International Indicators: Malaysia 5-Year Credit Default Swap (CDS) data was reported at 46.575 Basis Point in Feb 2025. This records a decrease from the previous number of 46.706 Basis Point for Jan 2025. International Indicators: Malaysia 5-Year Credit Default Swap (CDS) data is updated monthly, averaging 72.975 Basis Point from Jan 2012 (Median) to Feb 2025, with 156 observations. The data reached an all-time high of 238.823 Basis Point in Sep 2015 and a record low of 34.758 Basis Point in Dec 2019. International Indicators: Malaysia 5-Year Credit Default Swap (CDS) data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI002: Financial System Statistics: Macroeconomic Indicator.

Domestic Indicators: Indonesia 5-Year Credit Default Swap (CDS) data was reported at 95.436 Basis Point in Mar 2025. This records an increase from the previous number of 78.833 Basis Point for Feb 2025. Domestic Indicators: Indonesia 5-Year Credit Default Swap (CDS) data is updated monthly, averaging 103.532 Basis Point from Jan 2014 (Median) to Mar 2025, with 135 observations. The data reached an all-time high of 276.303 Basis Point in Sep 2015 and a record low of 65.982 Basis Point in Jan 2020. Domestic Indicators: Indonesia 5-Year Credit Default Swap (CDS) data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI002: Financial System Statistics: Macroeconomic Indicator.

We use a factor model and elastic net shrinkage to model a high-dimensional network of European credit default swap (CDS) spreads. Our empirical approach allows us to assess the joint transmission of bank and sovereign risk to the nonfinancial corporate sector. Our findings identify a sectoral clustering in the CDS network, where financial institutions are in the center and nonfinancial entities as well as sovereigns are grouped around the financial center. The network has a geographical component reflected in different patterns of real-sector risk transmission across countries. Our framework also provides dynamic estimates of risk transmission, a useful tool for systemic risk monitoring.

Dataset used for studying mutual funds' tail-risking behaviors during the recent financial crisis

Global credit derivatives (net - net), for credit default swaps, total (all currencies), total (all currencies), over 1 year and up to 5 years, other residual financial institutions, All countries (total), All countries (total), total (all ratings), total (all sectors), total (all methods), outstanding - notional amounts

Domestic Indicators: External Debt to Export data was reported at 118.883 % in Mar 2023. This records an increase from the previous number of 118.817 % for Dec 2022. Domestic Indicators: External Debt to Export data is updated monthly, averaging 168.391 % from Mar 2014 (Median) to Mar 2023, with 37 observations. The data reached an all-time high of 214.624 % in Dec 2020 and a record low of 118.817 % in Dec 2022. Domestic Indicators: External Debt to Export data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI002: Financial System Statistics: Macroeconomic Indicator.

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

Indonesia International Indicators: Japan 5-Year Credit Default Swap (CDS) data was reported at 15.773 Basis Point in Feb 2025. This records a decrease from the previous number of 20.127 Basis Point for Jan 2025. Indonesia International Indicators: Japan 5-Year Credit Default Swap (CDS) data is updated monthly, averaging 25.434 Basis Point from Jan 2012 (Median) to Feb 2025, with 158 observations. The data reached an all-time high of 158.082 Basis Point in Aug 2013 and a record low of 15.773 Basis Point in Feb 2025. Indonesia International Indicators: Japan 5-Year Credit Default Swap (CDS) data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI002: Financial System Statistics: Macroeconomic Indicator.

International Indicators: World Economic Growth data was reported at 3.300 % in Jan 2025. This records an increase from the previous number of 3.200 % for Dec 2024. International Indicators: World Economic Growth data is updated monthly, averaging 3.200 % from Dec 2012 (Median) to Jan 2025, with 25 observations. The data reached an all-time high of 6.200 % in Dec 2021 and a record low of -3.100 % in Dec 2020. International Indicators: World Economic Growth data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI002: Financial System Statistics: Macroeconomic Indicator.

