Asia Capital Markets Report 2025

Annex A. Methodology for data collection and classification

Regional classifications

This report uses the following regional definitions.

Asia: Bangladesh, Cambodia, People’s Republic of China, Hong Kong (China), India, Indonesia, Japan, Korea, Lao PDR, Malaysia, Mongolia, Pakistan, Philippines, Singapore, Sri Lanka, Chinese Taipei, Thailand, Viet Nam.

Latin America and the Caribbean (LAC): Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Lucia, Suriname, Trinidad and Tobago, Uruguay, Venezuela.

Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Ireland, Romania, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom.

Chapters 1 and 2 – Capital markets and public equity markets

Financial information of unlisted companies

The OECD-ORBIS Corporate Finance dataset provides financial information on unlisted firms, The dataset uses ORBIS as its main source of information. The database includes financial statement and ownership information listed and unlisted companies between 2005 and 2023.

To construct a panel with unique firm-year observations, several criteria are applied since the raw information contains multiple firm-year observations. The fiscal year is recorded as the previous calendar year of the closing date whenever the closing date of the financial statement is before 30 June. Financial statements covering a 12-month period are prioritised. When there are multiple observations within the same year, accounts with closing dates closer to year-end are preferred to accounts with older closing dates. Information coming from published annual reports is preferred to local registry filings, and local registry filings are preferred to unknown filing types. Accounts using IFRS are preferred to those using GAAP, and accounts using GAAP are preferred to those using unknown accounting practices. For companies with multiple consolidation codes, the following criteria apply: for companies that release consolidated financial statements, C1 is preferred when both C1 and C2 exist; for companies that release unconsolidated statements, observations from annual reports are preferred over others.

Companies with at least one observation showing negative assets or negative fixed assets are dropped from the sample. Companies with 10 or fewer employees are dropped from the sample. When employee numbers are not available, companies with total assets below EUR 35 000 are also dropped from the sample. Financial statement information is winsorised at 1% for both tails. Financial information initially collected in EUR was adjusted by 2024 EUR Consumer Price Index.

In the case that there are single year gaps in the financial data, these are populated by interpolation using the average value of the preceding and the succeeding year for major items. Sub-items are then calculated using accounting identities. Such interpolation is reserved for single year gaps. The OECD ORBIS Corporate Finance uses the 1 digit SIC industry classification.

For the calculations in this report, financially constrained firms are defined as those without any debt for three consecutive years between 2016 and 2018. The chosen period is before COVID-19 to avoid considering temporal effects related to it. Sales growth refers to the year-on-year change implied by the cumulative growth over a three-year period.

Public equity

The information on initial public offering (IPOs) and secondary public offerings (SPOs) presented in this report is based on transaction and/or firm level data gathered from several financial databases, such as LSEG (Screener, Datastream), FactSet and Bloomberg. Considerable resources have been committed to ensuring the consistency and quality of the dataset. Different data sources are checked against each other and, whenever necessary, the information is also controlled against original sources, including regulator, stock exchange and company websites and financial statements.

Country coverage and classification

The dataset includes information about all initial public offerings (IPOs) and secondary public offerings (SPOs or follow-on offerings) by financial and non-financial companies. All public equity listings following an IPO, including the first-time listings on an exchange other than the primary exchange, are classified as an SPO. If a company is listed in more than one exchange within 180 days, those transactions are consolidated under one IPO. The country breakdown is carried out based on the domicile country of the issuer. In the dataset, the country of issue classification is also made based on the stock exchange location of the issuer. It is possible that a company becomes listed in more than one country when going public. The financial databases record a dual listing as multiple transactions for each country where the company is listed. However, there is also a significant number of cases where dual listings are reported as one transaction only based on the primary market of the listing. For this reason, the country breakdown based on the stock exchange is currently carried out based on the primary market of the issuer. The IPO and SPO data are collected on a deal basis via commercial databases in current USD values. Issuance amounts initially collected in USD were adjusted by 2024 USD Consumer Price Index.

Exclusion criteria

With the aim of excluding IPOs and SPOs by trusts, funds and special purpose acquisition companies, the following exclusion criteria are used:

• Financial companies that conduct trust, fiduciary and custody activities

• Asset management companies such as health and welfare funds, pension funds and their third‑party administration, as well as other financial vehicles

• Open-end investment funds

• Other financial vehicles

• Grant making foundations

• Asset management companies that deal with trusts, estates and agency accounts

• Special Purpose Acquisition Companies (SPACs)

• Closed-end investment funds

• Listings on an over-the-counter (OTC) market

• Security types classified as “units” and “trust”

• Real Estate Investment Trusts (REITs)

• Transactions with missing or zero proceeds

Industry classification

LSEG uses the Reference data Business Classification (TRBC) Industry Description. The economic sectors used in the analysis are the following:

Table A A.1. Public offerings industry classification

TRBC Economic Sector
Basic materials Cyclical Consumer Goods & Services Energy Financials Governmental Services Healthcare Industrials Non-Cyclical Consumer Goods & Services Non-Profit/Private Organizations/Services Technology Telecommunications Services Utilities	Industrials
Cyclical consumer goods & services	Non-cyclical consumer goods & services
Energy	Real estate
Financials	Technology
Healthcare	Utilities

Listing information

The information on the number of listed companies and their market capitalisation is based on LSEG Screener and the following criteria are used to clean the data:

• Security type classified as “units” and “trust” are excluded.

