As of November 14, 2021, all S&P 500 sector indices had recovered to levels above those of January 2020, prior to full economic effects of the global coronavirus (COVID-19) pandemic taking hold. However, different sectors recovered at different rates to sit at widely different levels above their pre-pandemic levels. This suggests that the effect of the coronavirus on financial markets in the United States is directly affected by how the virus has impacted various parts of the underlying economy. Which industry performed the best during the coronavirus pandemic? Companies operating in the information technology (IT) sector have been the clear winners from the pandemic, with the IT S&P 500 sector index sitting at almost ** percent above early 2020 levels as of November 2021. This is perhaps not surprising given this industry includes some of the companies who benefitted the most from the pandemic such as ************** and *******. The reason for these companies’ success is clear – as shops were shuttered and social gatherings heavily restricted due to the pandemic, online services such shopping and video streaming were in high demand. The success of the IT sector is also reflected in the performance of global share markets during the coronavirus pandemic, with tech-heavy NASDAQ being the best performing major market worldwide. Which industry performed the worst during the pandemic? Conversely, energy companies fared the worst during the pandemic, with the S&P 500 sector index value sitting below its early 2020 value as late as July 2021. Since then it has somewhat recovered, and was around ** percent above January 2020 levels as of October 2021. This reflects the fact that many oil companies were among the share prices suffering the largest declines over 2020. A primary driver for this was falling demand for fuel in line with the reduction in tourism and commuting caused by lockdowns all over the world. However, as increasing COVID-19 vaccination rates throughout 2021 led to lockdowns being lifted and global tourism reopening, demand has again risen - reflected by the recent increase in the S&P 500 energy index.

As of December 2023, the Korea Composite Stock Price Index (KOSPI) and the Korean Securities Dealers Automated Quotations (KOSDAQ) index stood at ******* and ******, respectively. After fears of the coronavirus (COVID-19) caused the KOSPI to fall below ***** points for the first time in ten years, the Korean government announced a plan to help financial markets recover. The coronavirus adversely affected the South Korean economy, which, however, quickly recovered as early as 2021.

In the first quarter of 2020, global stock indices posted substantial losses that were triggered by the outbreak of COVID-19. The period from March 6 to 18 was particularly dramatic, with several stock indices losing more than ** percent of their value. Worldwide panic hits markets From the United States to the United Kingdom, stock market indices suffered steep falls as the coronavirus pandemic created economic uncertainty. The Nasdaq 100 and S&P 500 are two indices that track company performance in the United States, and both lost value as lockdowns were introduced in the country. European markets also recorded significant slumps, which triggered panic selling among investors. The FTSE 100 – the leading share index of companies in the UK – plunged by as much as ** percent in the opening weeks of March 2020. Is it time to invest in tech stocks? The S&P 500 is regarded as the best representation of the U.S. economy because it includes more companies from the leading industries. However, helped in no small part by its focus on tech companies, the Nasdaq 100 has risen in popularity and seen remarkable growth in recent years. Global demand for digital technologies has increased further due to the coronavirus, with remote working and online shopping becoming part of the new normal. As a result, more investors are likely to switch to the tech stocks listed on the Nasdaq 100.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

Supplementary information files for the article Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises

Abstract: This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around ** percent of their value compared to January *, 2020. However, Asian markets and the NASDAQ Composite Index only shed around ** to ** percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around ** percent higher than in January 2020, while most other markets were only between ** and ** percent higher. Why did the NASDAQ recover the quickest? Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide. Which markets suffered the most? The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

Context

COVID-19 or Corona Virus is on anyone's lips, since it has affected (and still affecting) a lot of aspects in our lives. From when the virus was first considered a pandemic until now, it has driven the markets crazy, having one of the most significant effects on the past years. No one was able to predict this and none of the financial models was prepared for the huge change the market has suffered. This dataset aims to explain the market evolution before and after the COVID-19

Content

Financial historical data from the World Major Indices, including: Shanghai, FTSE MIB, S&P 500, Nasdaq, Dow 30, Euro Stoxx 50, and much more. The dataset contains: OHLC values, the Volume and the Currency.

Note that the dataset has been generated using investpy an open-source Python package to extract financial data from Investing.com, and you can find all the usage information and documentation at: https://github.com/alvarobartt/investpy.

Inspiration

This dataset aims to explain the market evolution before and after the COVID-19 so as to extract conclusions based on just market data or maybe aggregating external data such as news reports, tweets, etc. so feel free to use this dataset and combine it with others so that we, the community, can develop useful kernels so as to analyse and understand this situation and its impacts. So it is also an open call to researchers, data scientists, financial analysts, etc. so to collaborate together in a market study on the impacts of COVID-19.

Acknowledgements

This dataset been created by Álvaro Bartolomé del Canto using investpy so as to retrieve the historical data from Investing.com. Also, the banner image is property of Investing.com since it is an Investing.com Weekly Comic.

We examine the impact of announcements related to COVID-19 on stock market performance in a small island Caribbean economy, the Trinidad and Tobago Stock Exchange (TTSE).

