66 datasets found
  1. F

    Real Risk Premium

    • fred.stlouisfed.org
    json
    Updated Jun 11, 2025
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    (2025). Real Risk Premium [Dataset]. https://fred.stlouisfed.org/series/TENEXPCHAREARISPRE
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    jsonAvailable download formats
    Dataset updated
    Jun 11, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Real Risk Premium (TENEXPCHAREARISPRE) from Jan 1982 to Jun 2025 about premium, real, and USA.

  2. f

    Data from: Implied Volatility Spreads and Expected Market Returns

    • tandf.figshare.com
    • figshare.com
    text/x-tex
    Updated May 31, 2023
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    Yigit Atilgan; Turan G. Bali; K. Ozgur Demirtas (2023). Implied Volatility Spreads and Expected Market Returns [Dataset]. http://doi.org/10.6084/m9.figshare.1054781.v2
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    text/x-texAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Yigit Atilgan; Turan G. Bali; K. Ozgur Demirtas
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This article investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets. The documented relation is significantly stronger for the periods during which (i) S&P 500 constituent firms announce their earnings; (ii) cash flow and discount rate news are large in magnitude; and (iii) consumer sentiment index takes extreme values. The intertemporal relation remains strongly negative after controlling for conditional volatility, variance risk premium, and macroeconomic variables. Moreover, a trading strategy based on the intertemporal relation with volatility spreads has higher portfolio returns compared to a passive strategy of investing in the S&P 500 index, after transaction costs are taken into account.

  3. A

    Armenia AM: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate

    • ceicdata.com
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    CEICdata.com, Armenia AM: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate [Dataset]. https://www.ceicdata.com/en/armenia/interest-rates/am-risk-premium-on-lending-lending-rate-minus-treasury-bill-rate
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    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2012 - Dec 1, 2023
    Area covered
    Armenia
    Variables measured
    Money Market Rate
    Description

    Armenia AM: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 1.253 % pa in 2023. This records an increase from the previous number of 1.131 % pa for 2022. Armenia AM: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 7.526 % pa from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 13.934 % pa in 2005 and a record low of 1.131 % pa in 2022. Armenia AM: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.;International Monetary Fund, International Financial Statistics database.;;

  4. f

    Datasets for the Role of Financial Investors in Commodity Futures Risk...

    • figshare.com
    application/x-rar
    Updated Dec 6, 2019
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    Mohammad Isleimeyyeh (2019). Datasets for the Role of Financial Investors in Commodity Futures Risk Premium [Dataset]. http://doi.org/10.6084/m9.figshare.9334793.v2
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    application/x-rarAvailable download formats
    Dataset updated
    Dec 6, 2019
    Dataset provided by
    figshare
    Authors
    Mohammad Isleimeyyeh
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

  5. S

    Sri Lanka LK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate...

    • ceicdata.com
    Updated Jun 15, 2018
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    CEICdata.com (2018). Sri Lanka LK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate [Dataset]. https://www.ceicdata.com/en/sri-lanka/interest-rates/lk-risk-premium-on-lending-lending-rate-minus-treasury-bill-rate
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    Dataset updated
    Jun 15, 2018
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2004 - Dec 1, 2016
    Area covered
    Sri Lanka
    Variables measured
    Money Market Rate
    Description

    Sri Lanka LK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 0.496 % pa in 2016. This records an increase from the previous number of 0.307 % pa for 2015. Sri Lanka LK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 1.446 % pa from Dec 2002 (Median) to 2016, with 14 observations. The data reached an all-time high of 2.737 % pa in 2009 and a record low of -0.022 % pa in 2008. Sri Lanka LK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

  6. Updating the Recession Risk and the Excess Bond Premium

    • catalog.data.gov
    • catalog-dev.data.gov
    Updated Dec 18, 2024
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    Board of Governors of the Federal Reserve System (2024). Updating the Recession Risk and the Excess Bond Premium [Dataset]. https://catalog.data.gov/dataset/updating-the-recession-risk-and-the-excess-bond-premium
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    Dataset updated
    Dec 18, 2024
    Dataset provided by
    Federal Reserve Board of Governors
    Federal Reserve Systemhttp://www.federalreserve.gov/
    Description