• For firms with multiple listings, only primary listings are kept.

• For firms with multiple observations but different countries of domicile, their true country of domicile is manually checked to remove the duplicates.

• Firms trading on over-the-counter (OTC) markets and those listed on multilateral trading facilities (MTFs) or SME/growth markets are excluded. SME/growth markets included in the analysis are: Korea Exchange (KOSDAQ), New York Stock Exchange (NYSE) and Nasdaq Capital Market (NASDAQ).

• Special Purpose Acquisition Companies (SPACs) are excluded.

• Investment funds are excluded.

• Real Estate Investment Trusts (REITs) are excluded.

Growth market classification and relevant information

The figures on equity growth markets are based on OECD calculations using company-level information from LSEG and the websites of stock exchanges. All data on equity growth markets refers to end of 2023. The identification of equity markets for growth companies is based on whether the regulatory authorities or the stock exchange governing the market segment designate it as being for growth companies and/or small and medium-sized enterprises (SMEs). Using this classification of growth markets, companies are categorised as either listed on the main market or growth market. Where available, segment information from LSEG is used to determine the listing segment of each company. However, as LSEG data often only indicate the exchange on which a company is listed without specifying the segment, additional manual verification was conducted.

For the manual verification, information on each listed company was collected directly from stock exchange websites. Company identifiers, such as ISINs, are used to match the LSEG data with stock exchange information. Where company identifiers are missing, name matching is used to accurately assign each company to the correct market segment on the exchange.

Ownership information

The ownership figures for publicly listed companies are based on OECD calculations using firm-level information from the FactSet Ownership database. The data are complemented and verified using LSEG and Bloomberg. Data are collected at the end of 2024 in current USD, thus no inflation adjustment is needed. Market information for each company is collected from LSEG. The dataset includes the records of owners for 46 086 companies listed across 98 countries covering 99% of the world market capitalisation. For each of the countries/regions presented, the information corresponds to all listed companies in those countries/regions with available information.

The records of owners are collected for each company. Some companies have up to 5 000 records in their list of owners. Each record contains the name of the institution, the percentage of outstanding shares owned, the investor type classification, the origin country of the investor, the ultimate parent name, among other things.

The table below presents the five categories of owners defined and used in this report following De La Cruz, Medina and Tang (2019[1]). Different types of investors are grouped into these five categories of owners. In many cases, when the ultimate owner is identified as a government, a province or a city and the direct owner was not identified as such, ownership records are reclassified as public sector. For example, public pension funds that are regulated under public sector law are classified as public sector, and sovereign wealth funds (SWFs) are also included in that same category.

Table A A.2. Categories of owners defined and used in the report

Investor category	Categories of owners
	Investor type
Private corporations and holding companies	Business association	Operating division
	Employee stock ownership plan	Private company
	Holding company	Public company
	Joint venture	Subsidiary
	Non-profit organisation
Public sector	Government	Regional governments
	Sovereign wealth manager	Public pension funds
Strategic individuals and family members	Individual (Strategic owners)	Family office
Institutional investors	Bank investment division	Mutual fund manager
	Broker	Other
	College/University	Pension fund
	Foundation/Endowment manager	Pension fund manager
	Fund of funds manager	Private banking/Wealth management
	Fund of hedge funds manager	Private equity fund/Alternative investments
	Hedge fund	Real estate manager
	Hedge fund manager	Research firm
	Insurance company	Stock borrowing/Lending
	Investment adviser	Trust/Trustee
	Market maker	Umbrella fund
	Mutual fund-closed end	Venture capital/Private equity
Other free‑float including retail investors	Shares in the hands of investors that are not required to disclose their holdings. It includes the direct holdings of retail investors who are not required to disclose their ownership and institutional investors that did not exceed the required thresholds for public disclosure of their holdings.

Private equity

The private equity and venture capital figures are based on OECD calculations using deal and fund-level data from Preqin. The information contains data on investors, funds, fund managers and deals. Data on fundraising and deals only include funds and deals classified by Preqin as private equity and venture capital. Private equity-style deals in infrastructure or real estate are excluded.

Fundraising data is based on funds in one of the following categories: close ended; a first, second, third, fourth, fifth or sixth close; liquidated; evergreen, open ended, semi-open ended. The latest close date is used to classify the fundraising year. Fundraising is classified through the geographic focus of funds. A significant part of this fundraising is done with a regional rather than national focus, and the regional classifications used by Preqin differs from the ones used in the rest of this report.

The regional classifications provided by Preqin have been used. North America includes Bermuda, Canada, the Cayman Islands and the United States. Europe includes all EU countries, Albania, Andorra, Armenia, Azerbaijan, Belarus, Bosnia & Herzegovina, Georgia, Guernsey, Iceland, Jersey, Kosovo*, Liechtenstein, Moldova, Montenegro, Monaco, North Macedonia, Norway, Russia, Switzerland, Turkey, the United Kingdom and Ukraine. Asia includes Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, People's Republic of China, Hong Kong (China), Fiji, India, Indonesia, Japan, Kazakhstan, Korea, Democratic People’s Republic of Korea, Kyrgyzstan, Lao PDR, Malaysia, Maldives, Mongolia, Myanmar, Nepal, North Korea, Pakistan, Philippines, Singapore, Sri Lanka, Chinese Taipei, Tajikistan, Thailand, Timor-Leste, Turkmenistan, Uzbekistan and Viet Nam.