Conventional Credit Derivatives: Credit Default Swap data was reported at 62.850 MYR mn in Mar 2020. This records a decrease from the previous number of 122.400 MYR mn for Feb 2020. Conventional Credit Derivatives: Credit Default Swap data is updated monthly, averaging 38.550 MYR mn from Jan 2012 (Median) to Mar 2020, with 99 observations. The data reached an all-time high of 751.050 MYR mn in Jun 2019 and a record low of 0.000 MYR mn in Dec 2019. Conventional Credit Derivatives: Credit Default Swap data remains active status in CEIC and is reported by Bank Negara Malaysia. The data is categorized under Global Database’s Malaysia – Table MY.Z033: Derivatives Turnover.

Identifying contagion effects during periods of financial crisis is known to be complicated by the changing volatility of asset returns during periods of stress. To untangle this we propose a GARCH (generalized autoregressive conditional heteroskedasticity) common features approach, where systemic risk emerges from a common factor source (or indeed multiple factor sources) with contagion evident through possible changes in the factor loadings relating to the common factor(s). Within a portfolio mimicking factor framework this can be identified using moment conditions. We use this framework to identify contagion in three illustrations involving both single and multiple factor specifications: to the Asian currency markets in 1997-1998, to US sectoral equity indices in 2007-2009 and to the CDS (credit default swap) market during the European sovereign debt crisis of 2010-2013. The results reveal the extent to which contagion effects may be masked by not accounting for the sources of changed volatility apparent in simple measures such as correlation.

This paper considers estimating the slope parameters and forecasting in potentially heterogeneous panel data regressions with a long time dimension. We propose a novel optimal pooling averaging estimator that makes an explicit trade-off between efficiency gains from pooling and bias due to heterogeneity. By theoretically and numerically comparing various estimators, we find that a uniformly best estimator does not exist and that our new estimator is superior in nonextreme cases and robust in extreme cases. Our results provide practical guidance for the best estimator and forecast depending on features of data and models. We apply our method to examine the determinants of sovereign credit default swap spreads and forecast future spreads.

Three datasets are chosen from the UCI machine learning repository in this study, which have been extensively adopted in data-driven researches, including Australian and Japanese datasets (Asuncion & Newman, 2007), and Polish bankruptcy dataset (Zięba et al., 2016). The three datasets contain different numbers of samples and features. Each sample in a credit dataset can be classified into good credit or bad credit. The size of Australian credit dataset is 690, with 307 samples in good credit and 383 in bad, and its feature dimension is 14, with 6 numerical and 8 categorical features. The size of Japanese credit dataset is 690, with 307 samples in good credit and 383 in bad, and its feature dimension is 15, with 6 numerical and 9 categorical features. Similarly, there are 7027 samples in Polish bankruptcy dataset, with 6756 samples in good credit and 271 in bad, and its 64 input features are numerical. All the dimensions of the input features of the three datasets listed in Table 1 do not include the class labels.

We introduce longitudinal factor analysis (LFA) to extract the common risk-free (CRF) rate from a sample of sovereign bonds of countries in a monetary union. Since LFA exploits the typically very large longitudinal dimension of bond data, it performs better than traditional factor analysis methods that rely on the much smaller cross-sectional dimension. European sovereign bond yields for the period 2006-2011 are decomposed into a CRF rate, a default risk premium and a liquidity risk premium. Our empirical findings suggest that investors chase both credit quality and liquidity, and that they price double default risk on credit default swaps.

In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APRA ceased the RFC data collection after September 2010.

The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars (USD).

Data are recorded on an end-quarter basis.

All banks operating in Australia complete ARF 231. Between March 2003 and September 2010, only those larger RFCs with sizeable overseas assets and/or liabilities completed RRF 231. Bank and RFC positions are reported in Australian dollars (AUD). Non-AUD denominated positions have been converted to AUD using an appropriate end-quarter exchange rate, so changes in reported data between quarters are due not only to changes in positions but also valuation gains or losses due to exchange rate changes.