    The excess bond premium (EBP) is a measure of investor sentiment or risk appetite in the corporate bond market. A credit spread index can be decomposed into two components: a component that captures the systematic movements in default risk of individual firms and a residual component: the excess bond premium that represents variation in the average price of bearing exposure to US corporate credit risk, above and beyond the compensation for expected defaults. The EBP component of corporate bond credit spreads that is not directly attributable to expected default risk provides an effective measure of investor sentiment or risk appetite in the corporate bond market.

  7. T

    Tanzania TZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate

    • ceicdata.com
    Updated Dec 30, 2018
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    CEICdata.com (2018). Tanzania TZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate [Dataset]. https://www.ceicdata.com/en/tanzania/interest-rates/tz-risk-premium-on-lending-lending-rate-minus-treasury-bill-rate
    Explore at:
    Dataset updated
    Dec 30, 2018
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2005 - Dec 1, 2016
    Area covered
    Tanzania
    Variables measured
    Money Market Rate
    Description

    Tanzania TZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at -0.215 % pa in 2016. This records a decrease from the previous number of 3.234 % pa for 2015. Tanzania TZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 7.378 % pa from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 18.668 % pa in 1996 and a record low of -0.215 % pa in 2016. Tanzania TZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

  8. F

    S&P 500

    • fred.stlouisfed.org
    json
    Updated Jun 27, 2025
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    (2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500
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    jsonAvailable download formats
    Dataset updated
    Jun 27, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval

    Description

    View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

  9. d

    PREMIUM: Point of Interest (POI) Shopping Centers Dataset I Coverage...

    • datarade.ai
    .csv, .xls
    Updated Feb 27, 2025
    + more versions
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    CAP Locations (2025). PREMIUM: Point of Interest (POI) Shopping Centers Dataset I Coverage USA/Canada | GLA (SQFT), Parking & Tenant Counts | 14 Attributes [Dataset]. https://datarade.ai/data-products/premium-point-of-interest-poi-shopping-centers-dataset-i-cap-locations-1c45
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    .csv, .xlsAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    CAP Locations
    Area covered
    Canada, United States
    Description

    Key Features of the Premium Dataset: In addition to the core data found in the Basic Dataset, the Premium Dataset includes the following exclusive variables:

    •  Parking Availability – Information on available parking spaces, crucial for understanding accessibility and shopper convenience.
    •  Shopping Center Tenants Count – The number of tenants within a shopping center, providing insights into size, tenant diversity, and business activity.
    •  Actual Gross Leasable Area (GLA) in Square Footage – Accurate measurements of leasable space, allowing for better property comparisons and evaluations.
    •  ICSC Shopping Center Classifications – Categorization based on International Council of Shopping Centers (ICSC) standards, helping users distinguish between different types of retail centers, from regional malls to neighborhood centers.
    

    Benefits of the Premium Dataset:

    By incorporating these additional data points, CAP’s Premium Dataset supports a wide range of use cases, including: • Retail Expansion & Site Selection – Retailers can analyze tenant distribution, parking availability, and shopping center classifications to identify ideal locations for expansion. • Real Estate Investment & Development – Investors and developers gain valuable insights into shopping center sizes, tenant compositions, and classification trends to inform property acquisition and development decisions. • Competitive & Market Analysis – Businesses and analysts can compare shopping centers across multiple metrics, assess competition, and understand local market dynamics with greater precision.

    With its enhanced level of detail, the Premium USA & Canada Shopping Centers Dataset is an essential tool for retailers, real estate professionals, investors, and market researchers looking to make data-driven decisions with confidence.

