The article examines causal relationships between sovereign credit default swaps (CDS) prices for the BRICS and most important EU economies (Germany, France, the UK, Italy, Spain) during the European debt crisis. The cross-correlation function (CCF) approach used in the research distinguishes between causality-in-mean and causality-in-variance. In both causality dimensions, the BRICS CDS prices tend to Granger cause those of the EU counterparts with the exception of Germany. Italy and Spain exhibit the highest dependence on the BRICS, whereas only India has a negative balance of outgoing and incoming causal linkages among the BRICS. Thus, the paper underscores the signs of decoupling effects in the sovereign CDS market and also supports the view that the European debt crisis has so far had a limited non-EU impact in this market.

CDS Rates and balance sheet data for the Dutch institutions in the sample

ABSTRACT This paper investigates the drivers of long term real interest rates in Brazil. It is shown that long term yield on inflation linked bonds are driven by yields on 10 year interest rates of United States (US) government bonds and 10 year risk premium, as measured by the Credit Default Swap (CDS). Long term interest rates in Brazil were on a downward trend, following US real rates and stable risk premium, until the taper tantrum in the first half of 2013. From then onwards, real interest rates rose due to the increase in US real rates in anticipation of the beginning of monetary policy normalization and, more recently, due to a sharp increase in Brazilian risk premium. Policy interest rates do not significantly affect long term real interest rates.

Lehman Brothers, the fourth largest investment bank on Wall Street, declared bankruptcy on the 15th of September 2008, becoming the largest bankruptcy in U.S. history. The investment house, which was founded in the mid-19th century, had become heavily involved in the U.S. housing bubble in the early 2000s, with its large holdings of toxic mortgage-backed securities (MBS) ultimately causing the bank's downfall. The bank had expanded rapidly following the repeal of the Glass-Steagall Act in 1999, which meant that investment banks could also engage in commercial banking activities. Lehman vertically integrated their mortgage business, buying smaller commercial enterprises that originated housing loans, which allowed the bank to expand its MBS holdings. The downfall of Lehman and the crash of '08 As the U.S. housing market began to slow down in 2006, the default rate on housing loans began to spike, triggering losses for Lehman from their MBS portfolio. Lehman's main competitor in mortgage financing, Bear Stearns, was bought by J.P. Morgan Chase in order to prevent bankruptcy in March 2008, leading investors and lenders to become increasingly concerned about the bank's financial health. As the bank relied on short-term funding on money markets in order to meet its obligations, the news of its huge losses in the third-quarter of 2008 further prevented it from funding itself on financial markets. By September, it was clear that without external assistance, the bank would fail. As its losses from credit default swaps mounted due to the deepening crash in the housing market, Lehman was forced to declare bankruptcy on September 15, as no buyer could be found to save the bank. The collapse of Lehman triggered panic in global financial markets, forcing the U.S. government to step in and bail-out the insurance giant AIG the next day on September 16. The effects of this financial crisis hit the non-financial economy hard, causing a global recession in 2009.

Collateralized Debt Obligation Market Outlook

The global collateralized debt obligation (CDO) market is projected to experience significant growth over the forecast period from 2024 to 2032. As of 2023, the market size is valued at approximately $75 billion and is expected to reach around $150 billion by 2032, exhibiting a compound annual growth rate (CAGR) of approximately 8%. The expansion of the CDO market is fueled by a combination of financial innovation, increasing demand for diversified investment instruments, and the recovery of global financial markets after previous economic downturns. The growth is further supported by advancements in risk management technologies and a growing appetite for higher-yielding investment opportunities among institutional investors.

A significant growth factor contributing to the expansion of the CDO market is the increasing demand for structured financial products. As investors seek to optimize returns while managing risk, CDOs offer an attractive investment vehicle by pooling various types of debt instruments and redistributing the associated risk. This demand is particularly prominent among institutional investors who are keen to enhance portfolio diversification and mitigate exposure to single asset risks. Moreover, the evolution of financial markets and the introduction of innovative CDO structures have broadened the scope and appeal of these instruments, promoting further market growth.

The resurgence of corporate debt issuance is another pivotal factor driving the CDO market. With companies across the globe seeking to leverage favorable interest rates and conditions to raise capital, there is a significant increase in the availability of corporate debt, which serves as a primary underlying asset for CDOs. This trend is supported by a robust global economic environment and the need for businesses to finance growth, mergers and acquisitions, and other strategic initiatives. As a result, the surge in corporate debt issuance creates a conducive environment for the proliferation of CDOs, which package these debts into marketable securities.

