Russia's main stock market index, the MOEX, fell to 2681 points on December 2, 2025, losing 0.20% from the previous session. Over the past month, the index has climbed 4.30% and is up 5.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on December of 2025.

On March 9, 2020, also referred to as Black Monday 2020, a global stock market crash took place, stemming from the collapse of the OPEC deal and the economic impact of the coronavirus (COVID-19). As a result, Russian oil companies suffered the most significant falls in shares. Lukoil and Rosneft saw their shares plunge by **** and **** percent, respectively.

Abstract (en): A primary purpose of the Federal Reserve Act of 1913 was to prevent banking panics by establishing the Federal Reserve System to function as a lender of last resort. Other types of financial crisis require a similar response, however, and the Federal Reserve has repeatedly used its capacity to generate liquidity to insulate the economy from crises in financial markets. The Fed's response to the terrorist attacks of September 11, 2001, is the most recent example of this. This paper reviews the Fed's responses to crises and potential crises in financial markets: the stock market crash of 1987, the Russian default, and the September 11th attacks. Files submitted are the data file 0403cnd.xls and the program file 0403cnp.prg. These data are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.

This study examines the correlation among Bitcoin (BTC), gold, equity, bonds, and exchange rate volatility in the context of new developments during Russia Ukraine conflict using daily data from January 01, 2018, to May 30, 2023. Three GARCH estimation models are utilized to capture the hedging, diversification, and safe haven properties of Bitcoin in Russian financial market. The results indicate from GJR-GARCH estimation model exhibits that BTC has hedging ability against the bonds and gold that enables investors to diversify the risk among the underline financial assets. In addition, value at risk and conditional value at risk estimations are employed to estimate potential losses in the portfolio during the crisis. The study observes a significant increase in Bitcoin investments during crisis, leading to heightening the volatility and uncertainty where negative news has a stronger impact compared to positive news which underscores the importance of prudent asset allocation for risk mitigation. The study provides notable policy implications within the context of the ongoing crisis between Russia and Ukraine.

In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets between 2007 and 2024, covering multiple financial crises, including the Global Financial Crisis (GFC), the European Sovereign Debt Crisis (ESD), and the COVID-19 pandemic. By training the RNN-AE with normalized stock returns, we derive correlations embedded in the model’s weight matrices. To explore the network structure, we construct threshold networks based on the middle-layer weights for each year and examine key topological metrics, such as entropy, average clustering coefficient, and average shortest path length, providing new insights into the dynamic evolution of global stock market interconnections. Our method effectively captures the major financial crises. Our analysis indicates that interactions among American indices were significantly higher during the GFC in 2008 and the COVID-19 pandemic in 2020. In contrast, interactions among European indices were more prominent during the 2022 Russia-Ukraine conflict. In examining net inter-continental interactions, the influence was stronger between Europe and America during the GFC and the ESD crisis while, the influence between America and Asia was more powerful during the COVID-19 pandemic. Finally, we determine the structural entropy of the constructed networks, which effectively monitors the states of the market. Overall, our RNN-AE based network construction method provides valuable insights into market dynamic and uncovering financial crises, offering a powerful tool for investors and policymakers.

This study investigates the dynamic and asymmetric propagation of return spillovers between sectoral commodities and industry stock markets in China. Using a daily dataset from February 2007 to July 2022, we employ a time-varying vector autoregressive (TVP-VAR) model to examine the asymmetric return spillovers and dynamic connectedness across sectors. The results reveal significant time-varying spillovers among these sectors, with the industry stocks acting as the primary transmitter of information to the commodity market. Materials, energy, and industrials stock sectors contribute significantly to these spillovers due to their close ties to commodity production and processing. The study also identifies significant asymmetric spillovers with bad returns dominating, influenced by major economic and political events such as the 2008 global financial crisis, the 2015 Chinese stock market crisis, the COVID-19 pandemic, and the Russia-Ukraine war. Furthermore, our study highlights the unique dynamics within the Chinese market, where net information spillovers from the stock market to commodities drive the financialization process, which differs from the bidirectional commodity financialization observed in other markets. Finally, portfolio analysis reveals that the minimum connectedness portfolio outperforms other approaches and effectively reflects asymmetries. Understanding these dynamics and sectoral heterogeneities has important implications for risk management, policy development, and trading practices.

ABSTRACT Purpose: Verify the effects of the subprime mortgage crisis on firms with different debt levels. Originality/gap/relevance/implications: The study contributes to the literature by examining the financial capital structure of emerging market companies in a context of crisis, in addition to using a robust econometric tool - the quantile regression. Key methodological aspects: In this work, the quantile regression was used as an analysis tool, and this technique allowed to observe the impacts of the crisis not only in the average level of indebtedness of companies, but also in its extreme values. Therefore, this work is characterized as descriptive, and the analyzed relations are quantitative. Firms were analyzed in Brazil, Russia, India and China. Summary of key results: The results indicate financing strategies, according to the theories of Pecking Order and Trade-off as regards to the level of debt. Key considerations/conclusions: The survey concluded that firms have different financing strategies even in the same country. Thus, the financial performance of firms would be influenced by economic conditions in the country, as well as the existing debt level in each company.