  10. N

    Nepal NP: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate

    • ceicdata.com
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    CEICdata.com, Nepal NP: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate [Dataset]. https://www.ceicdata.com/en/nepal/interest-rates/np-risk-premium-on-lending-lending-rate-minus-treasury-bill-rate
    Explore at:
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 1997 - Dec 1, 2010
    Area covered
    Nepal
    Variables measured
    Money Market Rate
    Description

    Nepal NP: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 1.087 % pa in 2010. This records a decrease from the previous number of 2.897 % pa for 2009. Nepal NP: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 8.073 % pa from Dec 1981 (Median) to 2010, with 22 observations. The data reached an all-time high of 12.000 % pa in 1985 and a record low of 0.783 % pa in 1996. Nepal NP: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

  11. U

    United States US: Risk Premium on Lending: Lending Rate Minus Treasury Bill...

    • ceicdata.com
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    CEICdata.com (2009). United States US: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate [Dataset]. https://www.ceicdata.com/en/united-states/interest-rates/us-risk-premium-on-lending-lending-rate-minus-treasury-bill-rate
    Explore at:
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2005 - Dec 1, 2016
    Area covered
    United States
    Variables measured
    Money Market Rate
    Description

    United States US: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 3.186 % pa in 2016. This records a decrease from the previous number of 3.201 % pa for 2015. United States US: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 2.868 % pa from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 4.793 % pa in 1981 and a record low of 0.587 % pa in 1965. United States US: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

  12. m

    Data for: Trade integration and research and development investment as a...

    • data.mendeley.com
    Updated Jun 3, 2021
    + more versions
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    Paper Authors (2021). Data for: Trade integration and research and development investment as a proxy for idiosyncratic risk in the cross-section of stock returns [Dataset]. http://doi.org/10.17632/g2xc3mxcgy.2
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    Dataset updated
    Jun 3, 2021
    Authors
    Paper Authors
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We compile raw data from the Datastream database for all stocks traded on the Tokyo Stock Exchance, Osaka Exchange, Fukuoka Stock Exchange, Nagoya Stock Exchange and Sapporo Securities Exchange. Particularly, we collect the following data series, on a monthly basis: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), and (iv) primary SIC codes. Following Griffing et al. (2010), we exclude non-common equity securities from Datastream data. Additionally, we remove all companies with less than 12 observations in RI series for the period under analysis. Hence, our sample comprises 5,627 stocks, considering all companies that started trading or were delisted in the period under analysis. We use the three-month Treasury Bill rate for Japan, as provided by the OECD database, as a proxy for the risk-free rate. Accordingly, the dataset comprises the following series:

    1. Japan_25_Portfolios_MV_PTBV_M: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    2. Japan_20_Portfolios_MOM_M: Monthly returns for 20 momentum portfolios rebalanced in June of each year. (Raw data source: Datastream database)
    3. Japan_61_Portfolios_SECTOR_M: Monthly returns for 61 industry portfolios. (Raw data source: Datastream database)
    4. Japan_RF_M: Three-month Treasury Bill rate for Japan. (Raw data source: OECD)
    5. Japan_C_Q: Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
    6. Japan_Trade_Y: Trade openness for Japan, as measured by the variation rate of exports plus imports. (Raw data source: OECD)
    7. Japan_RD_Y: Variation rate of R&D investment for Japan. (Raw data source: OECD)
    8. Japan_IK_Y: Investment-capital ratio for Japan., determined using the methodology suggested by Cochrane (1991) (Raw data source: OECD)
    9. Japan_CCI_M: Consumer confidence index for Japan. (Raw data source: OECD)

    REFERENCES:

    Cochrane, J.H. (1991), Production-based asset pricing and the link between stock returns and economic fluctuations. The Journal of Finance, 46, 209-237. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

  13. D

    Data from: A New Model for Pricing Collateralized OTC Derivatives

    • ssh.datastations.nl
    pdf, zip
    Updated Jul 23, 2020
    + more versions
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    DANS Data Station Social Sciences and Humanities (2020). A New Model for Pricing Collateralized OTC Derivatives [Dataset]. http://doi.org/10.17026/dans-za7-rjzk
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    pdf(308846), zip(13461)Available download formats
    Dataset updated
    Jul 23, 2020
    Dataset provided by
    DANS Data Station Social Sciences and Humanities
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Date: 2020-07-22