Technological advancements in financial services also play a crucial role in the growth of the CDO market. Enhanced data analytics, machine learning, and artificial intelligence applications have revolutionized the way financial products are structured, marketed, and managed. These technologies facilitate improved risk assessment, credit analysis, and pricing models for CDOs, making them more attractive to investors. Additionally, the increased transparency and efficiency brought about by technology encourage participation from a wider range of market players, further driving the demand and market size of CDOs.

Regionally, the North American market continues to dominate the CDO landscape, driven by a mature financial sector and high levels of institutional investment activity. However, significant growth is anticipated in the Asia Pacific region due to rapid economic development, increasing financial market sophistication, and rising demand for diversified investment instruments. Europe also presents promising opportunities, supported by the stabilization of its financial systems post-crisis and increasing regulatory clarity. Although the Middle East & Africa and Latin America currently represent smaller market shares, these regions are expected to witness gradual growth owing to economic diversification efforts and expanding financial sectors.

Type Analysis

The CDO market can be segmented by type into asset-backed CDOs, synthetic CDOs, and structured finance CDOs, each with distinct characteristics and market dynamics. Asset-backed CDOs, which are backed by a pool of loans, bonds, or other financial assets, have historically been the most prevalent type within the market. These instruments appeal to investors by offering a combination of predictable cash flows and risk diversification. The demand for asset-backed CDOs remains robust due to the continued availability of diverse underlying assets and the appeal of a structured investment that can be tailored to meet specific risk/return profiles.

Synthetic CDOs, which use credit default swaps and other derivatives instead of owning physical assets, have gained traction due to their flexibility and potential for higher yields. These instruments allow investors to take on specific risk exposures without the need to directly hold the underlying assets, making them attractive for sophisticated investors and those seeking to hedge existing portfolio risks. The synthetic CDO market is expected to grow as financial markets evolve and investo

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroscedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroscedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the model identification. Moreover, we introduce a quasi-maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model, and establish its theoretical properties. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

The increase in macroeconomic uncertainty leads to inefficiency in the financial and banking sectors, resulting in a rise in Non-Performing Loans (NPLs). When macroeconomic uncertainty increases, financial institutions experience higher inefficiencies, reflected in increased NPLs, and with proper management solutions, the economy can move toward sustainability. This research analyzes the effect of severe macroeconomic shocks on the NPLs of the Iranian banking system using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) model and a Panel Data Model. The study utilizes data from 2007 to 2021 on key macroeconomic indicators such as economic growth rate, inflation rate, interest rate, unemployment rate, and exchange rate, along with the ratio of Non-Current Claims to Total Facilities as an index of credit risk and the ratio of loans to total assets as a risk-taking index for banks. Our innovation lies in analyzing these variables dynamically, accounting for their correlation and mutual impact. The findings indicate that a 1% increase in inflation leads to a 0.0061% increase in NPLs, while a 1% rise in the unemployment rate results in a 0.0182% increase in NPLs. Conversely, a 1% increase in GDP growth reduces NPLs by 0.0036%. Furthermore, shocks to interest rates, exchange rates, and economic growth increase credit risk, with a 1% interest rate shock raising the default rate from 7.8% to 9.2% over time.

The increase in macroeconomic uncertainty leads to inefficiency in the financial and banking sectors, resulting in a rise in Non-Performing Loans (NPLs). When macroeconomic uncertainty increases, financial institutions experience higher inefficiencies, reflected in increased NPLs, and with proper management solutions, the economy can move toward sustainability. This research analyzes the effect of severe macroeconomic shocks on the NPLs of the Iranian banking system using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) model and a Panel Data Model. The study utilizes data from 2007 to 2021 on key macroeconomic indicators such as economic growth rate, inflation rate, interest rate, unemployment rate, and exchange rate, along with the ratio of Non-Current Claims to Total Facilities as an index of credit risk and the ratio of loans to total assets as a risk-taking index for banks. Our innovation lies in analyzing these variables dynamically, accounting for their correlation and mutual impact. The findings indicate that a 1% increase in inflation leads to a 0.0061% increase in NPLs, while a 1% rise in the unemployment rate results in a 0.0182% increase in NPLs. Conversely, a 1% increase in GDP growth reduces NPLs by 0.0036%. Furthermore, shocks to interest rates, exchange rates, and economic growth increase credit risk, with a 1% interest rate shock raising the default rate from 7.8% to 9.2% over time.