statistical significance. According to the results, only three premiums should be used when constructing asset pricing model in the Russian stock market. This result has practical implications in the context of investing in Russian stocks. An independent result is the assessment of responsibility premium for Russian shares. The statistical insignificance of responsibility premium allows us to make a reasonable assumption that the introduction of ESG factors into the activities of Russian institutional and retail investors will not reduce the effectiveness of their investment decisions.

In modern conditions, the integration of ESG-criteria into investment decisions of asset managers is considered as a key factor in sustainable economic development. We examine the effectiveness of the Momentum-ESG strategy based on the Responsibility and Openness Index in comparison with the Momentum strategy, which is based on the Moscow Exchange Broad Market Index, since December 2011 to December 2020. We propose an algorithm for integrating ESG criteria into momentum strategy. We select "winners" and "losers" stocks based on their monthly return. The Momentum-ESG strategy has a high Sharpe ratio for a time horizon of 12 months, the Momentum strategy - for a time horizon of 6 months. The testing of the Momentum-ESG strategy shows its greater efficiency in terms of the Sharpe ratio compared to the Momentum strategy, which does not take into account the ESG-factors, in the investing period.