  14. f

    Data from: Effects of Idiosyncratic Volatility in Asset Pricing

    • figshare.com
    • scielo.figshare.com
    jpeg
    Updated Jun 15, 2023
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    André Luís Leite; Antonio Carlos Figueiredo Pinto; Marcelo Cabus Klotzle (2023). Effects of Idiosyncratic Volatility in Asset Pricing [Dataset]. http://doi.org/10.6084/m9.figshare.20025373.v1
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    jpegAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    SciELO journals
    Authors
    André Luís Leite; Antonio Carlos Figueiredo Pinto; Marcelo Cabus Klotzle
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This paper aims to evaluate the effects of the aggregate market volatility components - average volatility and average correlation - on the pricing of portfolios sorted by idiosyncratic volatility, using Brazilian data. The study investigates whether portfolios with high and low idiosyncratic volatility - in relation to the Fama and French model (1996) - have different exposures to innovations in average market volatility, and consequently, different expectations for return. The results are in line with those found for US data, although they portray the Brazilian reality. Decomposition of volatility allows the average volatility component, without the disturbance generated by the average correlation component, to better price the effects of a worsening or an improvement in the investment environment. This result is also identical to that found for US data. Average variance should thus command a risk premium. For US data, this premium is negative. According to Chen and Petkova (2012), the main reason for this negative sign is the high level of investment in research and development recorded by companies with high idiosyncratic volatility. As in Brazil this type of investment is significantly lower than in the US, it was expected that a result with the opposite sign would be found, which is in fact what occurred.

  15. d

    Non-Life Insurance: Year-, Month- and Insurance-company-type-wise Market...

    • dataful.in
    Updated Apr 1, 2025
    + more versions
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    Dataful (Factly) (2025). Non-Life Insurance: Year-, Month- and Insurance-company-type-wise Market Share of Insurance Companies in the Gross Direct Premium Underwritten [Dataset]. https://dataful.in/datasets/19774
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    xlsx, csv, application/x-parquetAvailable download formats
    Dataset updated
    Apr 1, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    All India
    Variables measured
    Market Share
    Description

    The dataset contains year-, month- and company-wise complied data on the market share in the total amount of Gross Direct Premium Underwritten by each insurance company, categorized by General, Private, Public, Stand Alone, and Specialized PPSU Insurers, etc.

    Notes:

    1. As per IRDA definition, Underwriting refers to the process of assessing risk and ensuring that the cost of the cover is proportionate to the risks faced by the individual concerned. Based on underwriting, a decision on acceptance or rejection of cover as well as applicability of suitable premium or modified terms, if any, is taken

    2. Negative Values in the dataset are as per Official Source

  16. H

    The Possible Trinity: Optimal Interest Rate, Exchange Rate, and Taxes on...

    • data.niaid.nih.gov
    • dataverse.harvard.edu
    bin
    Updated Jun 24, 2014
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    Guillermo J. Escudé (2014). The Possible Trinity: Optimal Interest Rate, Exchange Rate, and Taxes on Capital Flows in a DSGE Model for a Small Open Economy[Dataset] [Dataset]. http://doi.org/10.7910/DVN/25238
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    binAvailable download formats
    Dataset updated
    Jun 24, 2014
    Authors
    Guillermo J. Escudé
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    A traditional way of thinking about the exchange rate regime and capital account openness has been framed in terms of the 'impossible trinity' or 'trilemma', according to which policymakers can only have two of three possible outcomes: open capital markets, monetary independence and pegged exchange rates. The present paper is a natural extension of Escude (A DSGE Model for a SOE with Systematic Interest and Foreign Exchange Policies in Which Policymakers Exploit the Risk Premium for Stabilization Purposes, 2013), which focuses on interest rate and exchange rate policies, since it introduces the third vertex of the 'trinity' in the form of taxes on private foreign debt. These affect the risk-adjusted uncovered interest parity equation and hence influence the SOE's international financial flows. A useful way to illustrate the range of policy alternatives is to associate them with the faces of an isosceles triangle. Each of three possible government intervention policies taken individually (in the domestic currency bond market, in the foreign currency market, and in the foreign currency bonds market) corresponds to one of the vertices of the triangle, each of the three possible pairs of intervention policies corresponds to one of the three edges of the triangle, and the three simultaneous intervention policies taken jointly correspond to the triangle's interior. This paper shows that this interior, or 'pos sible trinity' is quite generally not only possible but optimal, since the central bank obtains a lower loss when it implements a policy with all three interventions.