Investment and exit deals data is based on completed deals. The following deal types have been excluded: general partner stakes, private debt, venture debt and joint venture. Investments and exits are reported according to the location of the portfolio company. Private equity statistics initially collected in USD were adjusted by 2024 USD Consumer Price Index.

Investors

The source of the information for the investment funds and insurance companies is presented in the below tables.

Table A A.3. Source information for investment funds

Economies	Source
China	IIFA (2012[2]), Asset Management Association of China, MEMBERS — CHINA - International Investment Funds Association; Caixin Global (2024[3]), Caixin Global, Chart of the Day: Value of China’s Mutual Funds Comes Down From Peak
Hong Kong (China)	SFC (2013[4]), Fund Management Activities Survey 2012, https://www.sfc.hk/-/media/EN/files/ER/PDF/FMAS-2012ReportEngFinal-on-25-Jul-2013.pdf; FSTB (2025[5]), Asset and Wealth Management, https://www.fstb.gov.hk/en/financial_ser/asset-and-wealth-management.htm
India	The Economic Times (2013[6]), Mutual Fund industry AUM rises by Rs 1.5 lakh cr in 2012-13, https://economictimes.indiatimes.com/mf/mf-news/mutual-fund-industry-aum-rises-by-rs-1-5-lakh-cr-in-2012-13/articleshow/19362017.cms; The Economic Times (2024[7]), Navigating investment confusion: How to choose the right asset for your goals, https://economictimes.indiatimes.com/mf/analysis/navigating-investment-confusion-how-to-choose-the-right-asset-for-your-goals/articleshow/107615043.cms
Indonesia	OJK (2014[8]), Statistik Reksa Dana Syariah, https://ojk.go.id/id/kanal/syariah/data-dan-statistik/reksa-dana-syariah/Pages/statistik-reksa-dana-syariah-november-2014.aspx; OJK (2025[9]), Indonesia's Capital Market Remains Resilient in 2024, https://iru.ojk.go.id/iru/news/detailnews/13246/indonesia-s-capital-market-remains-resilient-in-2024-indonesia-stock-exchange-closing-ceremony-2024
Japan	JSDA (2012[10]), Fact Book 2012, https://www.jsda.or.jp/shiryoshitsu/toukei/factbook/files/factbook2012_e_20121227.pdf; JITA (2024[11]), Factbook of Japanese Investment Trusts, https://www.toushin.or.jp/files/statistics/8/FACTBOOK_202412.pdf
Korea	Korean Capital Market Institute (2013[12]), Internationalization of Korea's Asset Management Companies, https://www.kcmi.re.kr/kcmifile/webzine_content/perspective_eng/3011/webzinepdf_3011.PDF; Alpha BIZ (2025[13]), Alpha BIZ, https://alphabiz.co.kr/news/view/1065579873222036
Malaysia	SC Malaysia (2013[14]), A record year for Malaysia’s RM2.5 trillion capital market, https://www.sc.com.my/resources/media/media-release/a-record-year-for-malaysias-rm25-trillion-capital-market; The Edge Malaysia (2025[15]), SC: Investment management AUM exceeded RM1 tril for first time in 2024, https://theedgemalaysia.com/node/748573
Pakistan	The Express Tribune (2015[16]), https://tribune.com.pk/story/842538/asset-management-slow-growth-the-result-of-tax-anomalies; MUFAP (2024[17]), Newsletter December 2024, https://www.mufap.com.pk/Upload/WebDoc/Communication/MUFAP_Newsletter_Dec_20243143.pdf
Philippines	FMAP (2020[18]), Investment Funds in the Philippines, https://www.nomurafoundation.or.jp/wordpress/wp-content/uploads/2020/04/NJACM4-2SP20-05.pdf; OECD (2024[19]), OECD Capital Market Review of the Philippines 2024, https://doi.org/10.1787/80afb228-en
Singapore	AAM (2013[20]), Asia Asset Management, https://www.asiaasset.com/post/4065-news-masres1https://www.fstb.gov.hk/en/financial_ser/asset-and-wealth-management.htm; IFC Review (2024[21]), Singapore as an Asian fund hub: The growing trend of ‘onshoring’, https://www.ifcreview.com/articles/2024/august/singapore-as-an-asian-fund-hub-the-growing-trend-of-onshoring/
Sri Lanka	CBSL (2013[22]), Annual Report 2012, https://www.cbsl.gov.lk/sites/default/files/cbslweb_documents/publications/annual_report/2012/en/12_Chapter_08.pdf CBSL (2025[23]), Annual Economic Review 2024, https://www.cbsl.gov.lk/sites/default/files/cbslweb_documents/publications/aer/2024/en/07_Chapter_02.pdf
Chinese Taipei	Roland Berger Strategy Consultants (2012[24]), Asset Management in Taiwan, https://www.rolandberger.com/publications/publication_pdf/roland_berger_tac_asset_management_taiwan_1.pdf; SITCA (2024[25]), Opportunities and Challenges of Taiwan ETF Market in 2025, https://www.sitca.org.tw/ROC/SITCA_ETF/files/ETF_Market_2025.pdf
Thailand	Bangkok Post, (2012[26]), Thai mutual funds grow 12.3% in first half of 2012, https://www.bangkokpost.com/business/general/301179/thai-mutual-funds-grow-12-3-in-first-half-of-2012; Polkuamdee (2024[27]), Bangkok Post, https://www.bangkokpost.com/business/general/2779835/mutual-fund-assets-up-2-58-to-b5-28tn