There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only consolidated data are reported in this statistical table.

The data in this statistical table summarise the country exposures of Australian-owned banks (and selected RFCs between March 2003 and September 2010). This is a smaller reporting pool than in the series reported in statistical table B11.2, which is based on all banks and RFCs reporting ARF/RRF 231 data. The types of assets included here are consistent with those reported in statistical tables B11.1, B11.2 and B12.1, except that the data are consolidated for Australian-owned reporting entities (i.e. includes the claims on countries of all the offices worldwide of entities with head offices in Australia, but excludes positions between different offices of the same group). Consolidated data only include positions with non-residents (in any currency).

Data are shown for a selected group of countries that account for the bulk of the total. Similar data for other countries are also available in statistical table B13.2.1.

Data presented in this statistical table are ultimate risk claims. Ultimate risk claims cover claims on an immediate counterparty location basis that have been adjusted (via guarantees and other risk transfers) to reflect the location of the ultimate counterparty/risk. Data on immediate risk claims (expressed by the BIS as claims on an immediate borrower basis) are available in complementary statistical tables B13.1 and B13.1.2.

Foreign claims refers to all cross-border claims plus foreign offices’ local claims on residents in both local and foreign currencies. It is equal to the addition of local currency claims of reporting entities’ foreign offices on local residents, and international claims. Data for all these accounts on an immediate risk basis are available in a complementary statistical table B13.1.

International organisations are included in the ‘Public sector’ category in the consolidated data (while in the locational data they can be reported as either bank or non-bank depending on the particular organisation). Official monetary authorities (central banks or similar national and international bodies, such as the BIS) are also included in the public sector in the consolidated data (but are treated as banks in the locational data, B12.1 and B12.2). Publicly-owned entities (other than banks) are classed in the ‘Non-bank private sector’ in the consolidated data (and as non-banks in the locational data).

‘Cross border’ positions are those positions with bank and non-bank counterparties located in a country other than the country of residence of the reporting entity (or its affiliate). This would include, for example, lending by a bank in Australia to a company in France; it would also include loans by that bank’s subsidiary in the UK to a company in France.

‘Local’ claims are those claims of overseas affiliates of the reporting entity on the residents of the countries in which they are located. These are largely in local currencies but include non-local currencies as well.

Derivatives are not included in foreign claims. On- and off-balance sheet derivatives are shown separately as a memo item. ‘Derivatives’ are those on- and off-balance sheet derivative exposures (to the country of ultimate risk) that are in a positive market value position. Negative market values of derivative contracts represent financial liabilities and are therefore excluded from the reporting of financial claims. The data mainly comprise forwards, swaps and options relating to foreign exchange, interest rate, equity, commodity and credit derivative contracts. Credit derivatives, such as credit default swaps and total return swaps, are included in ‘Derivatives’ if they belong to the trading book of a protection-buying reporting entity. Credit derivatives that belong to the banking book are reported as risk transfers by the protection buyer. All credit derivatives are reported as guarantees by the protection seller.

‘Guarantees’ refers to contingent liabilities arising from an irrevocable obligation to pay to a third-party beneficiary when a client fails to perform some contractual obligations. They include: secured, bid and performance bonds; warranties and indemnities; confirmed documentary credits; irrevocable and stand-by letters of credit; acceptances; and endorsements. Guarantees also include the contingent liabilities of the protection seller of credit derivative contracts.

‘Credit commitments’ covers arrangements that irrevocably obligate an institution, at a client’s request, to extend credit in the form of: loans; participation in loans, lease financing receivables, mortgages, overdrafts or other loan substitutes; or commitments to extend credit in the form of the purchase of loans, securities, or other assets (e.g. back-up facilities including those under note issuance and revolving underwriting facilities).

Graph and download economic data for ICE BofA BB US High Yield Index Option-Adjusted Spread (BAMLH0A1HYBB) from 1996-12-31 to 2025-07-10 about BB, option-adjusted spread, yield, interest rate, interest, rate, and USA.