  17. R

    Data from: IFRS 17 implementation: market participants perspective

    • repod.icm.edu.pl
    tsv
    Updated Mar 21, 2025
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    Puławska, Karolina; Strzelczyk, Wojciech (2025). IFRS 17 implementation: market participants perspective [Dataset]. http://doi.org/10.18150/8MSDZA
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    tsv(8222)Available download formats
    Dataset updated
    Mar 21, 2025
    Dataset provided by
    RepOD
    Authors
    Puławska, Karolina; Strzelczyk, Wojciech
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Dataset funded by
    National Science Centre (Poland)
    Description

    The dataset consists of 68 observations (rows) and 24 variables (columns). It contains information about opinions issued on IFRS 17 implementation challenges. The dataset includes details about the opinion issuer (e.g., whether they are a practitioner, expert, researcher, or teacher), as well as binary indicators for various IFRS 17-related issues. These issues include problems with annual cohorts, the treatment of onerous contracts, discount rate calculation methods, the Premium Allocation Approach (PAA), first-time implementation difficulties, risk adjustment for non-financial risk, IT system challenges, and interactions between IFRS 9 and IFRS 17. Each issue is coded as “1” if mentioned in the opinion and “0” otherwise. The dataset allows for analyzing how different types of opinion issuers perceive IFRS 17 challenges and which problems are most frequently reported.Genaral Variables1. Company – Name of the company being evaluated.2. no – Identification number of the company.3. Company_n – Alternative identification of the company (possibly categorization).4. Data – Date of opinion issuance.5. old_num – Number of days that have passed since the opinion was issued.6. months_num – Number of months that have passed since the opinion was issued.7. BIG4_d – Binary variable (0/1) indicating whether the company is part of the Big Four.8. cathegory – Classification of the opinion issuer. The categories include:· practitioner – The opinion was issued by a practitioner.· expert – The opinion was issued by an expert.· other – The opinion was issued by someone categorized as "other."· researcher – The opinion was issued by a researcher.· teacher – The opinion was issued by an academic or educator.Opinion Issuer Characteristics:9. practitioner_d – Binary variable (0/1) indicating whether the opinion issuer is a practitioner.10. expert_d – Binary variable (0/1) indicating whether the opinion issuer is an expert.11. other_d / researcher_d / teacher_d – Binary variables (0/1) indicating whether the opinion issuer falls into one of these categories.Variables Related to Identified Problems:From here onward, the variables indicate whether the analyzed opinion highlights a specific problem. Each is coded as “1” if the problem is mentioned in the opinion, otherwise “0.” These align with the eight identified IFRS 17-related challenges:12. cohorts – Whether the opinion discusses problems related to annual cohorts, which may cause unnecessary complexity in financial reporting.13. contract_liabilities – Whether the opinion mentions problems with contract liabilities, which may include recognition, measurement, or classification issues.14. non-financial – Whether the opinion discusses risk adjustment for non-financial risk, which affects the measurement of insurance contracts.15. First-time – Whether the opinion mentions challenges related to first-time implementation, including the need for additional provisions and adjustments.16. IFRS 9 – Whether the opinion addresses interactions between IFRS 9 and IFRS 17, particularly how the classification of financial assets affects insurance contract accounting.17. rates – Whether the opinion discusses problems with the two methods for discount rate calculation, affecting the valuation of insurance liabilities.18. PAA – Whether the opinion refers to difficulties with the Premium Allocation Approach (PAA), an alternative method for measuring liabilities under IFRS 17.19. onerous – Whether the opinion highlights issues related to the treatment of onerous contracts, i.e., contracts expected to be loss-making, requiring special accounting treatment.20. understanding – Whether the opinion raises concerns about the complexity and understanding of IFRS 17, especially regarding its interpretation and practical application.21. systems – Whether the opinion highlights issues with IT systems, such as challenges in adapting accounting systems to IFRS 17 requirements.22. comparability – Whether the opinion mentions comparability issues, indicating concerns that IFRS 17 may reduce the consistency of financial reporting across companies.