Table A A.4. Source information for insurance companies

Economies	Source
China	FRED (2020[28]), Insurance Company Assets to GDP for China, https://fred.stlouisfed.org/series/DDDI11CNA156NWDB
Hong Kong (China)	FRED (2011[29]), Insurance Company Assets to GDP for Hong Kong SAR, https://fred.stlouisfed.org/series/DDDI11HKA156NWDB; Insurance Authority (2023[30]), Annex Key Indicators and Summary Statistics, https://www.ia.org.hk/en/infocenter/press_releases/files/Annex_Key_Indicators_and_Summary_Statistics_2023.pdf
India	FRED (2019[31]), Insurance Company Assets to GDP for India, https://fred.stlouisfed.org/series/DDDI11INA156NWDB
Indonesia	FRED (2020[32]), Insurance Company Assets to GDP for Indonesia, https://fred.stlouisfed.org/series/DDDI11IDA156NWDB
Japan	FRED (2020[33]), Insurance Company Assets to GDP for Japan, https://fred.stlouisfed.org/series/DDDI11JPA156NWDB
Korea	FRED (2020[34]), Insurance Company Assets to GDP for Republic of Korea, https://fred.stlouisfed.org/series/DDDI11KRA156NWDB
Malaysia	FRED (2020[35]), Insurance Company Assets to GDP for Malaysia, https://fred.stlouisfed.org/series/DDDI11MYA156NWDB
Pakistan	FRED (2020[36]), Insurance Company Assets to GDP for Pakistan, https://fred.stlouisfed.org/series/DDDI11PKA156NWDB
Philippines	FRED (2020[37]), Insurance Company Assets to GDP for Philippines, https://fred.stlouisfed.org/series/DDDI11PHA156NWDB
Singapore	FRED (2020[38]), Insurance Company Assets to GDP for Singapore, https://fred.stlouisfed.org/series/DDDI11SGA156NWDB
Sri Lanka	FRED (2020[39]), Insurance Company Assets to GDP for Sri Lanka, https://fred.stlouisfed.org/series/DDDI11LKA156NWDB
Thailand	FRED (2018[40]), Insurance Company Assets to GDP for Thailand, https://fred.stlouisfed.org/series/DDDI11THA156NWDB
Chinese Taipei	The Global Economy (2020[41]), Taiwan: Insurance company assets, https://www.theglobaleconomy.com/Taiwan/insurance_company_assets

Chapter 3 - Corporate debt markets

Corporate bonds

Data presented on corporate bond issues are based on OECD calculations using deal-level data obtained from LSEG on new issues of corporate bonds that are underwritten by an investment bank. The database provides detailed information for each corporate bond issue, including the identity, nationality and sector of the issuer; the type, interest rate structure, maturity date and rating category of the bond; and the amount of proceeds obtained from the issue and intended uses thereof.

Convertible bonds, deals that were registered but not consummated, preferred shares, sukuk bonds, bonds with an original maturity less than or equal to one year or an issue size less than USD 1 million are excluded from the dataset. Industry classifications are based on The Reference data Business Classification (TRBC) from LSEG. Yearly issuance amounts initially collected in USD were adjusted by 2024 USD Consumer Price Index.

Given that a significant portion of bonds are issued internationally, it is not possible to systematically assign issues to a certain country of issue. For this reason, the country breakdown is carried out based on the country of domicile of the issuer. The advanced/emerging market classification is based on IMF country classifications.

Rating data

Rating information is based on OECD calculations using data obtained from LSEG that provides rating information from three leading rating agencies: Fitch, Moody’s and S&P. For each bond that has rating information in the dataset, a value of 1 is assigned to the lowest credit quality rating (C) and 21 to the highest credit quality rating (AAA for Fitch and S&P; and Aaa for Moody’s). There are 11 non‑investment grade categories: five from C (C to CCC+); and six from B (B- to BB+). There are ten investment grade categories: three from B (BBB- to BBB+); and seven from A (A- to AAA).

If ratings from multiple rating agencies are available for a given issue, their average is used. Some issues in the dataset, on the other hand, do not have rating information. For such issues, the average rating of all bonds issued by the same issuer in the same year (t) is assigned. If the issuer has no rated bonds in year t, year t-1 and year t-2 are also considered, respectively. This procedure increases the number of rated bonds in the dataset and hence improves how representative the rating-based analysis is. When differentiating between investment and non-investment grade bonds, the final rating is rounded to the closest integer and issuances with a rounded rating less than or equal to 11 are classified as non‑investment grade.