COVID-19 is an economic shock analogous to the global financial crisis. Now, as then, there will be short-term changes as life, industry, and people adjust, but also more long-term structural changes that will live on long after COVID-19 has passed. This presents opportunities for decisive banks to right-size branch networks, optimize the digital experience, and establish sustainability credentials, thus emerging in a stronger position. But many banks will have been blindsided by this issue, which is evolving in fast and unpredictable ways. As in 2008, banks risk being immobilized by uncertainty, or getting caught in constant firefights that prevent a more considered, strategic response. And the costs of any missteps will be much higher than in 2008, as a variety of banking alternatives – including new digital banks, telcos, and tech companies – are poised and ready to grab market share. Read More

https://static01.nyt.com/images/2020/11/18/nyregion/00nyblind1/merlin_179220645_b77f46ff-a503-40b6-bf2b-4922a676e61b-superJumbo.jpg" alt=""> This dataset offers a comprehensive insight into the economic trajectories of nine major economies from the onset of the COVID-19 pandemic through the beginning of 2024. It encompasses crucial economic indicators and financial market data, covering aspects such as manufacturing and services performance, consumer sentiment, monetary policies, inflation rates, unemployment rates, and overall economic output. Additionally, it includes price data for each economy, with values compared against the dollar for clarity. With data spanning this period, the dataset provides valuable insights for analysts, researchers, and stakeholders into the impact of the pandemic and other significant events on these economies, facilitating an assessment of their resilience, challenges, and opportunities.

Countries included : Australia / Canada / China / Europe / Japan / New Zealand / Switzerland / United Kingdom / United States

Column Descriptions:

• Country : The name of the country.
• Date : The date format (e.g., YYYY-MM-DD).
• Manufacturing PMI : Purchasing Managers' Index (PMI) for the manufacturing sector, indicating the economic health and activity level of the manufacturing industry.
• Services PMI : Purchasing Managers' Index (PMI) for the services sector, indicating the economic health and activity level of the services industry.
• Consumer Confidence : A measure of consumer sentiment or confidence in the economy, indicating consumers' optimism or pessimism about their financial situation and the overall state of the economy.
• Interest Rates : The prevailing interest rates set by the central bank or monetary authority, which influence borrowing costs and investment decisions.
• CPI YoY : Consumer Price Index (CPI) Year-over-Year change, indicating the percentage change in the average price level of a basket of consumer goods and services over the previous year.
• Core CPI : Core Consumer Price Index (CPI), which excludes volatile items such as food and energy prices, providing a measure of underlying inflation trends.
• Unemployment Rate : The percentage of the labor force that is unemployed and actively seeking employment, indicating the health of the labor market.
• GDP YoY : Gross Domestic Product (GDP) Year-over-Year change, indicating the percentage change in the total value of goods and services produced by a country's economy.
• Ticker: Ticker symbol for the corresponding financial asset or index.
• Open: The opening price of the financial asset or index on the specified date.
• High: The highest price of the financial asset or index during the specified date.
• Low: The lowest price of the financial asset or index during the specified date.
• Close: The closing price of the financial asset or index on the specified date.

This package contains the data as well as the Matlab files needed to replicate the empirical findings in "Covid-19 Fiscal Support and its Effectiveness", Economics Letters 205, pp. 109939/1–5: https://doi.org/10.1016/j.econlet.2021.109939

Abstract: This paper uses a threshold-augmented Global VAR model to quantify the macroeconomic effects of countries' discretionary fiscal actions in response to the Covid-19 pandemic and its fallout. Our results are threefold: (1) fiscal policy is playing a key role in mitigating the effects of the pandemic; (2) all else equal, countries that implemented larger fiscal support are expected to experience less output contractions; (3) emerging markets are also benefiting from the synchronized fiscal actions globally through the spillover channel and reduced financial market volatility.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

As the coronavirus spreads around the world, the impact on the Polish stock exchange is increasing. As of 4 March, the WIG20 index was at the level of 1,860.95 points. Since then, the index has been systematically decreasing, and it reached the level of 1,305.73 points on 12 March. The reason for the falls on the stock exchange is a coronavirus (COVID-19). Fear of the epidemic has been visible in the markets for three weeks. As of 27 March, WIG20 has lost over 31 percent since the beginning of the year. Most probably, the first quarter of 2020 will be the worst in the history of the index. Even worse than the end of the memorable 2008, when the financial crisis broke out. On June 29, WIG20 index reached the closing value of 1,769.47, which is a decrease of 17.70 percent compared to the beginning of 2020.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

The Business Jet market will suffer from the COVID-19 crisis due to the lockdowns occurring in a number of geographies, the imposition of quarantines, the economic collapse resulting from the COVID-19 crisis, as well as the cash flow issues impacting buyers, lessors, financial institutions, and manufacturers. Read More

As of April 2020, over ** percent of Canadians told Angus Reid that they were currently in good shape financially, but were worried about the duration of the COVID-19 outbreak. The coronavirus pandemic has resulted in supply chain disruptions, job losses, stock market dips, and increased the risk of recession worldwide.