  18. T

    Canada 10-Year Government Bond Yield Data

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 27, 2025
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    TRADING ECONOMICS (2025). Canada 10-Year Government Bond Yield Data [Dataset]. https://tradingeconomics.com/canada/government-bond-yield
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    excel, csv, json, xmlAvailable download formats
    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1985 - Jun 27, 2025
    Area covered
    Canada
    Description

    The yield on Canada 10Y Bond Yield eased to 3.32% on June 27, 2025, marking a 0.02 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.07 points, though it remains 0.19 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Canada 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on June of 2025.

  19. H

    Hong Kong SAR, China HK: Risk Premium on Lending: Lending Rate Minus...

    • ceicdata.com
    Updated Feb 15, 2018
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    CEICdata.com (2018). Hong Kong SAR, China HK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate [Dataset]. https://www.ceicdata.com/en/hong-kong/interest-rates/hk-risk-premium-on-lending-lending-rate-minus-treasury-bill-rate
    Explore at:
    Dataset updated
    Feb 15, 2018
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2003 - Dec 1, 2016
    Area covered
    Hong Kong
    Variables measured
    Money Market Rate
    Description

    Hong Kong HK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 4.300 % pa in 2016. This records a decrease from the previous number of 4.900 % pa for 2013. Hong Kong HK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 4.100 % pa from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 5.100 % pa in 2003 and a record low of 2.000 % pa in 1997. Hong Kong HK: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

  20. J

    Robust tests of forward exchange market efficiency with empirical evidence...

    • journaldata.zbw.eu
    .pmm, txt
    Updated Dec 8, 2022
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    Peter C.B. Phillips; James W. McFarland; Patrick C. McMahon; Peter C.B. Phillips; James W. McFarland; Patrick C. McMahon (2022). Robust tests of forward exchange market efficiency with empirical evidence from the 1920s (replication data) [Dataset]. http://doi.org/10.15456/jae.2022313.1132911299
    Explore at:
    txt(821), .pmm(70732)Available download formats
    Dataset updated
    Dec 8, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Peter C.B. Phillips; James W. McFarland; Patrick C. McMahon; Peter C.B. Phillips; James W. McFarland; Patrick C. McMahon
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This paper provides a robust statistical approach to testing the unbiasedness hypothesis in forward exchange market efficiency studies. The methods we use allow us to work explicitly with levels rather than differenced data. They are statistically robust to data distributions with heavy tails, and they can be applied to data sets where the frequency of observation and the futures maturity do not coincide. In addition, our methods allow for stochastic trend non-stationarity and general forms of serial dependence. The methods are applied to daily data of spot exchange rates and forward exchange rates during the 1920s, which marked the first episode of a broadly general floating exchange rate system. The tail behaviour of the data is analysed using an adaptive data-based method for estimating the tail slope of the density. The results confirm the need for the use of robust regression methods. We find cointegration between the forward rate and spot rate for the four currencies we consider (the Belgian and French francs, the Italian lira and the US dollar, all measured against the British pound), we find support for a stationary risk premium in the case of the Belgian franc, the Italian lira and the US dollar, and we find support for the simple market efficiency hypothesis (where the forward rate is an unbiased predictor of the future spot rate and there is a zero mean risk premium) in the case of the US dollar.

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(2025). Real Risk Premium [Dataset]. https://fred.stlouisfed.org/series/TENEXPCHAREARISPRE

Real Risk Premium

TENEXPCHAREARISPRE

Explore at:
jsonAvailable download formats
Dataset updated
Jun 11, 2025
License

https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

Description

Graph and download economic data for Real Risk Premium (TENEXPCHAREARISPRE) from Jan 1982 to Jun 2025 about premium, real, and USA.

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