Early redemption data

When calculating the outstanding amount of corporate bonds in a given year, issues that are no longer outstanding due to having been redeemed before their maturity date are deducted. The early redemption data are obtained from LSEG and cover bonds that have been redeemed early due to being repaid via final default distribution, called, liquidated, put or repurchased. The early redemption data are merged with the primary corporate bond market data via International Securities Identification Numbers (ISINs).

Syndicated loans

The syndicated loan figures are based on OECD calculations using deal-level data from LSEG. This database provides detailed information on each loan, including the borrower’s identity, nationality and sector, as well as the interest rate structure, maturity date and loan amount. The loan credit rating category is defined based on the following criteria:

• Investment grade: Initial pricing up to 299 basis points above the base rate.

• Leveraged: Initial pricing between 300 and 399 basis points above the base rate.

• Highly leveraged: Initial pricing 400 basis points or more above the base rate.

Only loans classified as "syndicated" or "club syndicate" are included in the analysis. Deals with maturities of less than 90 days are excluded. Annual data are based on the closing date, which is when the syndication on all levels/tiers has been signed and completed. Industry-level analyses follow LSEG’s Reference data Business Classification (TRBC), while country breakdowns are based on the borrower’s domicile. To account for inflation, issuance amounts initially collected in USD were adjusted by 2024 USD Consumer Price Index.

Private credit

The private credit figures are based on OECD calculations using deal and fund-level data from Preqin. This database contains detailed information on private credit deals including the year, the lenders’ identity, the volume and the use of proceeds of the financing. It also includes information on private credit funds, including the year of establishment and size.

Assets under management (AUM) are calculated at the end of a calendar year, with the exception of the datapoint for 2024 which is calculated at the end of June. The figures only consider closed-end, unlisted private debt funds, which includes unlisted Business Development Companies (BDCs). The figures include both debt provided directly to a company without an acquirer involved (i.e. non-sponsored deals) and debt provided by an investment company in support of an investment or acquisition of a private equity sponsor (i.e. sponsored deals). Collateralised debt obligations (CDOs), infrastructure and real estate debt transactions are excluded. The calculation of AUM is based on Preqin’s internal methodology, and the regional AUM breakdown follows Preqin’s internal taxonomy. The regional breakdown of investment data is done by the OECD based on the borrowers’ country of headquarter.

For the regional breakdown of AUM, Asia includes Afghanistan, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Hong Kong (China), Democratic People’s Republic of Korea, Fiji, India, Indonesia, Japan, Kazakhstan, Korea, Kyrgyzstan, Lao People’s Democratic Republic, Macau (China), Malaysia, the Maldives, Mongolia, Myanmar, Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Chinese Taipei, Tajikistan, Thailand, Timor-Leste, Turkmenistan, Uzbekistan and Viet Nam.

Chapter 4 - Sustainable bonds

OECD Corporate Sustainability dataset

Sustainable bonds are mainly collected from LSEG. This dataset contains deal-level information of nearly 19 000 bonds issued by both the corporate and official sectors from 109 jurisdictions since 2015. This dataset provides a detailed set of information for each sustainable bond issue, including the identity, nationality, and industry of the issuer; the type, interest rate structure, maturity date and rating category of the bond, the amount of and “use of proceeds” obtained from the issue. The issuance amounts initially collected in USD were adjusted by 2024 USD Consumer Price Index.

For sustainable bonds, values for corporations, agencies, local governments, and multilateral institutions correspond to the “gross proceeds” (i.e. the amount paid by investors to acquire the bonds) in most cases.

Agencies and local governments include national government agencies (e.g. KFW, Transport for London), local governments (e.g. Prefecture of Shiga, Province of Ontario), and national development banks (e.g. China Development Bank, Brazilian Development Bank). This category does not contain US municipals that are not readily available in LSEG. The category Central governments includes governments, treasuries and central banks. The category Multilateral institutions includes organisations formed by three or more jurisdictions (e.g. Asian Development Bank, International Finance Corporation) and the European Union.

LSEG data contains both Regulation S and Rule 144A sustainable bonds. Rule 144A presents a safe harbour from the registration requirements of the Securities Act for resales of securities not fungible with securities listed on a US securities exchange to qualified institutional buyers. Regulation S provides a safe harbour from the registration requirements of the Securities Act for offerings made outside the United States (Bruckhaus, 2017[42]). The calculations presented take account of this factor, and an exercise to eliminate the duplication when a single bond was issued both under Regulation S and Rule 144A was performed.

When calculating the outstanding amount of bonds in a given year, issues that are no longer outstanding due to being redeemed earlier than their maturity are deducted. Outstanding values refer to the “principal amount” or otherwise to the “original amount issued” (i.e. the face value of the bonds in their legal documentation) when the “principal amount” could not be retrieved. The early redemption data are obtained from LSEG and cover bonds that have been redeemed early due to being repaid via final default distribution, called, liquidated, put or repurchased. The early redemption data are merged with the primary bond market data via international securities identification numbers (i.e. ISINs).

The greenium analysis

The greenium analysis used two databases. The first one includes 234 820 corporate bonds from the OECD Capital Market Series dataset and the other one 274 269 bonds issued by official sector entities. Both datasets consider only issuers that have issued at least one sustainable bond and one conventional bond to ensure a balanced representation, to control for credit and exchange rate risk. To ensure data integrity and robustness in subsequent regression analysis stages, the focus is directed towards bonds with comprehensive data across specific variables. These variables are: issuer, domicile, currency, seniority, issue date, maturity date, coupon, coupon type, yield‑to‑maturity and the amount issued in USD.