According to Cognitive Market Research, the global Personal Finance Management Tools Market size was USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2033. Market Dynamics Key Driver

Key Drivers for Personal finance management tools market

Increasing investments in the market: The key Driver of Personal Finance Management Tools 

Increasing investment in the market Is driving the growth of financial tools, enhancing accessibility and efficiency in financial planning. The increasing investment in the market especially after Covid-19 had a significant impact on the expansion of the PFM tools market. The pandemic had a positive impact on the increase in savings and investments in the market due to future uncertainties. For instance, the study conducted on U.S. investors who have personal experience with COVID-19, who are in a vulnerable health category, who tested positive, and who know someone in their close circle of friends or family who died because of COVID-19, increase their investments by 12%. The increase in investment in the market is leading to the rise in the demand for personal finance management tools. For instance, as of 2023 about 3% of the Indian population actively invest in the stock market. This number has gradually grown, prominent reason for growth is access to technology and, more people becoming financially aware. According to NSE, more than 120 million investors were registered between 2019 and 2023 indicating a significant rise in Indian Stock Market. In January 2024 alone over 5.4 million new investors joined.

Rising financial literacy fuels the Financial Management tools market 

Financial literacy empowers individuals to make informed financial choices. The financial literacy rate among its young and adult population has been growing due to various factors including the recent advancement in technology and media coverage. Additionally, the policies formed by the government globally are leading to improved literacy rates. • For instance, the expansion of digital financial services has helped decrease the number of adults without access to an account from 2.5 billion in 2011 to 1.4 billion in 2021, with 76% of the global adult population owning an account by 2021. Countries achieving significant progress have implemented large-scale policies, such as India's Aadhaar initiative, which has provided over 1.2 billion residents with universal digital identification, facilitating the opening of Jan Dhan Yojana (JDY) accounts. Leveraging government payments has also been instrumental; for instance, 35% of adults in low-income countries who received government payments opened their first financial account for this purpose.
• For instance, according to survey each person in China, on average, had 10 accounts and 7 cards at the end of 2023. The steps taken by the government had a significant impact on financial literacy leading to financial inclusion which has made people aware about the investment choices available in the market leading to the expansion in the PFM tools market.

Restraints

Security and compliance risks pose challenges for AI-powered financial tools, making data protection crucial to prevent cyber threats and frauds.

AI-powered financial tools can pose privacy and security risks. Personal financial information is sensitive data that can be vulnerable to cyberattacks and data breaches. It's important to use financial tools that have robust security features in place to protect your information and minimize the risk of unauthorized access. The most common scams in PFM tools include phishing, insider trading, money laundering and mortgage fraud. Phishing attacks are a significant threat to the financial sector, with attackers often targeting financial institutions and individuals to steal credentials or financial information. For instance, in 2024, India saw a 175% surge in phishing attacks targeting the financial sector, with over 135,000 incidents reported from January to June. According to SlashNext’s 2024 Phishing Intelligence Report, a substantial 703% surge in credential phishing attacks was also observed in the same period. AI in financial tools presents compliance challenges related to data privacy, security, algorithmic bias, transparency, and accountability, requiring ...

Russia Debt Securities Issued on Domestic Market: Amount Outstanding: Other Financial Corporations data was reported at 12,912,366.000 RUB mn in Mar 2025. This records an increase from the previous number of 12,417,418.000 RUB mn for Feb 2025. Russia Debt Securities Issued on Domestic Market: Amount Outstanding: Other Financial Corporations data is updated monthly, averaging 2,792,286.500 RUB mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 12,912,366.000 RUB mn in Mar 2025 and a record low of 1,045,141.000 RUB mn in Dec 2012. Russia Debt Securities Issued on Domestic Market: Amount Outstanding: Other Financial Corporations data remains active status in CEIC and is reported by Bank of Russia. The data is categorized under Russia Premium Database’s Financial Market – Table RU.ZD005: Debt Securities Issued on Domestic Market: Amount Outstanding. [COVID-19-IMPACT]

Russia Debt Securities Issued on Domestic Market: Amount Outstanding: NC: Other Financial Corporations data was reported at 10,764,922.000 RUB mn in Mar 2025. This records an increase from the previous number of 10,203,539.000 RUB mn for Feb 2025. Russia Debt Securities Issued on Domestic Market: Amount Outstanding: NC: Other Financial Corporations data is updated monthly, averaging 2,604,479.500 RUB mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 10,764,922.000 RUB mn in Mar 2025 and a record low of 1,000,807.000 RUB mn in Dec 2012. Russia Debt Securities Issued on Domestic Market: Amount Outstanding: NC: Other Financial Corporations data remains active status in CEIC and is reported by Bank of Russia. The data is categorized under Russia Premium Database’s Financial Market – Table RU.ZD005: Debt Securities Issued on Domestic Market: Amount Outstanding. [COVID-19-IMPACT]