Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

ABSTRACT From 1953 to 1992, Japan reached the highest economic growth rates among industrialized countries. This performance was achieved despite two oil shocks and the endaka - the continuous rise of the yen vis-à-vis the dollar. This long-term growth cycle came to a sudden halt in early 90’s. Japanese economy stagnated while other industrialized countries continued growing. This was mainly due to the “economic bubkle” burst. From 1990 to 1992, the value of urban land and of the stock market index were cut to almost half. As a result, Japanese banks accumulated US$ 800 billion performing assets. This paper intends to analyse the Japanese “bubble economy crisis” and its long-term impacts on the Japanese economy, on its financial system and on its bilateral relations with the United States.

In 2024, the Tokyo Stock Price Index (TOPIX) hit a daily closing low of ******** points on August 5, when Japan's stock market experienced a historic crash. TOPIX is a free-float adjusted market capitalization-weighted index that has been published by the Tokyo Stock Exchange (TSE) since 1969. The market capitalization as of the base date (January 4, 1968) is set at 100 points.

The USD/JPY exchange rate rose to 155.6000 on December 2, 2025, up 0.09% from the previous session. Over the past month, the Japanese Yen has weakened 0.90%, and is down by 4.00% over the last 12 months. Japanese Yen - values, historical data, forecasts and news - updated on December of 2025.

Snapshot img

Data Resiliency Market Size 2024-2028

The data resiliency market size is forecast to increase by USD 22.26 billion at a CAGR of 18.5% between 2023 and 2028.

The market is witnessing significant growth due to the exponential increase in data generation from various sources, including the Aral Sea's evaporation leading to extensive data from satellite imagery, and the Flint water crisis generating vast amounts of data for environmental monitoring. The attractiveness of blockchain solutions for data resiliency is on the rise, offering enhanced security and immutability. Open-source alternatives are also gaining popularity due to their cost-effectiveness and flexibility. Environmental compliance and public health concerns are driving the need for data resiliency in industries dealing with contaminated wastewater, ensuring operational efficiency and employee safety. Accidents and data loss can lead to severe consequences, including financial losses, reputational damage, and even endangering public health. Sustainability goals are another factor fueling market growth, as organizations aim to minimize their carbon footprint and reduce the risk of data loss. In conclusion, the data resiliency market is experiencing strong growth due to the massive increase in data generation, the need for environmental compliance, and the attractiveness of blockchain solutions and open-source alternatives. The market is expected to continue growing as organizations prioritize operational efficiency, employee safety, and sustainability goals.

What will be the Size of the Market During the Forecast Period?

Request Free Sample

The market is rapidly evolving as organizations prioritize data protection software to safeguard against both cyber mishaps and physical mishaps. Implementing data backup best practices is crucial, with strategies like air-gapped backups and immutable backups ensuring that critical data remains secure from ransomware attacks. Organizations are focusing on achieving error-free backups to minimize risks associated with accidental deletion. Additionally, the importance of encryption for data at rest and data transit cannot be overstated, enhancing security for sensitive information. Understanding the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) is essential for effective data recovery strategies. As businesses increasingly adopt hybrid workloads and SaaS apps, managing endpoints becomes critical. Emphasizing human validation in backup processes and following security best practices will further fortify data resiliency, ensuring that organizations can effectively respond to potential data loss while maintaining operational continuity.



Data resiliency can help mitigate these risks by providing real-time monitoring of wastewater quality and treatment processes, enabling timely intervention and reducing the risk of accidents. Sustainability goals are increasingly becoming a priority in water and wastewater management. Data resiliency can help organizations meet these goals by enabling real-time monitoring and optimization of water and wastewater treatment processes, reducing water usage, and minimizing the generation of hazardous waste. In conclusion, data resiliency plays a crucial role in water and wastewater management, ensuring public health, environmental compliance, operational efficiency, and employee safety. By providing accurate, reliable, and timely data on wastewater quality and treatment processes, data resiliency can help organizations optimize their operations, reduce costs, and minimize risks.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises data resiliency solutions held a significant market share due to their dependable networking communications, resulting in faster performance and lower latency. Organizations prioritizing superior execution across various workload types opt for on-premises implementation. This deployment method is particularly favored by sectors like government, defense, and the Banking, Financial Services, and Insurance (BFSI) industry, as they cannot risk losing sensitive data, financial records, customer information, or monetary transaction details. The relevance of workloads determines the data center's resiliency techniques. Prolonged service interruptions can result in substantial costs, making it crucial fo