The Nearest Neighbour Matching technique was used for the matching process. A propensity score model was estimated using the selected variables to estimate the probability of each bond being green. The matching was performed in a 1:1 ratio, meaning each green bond was matched to one conventional bond. Propensity scores range between 0 and 1. In the context of propensity score matching, a propensity score is the probability of a unit (a bond, in this case) being assigned to a particular treatment given a set of observed characteristics. In other words, it is the conditional probability of receiving treatment (being a sustainable bond) as a function of observed characteristics. A score closer to 1 indicates a higher likelihood of being a sustainable bond, given the observed features, while a score closer to 0 suggests a lower probability.

Using the Nearest Neighbour Matching, bonds were matched based on:

• nearest values in terms of issue date, maturity date and amount issued

• exact matching on issuer, domicile, currency, coupon type and seniority.

The datasets obtained include 1 686 official sector bonds (843 sustainable and 843 conventional) and 7 566 corporate bonds (3 783 sustainable and 3 783 conventional). Further criteria were applied to minimise potential bias when observing the yield differential between sustainable and conventional bonds. By controlling for issue and maturity dates, pairs of bonds were identified where neither the issue nor the maturity date differed by more than two years compared to the paired bond. Final datasets comprised 1477 pairs of corporate bonds and 192 pairs of official sector bonds.

Then, a regression analysis was carried out to test the independence of the “sustainable effect”. Alongside the yield to maturity, seniority, currency, coupon type, issuer and domicile were added as covariates. This was done to eliminate residual differences (i.e. to correct minor discrepancies that may have remained between groups after matching), improve model precision by reducing residual variance and obtaining more stable estimates to assess the robustness of the sustainable effect, i.e. whether it persists even when considering other factors.

Chapter 5 - AI innovation facilitators

List of innovation facilitators across Asian jurisdictions

Table A A.5. further information on the financial sector-focused innovation facilitators that explicitly focus on AI (HKMA GenAI Sandbox), that include AI among the technologies within their scope (SFC Fin Tech Contact Point, RBI Regulatory Sandbox), that have reported past AI use cases (MAS Regulatory Sandbox; JFSA FinTech Proof-of-Concept Hub, OJK Regulatory Sandbox, Korean FinTech Regulatory Sandbox, Seoul FinTech Lab and SECP Regulatory Sandbox) or that have other AI aspects (HKMA FinTech Facilitation Office and RBI Innovation Hub). Table 5.1 also provides brief descriptions of each facilitator.

Table A A.5. Innovation facilitators focused on or featuring AI in finance in Asia

Name	Type	Jurisdiction	Sector	Year established	Ownership and oversight
MAS Regulatory Sandbox	Sandbox	Singapore		2016, 2019 (update), 2022 (update)	Monetary Authority of Singapore (MAS)
SFC Fintech Contact Point	Innovation Hub	Hong Kong (China)	FinTech	2016	Securities and Futures Commission (SFC)
Fintech Facilitation Office (FFO)	Innovation Hub	Hong Kong (China)	FinTech	2016	Hong Kong Monetary Authority (HKMA)
FinTech Proof-of-Concept Hub	Innovation Hub and sandbox	Japan		2017	Japan Financial Services Agency (JFSA)
OJK Regulatory Sandbox	Sandbox	Indonesia		2018	Indonesia Financial Services Authority (OJK)
RBI Regulatory Sandbox	Sandbox	India		2019, 2020 (update), 2021 (update)	Reserve Bank of India (RBI)
Fintech Regulatory Sandbox	Sandbox	Korea		2019	Financial Services Commission (FSC)
Seoul FinTech Lab	Innovation Hub	Korea		2019	Seoul Metropolitan Government
SECP Regulatory Sandbox	Sandbox	Pakistan		2019	Securities and Exchange Commission of Pakistan (SECP)
RBI Innovation Hub	Innovation Hub	India		2020	Reserve Bank India (RBI)
BSP Regulatory Sandbox	Sandbox	Philippines		2022	Bangko Sentral ng Pilipinas (BSP)
GenAI Sandbox	Sandbox	Hong Kong (China)	Banking	2024	HKMA Hong Kong Cyberport Management Company (HKCMC)

Source: publicly available information.

Table A A.6. lists six innovation facilitators focused on AI innovation beyond the financial sector, including brief descriptions of each. This includes general cross-sectoral facilitators (Malaysia’s AI Sandbox Pilot Program and AI Hub Korea), that focus on specific forms of AI (Singapore’s GenAI Sandboxes), and that focus on specific applications of AI (Singapore’s PET Sandbox).

Table A A.7. lists a select group of FinTech innovation facilitators that do not explicitly feature AI technologies but enable such technologies to be tested. As not all facilitators provide public information on their objectives, scope, participants and outcomes, it could be expected that AI use cases may have already been tested or may be included in the priorities of some facilitators in the future. Fifteen FinTech sandboxes were identified across the region, mostly being cross-sectoral but with a few sector-specific examples. A further four innovation hubs and one hybrid innovation facilitators were identified. Several other Asian economies have announced the introduction of sandboxes, but these have been omitted due to a lack of information on their status.