With the collapse of the U.S. housing market and the subsequent financial crisis on Wall Street in 2007 and 2008, economies across the globe began to enter into deep recessions. What had started out as a crisis centered on the United States quickly became global in nature, as it became apparent that not only had the economies of other advanced countries (grouped together as the G7) become intimately tied to the U.S. financial system, but that many of them had experienced housing and asset price bubbles similar to that in the U.S.. The United Kingdom had experienced a huge inflation of housing prices since the 1990s, while Eurozone members (such as Germany, France and Italy) had financial sectors which had become involved in reckless lending to economies on the periphery of the EU, such as Greece, Ireland and Portugal. Other countries, such as Japan, were hit heavily due their export-led growth models which suffered from the decline in international trade. Unemployment during the Great Recession As business and consumer confidence crashed, credit markets froze, and international trade contracted, the unemployment rate in the most advanced economies shot up. While four to five percent is generally considered to be a healthy unemployment rate, nearing full employment in the economy (when any remaining unemployment is not related to a lack of consumer demand), many of these countries experienced rates at least double that, with unemployment in the United States peaking at almost 10 percent in 2010. In large countries, unemployment rates of this level meant millions or tens of millions of people being out of work, which led to political pressures to stimulate economies and create jobs. By 2012, many of these countries were seeing declining unemployment rates, however, in France and Italy rates of joblessness continued to increase as the Euro crisis took hold. These countries suffered from having a monetary policy which was too tight for their economies (due to the ECB controlling interest rates) and fiscal policy which was constrained by EU debt rules. Left with the option of deregulating their labor markets and pursuing austerity policies, their unemployment rates remained over 10 percent well into the 2010s. Differences in labor markets The differences in unemployment rates at the peak of the crisis (2009-2010) reflect not only the differences in how economies were affected by the downturn, but also the differing labor market institutions and programs in the various countries. Countries with more 'liberalized' labor markets, such as the United States and United Kingdom experienced sharp jumps in their unemployment rate due to the ease at which employers can lay off workers in these countries. When the crisis subsided in these countries, however, their unemployment rates quickly began to drop below those of the other countries, due to their more dynamic labor markets which make it easier to hire workers when the economy is doing well. On the other hand, countries with more 'coordinated' labor market institutions, such as Germany and Japan, experiences lower rates of unemployment during the crisis, as programs such as short-time work, job sharing, and wage restraint agreements were used to keep workers in their jobs. While these countries are less likely to experience spikes in unemployment during crises, the highly regulated nature of their labor markets mean that they are slower to add jobs during periods of economic prosperity.

From the onset of the Global Financial Crisis in the Summer of 2007, the world economy experienced an almost unprecedented period of turmoil in which millions of people were made unemployed, businesses declared bankruptcy en masse, and structurally critical financial institutions failed. The crisis was triggered by the collapse of the U.S. housing market and subsequent losses by investment banks such as Bear Stearns, Lehman Brothers, and Merrill Lynch. These institutions, which had become over-leveraged with complex financial securities known as derivatives, were tied to each other through a web of financial contracts, meaning that the collapse of one investment bank could trigger the collapse of several others. As Lehman Brothers failed on September 15. 2008, becoming the largest bankruptcy in U.S. history, shockwaves were felt throughout the global financial system. The sudden stop of flows of credit worldwide caused a financial panic and sent most of the world's largest economies into a deep recession, later known as the Great Recession. The World Economy in recession
More than any other period in history, the world economy had become highly interconnected and interdependent over the period from the 1970s to 2007. As governments liberalized financial flows, banks and other financial institutions could take money in one country and invest it in another part of the globe. Financial institutions and other non-financial companies became multinational, meaning that they had subsidiaries and partners in many regions. All this meant that when Wall Street, the center of global finance in New York City, was shaken by bankruptcies and credit freezes in late 2007, other advanced economies did not need to wait long to feel the tremors. All of the G7 countries, the seven most economically advanced western-aligned countries, entered recession in 2008, before experiencing an even deeper trough in 2009. While all returned to growth by 2010, this was less stable in the countries of the Eurozone (Germany, France, Italy) over the following years due to the Eurozone crisis, as well as in Japan, which has had issues with low growth since the mid-1990s.

The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.

From the Summer of 2007 until the end of 2009 (at least), the world was gripped by a series of economic crises commonly known as the Global Financial Crisis (2007-2008) and the Great Recession (2008-2009). The financial crisis was triggered by the collapse of the U.S. housing market, which caused panic on Wall Street, the center of global finance in New York. Due to the outsized nature of the U.S. economy compared to other countries and particularly the centrality of U.S. finance for the world economy, the crisis spread quickly to other countries, affecting most regions across the globe. By 2009, global GDP growth was in negative territory, with international credit markets frozen, international trade contracting, and tens of millions of workers being made unemployed.

Global similarities, global differences

Since the 1980s, the world economy had entered a period of integration and globalization. This process particularly accelerated after the collapse of the Soviet Union ended the Cold War (1947-1991). This was the period of the 'Washington Consensus', whereby the U.S. and international institutions such as the World Bank and IMF promoted policies of economic liberalization across the globe. This increasing interdependence and openness to the global economy meant that when the crisis hit in 2007, many countries experienced the same issues. This is particularly evident in the synchronization of the recessions in the most advanced economies of the G7. Nevertheless, the aggregate global GDP number masks the important regional differences which occurred during the recession. While the more advanced economies of North America, Western Europe, and Japan were all hit hard, along with countries who are reliant on them for trade or finance, large emerging economies such as India and China bucked this trend. In particular, China's huge fiscal stimulus in 2008-2009 likely did much to prevent the global economy from sliding further into a depression. In 2009, while the United States' GDP sank to -2.6 percent, China's GDP, as reported by national authorities, was almost 10 percent.

The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.