Table A A.6. Innovation facilitators focused on AI beyond the financial sector in Asia

Name	Type	Jurisdiction	Sectoral focus	Year established	Ownership and oversight	Brief description
AI Hub Korea	Innovation hub	Korea		2017 (earliest release of AI training data)	Ministry of Science and ICT; National Information Society Agency	Provides a range of services to support the integration of AI solutions in businesses. Services include: Synthetic datasets and API integration for AI testing (including synthetic financial data). “Safe zones” for training AI models using local datasets. Training courses on AI topics. Access to high-performance computing resources. The AI Hub has regularly released data for training AI models since 2017.
PET Sandbox	Sandbox	Singapore	Privacy Enhancing Techno-logies	2022	InfoComm Media Development Authority (IMDA); Data Protection Commission (PDPC)	Provides opportunities for companies to work with trusted PET digital solution providers to develop use cases and pilot PETs. Services include: matchmaking with PET providers; granting support for pilot projects; and regulatory support. The sandbox has been expanded to encourage projects exploring the use of PETs for data generation to support GenAI use cases. Multiple AI-related case studies have also been reported.
GenAI Evaluation Sandbox	Sandbox	Singapore	General	2023	IMDA; AI Verify Foundation	Brings global ecosystem players together through concrete use cases, to evaluate trusted AI products. Provides a baseline by offering a research-based categorisation of current evaluation benchmarks and methods.
GenAI Sandbox for SMEs	Sandbox	Singapore	SMEs	2024	Enterprise Singapore; IMDA	13 GenAI solutions are being developed, and selected SMEs will receive grant support to trial one solution of their choice for three months. EnterpriseSG and IMDA will review SME’s feedback on the solutions and consider how to scale them. Available to all local SMEs.
GenAI Sandbox 2.0	Sandbox	Singapore	SMEs	2024	IMDA	Following the success of the first GenAI sandbox (above), the second sandbox provides 15 new GenAI solutions. Eligible local SMEs will receive a grant to trial a solution of their choice for three months, with up to 50% of the cost covered.
AI Sandbox Pilot Program	Sandbox	Malaysia	General	2024	Malaysian Research Accelerator for Technology & Innovation (MRANTI); NVIDIA Corp	The AI Sandbox Pilot is part of Malaysia’s National Technology and Innovation Sandbox. It is open to startups, researchers and academics seeking to test and implement AI. Provides AI training, access to computing resources, technical development support and networking opportunities.

Source: publicly available information.

Table A A.7. Other FinTech innovation facilitators in Asia

Name	Type	Jurisdiction	Sectoral focus	Year established	Ownership and oversight	Brief description
Fintech Super-visory Sandbox	Sandbox	Hong Kong (China)	Banking	2016 (1.0) 2017 (2.0) 2021 (3.0)	Hong Kong Monetary Authority (HKMA)	Allows banks and partnering technology firms to pilot FinTech initiatives with a limited number of participating customers without the need for compliance with all supervisory requirements. Sandbox 3.0 enables tech firms to apply for funding support.
Regulatory Sandbox	Sandbox	Malaysia		2016 2024 (Green Lane)	Bank Negara Malaysia (BNM)	Includes two tracks: a Standard Sandbox introduced in 2016, and accelerated track introduced in 2024 for financial institutions with a strong track record in risk management.
FinTech Space	Hybrid	Chinese Taipei		2017	Financial Supervisory Commission (FSC) Taiwan Financial Services Roundtable	Offers several services for startups, including: counselling, mentoring and other services to support regulatory compliance; an on-site “digital Sandbox” with financial data for startups to trial solutions; and support for expanding to other countries, including an international startup exchange programme
FinTech Sandbox	Sandbox	Chinese Taipei		2018	FSC	Limited information is available on the parameters of the sandbox. However the FSC is responsible and has reported on approved experiments, for example on Robo Advisors.
Mumbai FinTech Hub	Innovation Hub	India		2018	Government of Maharashtra State	Past initiatives under the FinTech Hub include a Fintech registry, an API sandbox, a government grant/reimbursement scheme, a Global Market Access program, and a Fintech Investment & Deals platform. Limited information on the status of the Hub.
JFSA Regulatory Sandbox	Sandbox	Japan		2018	Japan Financial Services Authority (JFSA)	Regulatory sandbox for trialing new technologies, with the possibility to amend regulations based on the sandbox experiences.
PBOC Fintech Innovation Regulatory Facility	Sandbox	China		2019	People’s Bank of China (PBOC)	The PBOC has implemented a framework for promoting innovation through regulatory flexibility. This has been built up over time on a city-by-city basis. The first Pilot was launched in Beijing in 2019. As of August 2024, the Pilot has been expanded to 9 cities. Limited information is available regarding the features of these Pilot programmes. In 2023 the PBOC, HKMA and Monetary Authority of Macau signed a MoU to integrate the sandboxes in the respective jurisdictions.
CBSL Regulatory Sandbox	Sandbox	Sri Lanka		2020	Central Bank of Sri Lanka	Limited information available regarding the status of the sandbox and participating entities.
Insurance Regulatory Sandbox	Sandbox	Thailand	Insurance	2021	Office of Insurance Commission (OIC)	Sandbox for innovations in InsurTech. Includes both a regulatory sandbox and an “own sandbox”.
FinTech Regulatory Sandbox	Sandbox	Cambodia		2022	Non-bank Financial Services Authority (FSA)	Three regulators under FSA’s authority participate in the sandbox, covering the Insurance companies, Trusts, and Securities & Exchange sectors.
SEBI Sandbox	Sandbox	India		2022	SEBI	Provides access to testing facilities, data, and regulatory flexibility to trial FinTech solutions in a live environment, on limited set of real users and for a limited time frame.
Inter-operable Regulatory Sandbox (IORS)	Sandbox	India		2022	RBI, SEBI, IRDA, International Financial Services Centres Authority, Pension Fund Regulatory and Development Authority	The IoRS aims to facilitate testing of “hybrid” products/services falling under the regulatory ambit of more than one regulator. The RBI receives all applications, then entities are allocated to the sandbox of the regulator responsible for the “dominant feature” of the innovative product. Other relevant regulators provide inputs when requested. The Standard Operating Procedures determine requirements such as what is considered the “dominant feature”.
BOT Regulatory Sandbox	Sandbox	Thailand		2024	Bank of Thailand (BOT)	Includes three sandbox models: a “Regulatory Sandbox” for innovations in specific financial services or that could become structural or systemic; an “Own Sandbox” for innovations that do not require the same level of regulatory scrutiny; and an “Enhanced Regulatory Sandbox” that applies to new financial products or services not yet authorised by BOT.
Regulatory Sandbox	Sandbox	Malaysia	Capital Markets	2025	Securities Commission of Malaysia	Sandbox for innovative capital market products and services. Provides a controlled environment to test proposed innovations with regular reporting. No regulatory requirements are relaxed during the testing phase.

Source: publicly available information.

Key OECD Standards relating to innovation facilitators

The following OECD Standards related to the use of innovation facilitators to promote safe experimentation in artificial intelligence are referenced in Chapter 5:

Recommendation of the Council on Artificial Intelligence (OECD AI Principles)

The OECD AI Principles, updated in 2024, (OECD, 2019[43]) are the first intergovernmental standard on AI, aiming to promote innovative, trustworthy AI that respects human rights and democratic values. They are composed of five values-based principles and five recommendations that provide practical and flexible guidance for policymakers and AI actors. Among these, the following recommendations are relevant to innovation facilitators:

V.2.3. Shaping an enabling interoperable governance and policy environment for AI:

a) Governments should promote an agile policy environment that supports transitioning from the research and development stage to the deployment and operation stage for trustworthy AI systems. To this effect, they should consider using experimentation to provide a controlled environment in which AI systems can be tested, and scaled-up, as appropriate. They should also adopt outcome-based approaches that provide flexibility in achieving governance objectives and co-operate within and across jurisdictions to promote interoperable governance and policy environments, as appropriate.

b) Governments should review and adapt, as appropriate, their policy and regulatory frameworks and assessment mechanisms as they apply to AI systems to encourage innovation and competition for trustworthy AI.

OECD Policy Framework for Effective and Efficient Financial Regulation

The following principles contained in the OECD Policy Framework for Effective and Efficient Financial Regulation (OECD, 2009[44]) demonstrate that innovation facilitators can play an important role in developing well-functioning financial systems:

• I. C. a) The functioning of the financial system and its broader macro-economic and global linkages should be well understood, as should the products, services, institutions, systems, and markets connected with the financial system.

• II. A. b) The case for government intervention in the financial system should be established, including an assessment of potential benefits versus costs, the b, and possible alternatives to intervention, and the targets of intervention and related expected outcomes should be clearly identified.

• II. B1. b) Policy objectives should be based on sound policy analysis and correspond to the beneficial outcomes to be expected from addressing identified market failures and other identified broader economic or social needs.

• II. B1. e) Any trade-offs or mutual reinforcement among policy objectives should be carefully analysed.

• II. B2. a) More specific – and potentially different – policy objectives for particular sectors, institutions, and products may need to be elaborated given differences in market conditions, practices, and participants in different parts of the financial system

• II. C. b) Mechanisms to promote accountability should be in place, such as indicators measuring progress toward the achievement of policy objectives.

• III. A. b) The features and possible impacts of each policy instrument should be identified and understood, particularly in terms of costs, incentives, and international spillover effects.

Recommendation of the Council for Agile Regulatory Governance to Harness Innovation

This OECD Recommendation (OECD, 2021[45]) aims to help governments develop and implement agile and resilient regulatory approaches and facilitate institutional co-operation in response to and to further stimulate innovation. The following recommendations are relevant to innovation facilitators and the sharing of experiences between regulatory authorities within and across jurisdictions:

• III. RECOMMENDS that Adherents lay institutional foundations to enable co-operation and joined-up approaches within and across jurisdictions by:

  • 1. Strengthening co-operation across policy-making departments and regulatory agencies as well as between national and sub-national levels of government.

  • 2. Stepping up bilateral, regional, and multilateral regulatory co-operation to address the transboundary policy implications of innovation.

• IV. RECOMMENDS that Adherents develop governance frameworks to enable the development of agile and future-proof regulation by:

  • 4. Enabling greater experimentation, testing, and trialling to stimulate innovation under regulatory supervision.

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