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

This dataset is about books. It has 1 row and is filtered where the book is The levered equity risk premium and credit spreads : a unified framework. It features 7 columns including author, publication date, language, and book publisher.

This replication folder recreates all tables and figures in RFS article "The Market Risk Premium for Unsecured Consumer Credit Risk." For instructions, see the file "Instructions_ReplicationCode.pdf."

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.

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.;;

A common practice in business valuation and the determination of fair rates of return by regulatory agencies is to use the capital asset pricing model (CAPM) with the ad hoc addition of a country risk premium. The present paper documents this practice in the valuation reports required in public acquisition offers available on the CVM (Brazilian Securities and Exchange Commission) website. Multiple linear regression is used with monthly returns for stock shares of 204 firms listed on the BM&FBovespa (Brazilian Stock Exchange). The period covered is from January 2009 to December 2013, and the results indicate that there is a premium for Brazilian risk that is not completely reflected in Ibovespa returns for only 17 securities. Hence, if one uses the local market index when estimating a firm's cost of equity, it would be both redundant and incorrect to add a country risk premium. The paper concludes with a real company example in which the adoption of the conventional approach - with a country risk premium added - would lead to a 17% pricing error.

Bulgaria BG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 7.078 % pa in 2015. This records an increase from the previous number of 7.048 % pa for 2014. Bulgaria BG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 7.048 % pa from Dec 2006 (Median) to 2015, with 9 observations. The data reached an all-time high of 9.377 % pa in 2011 and a record low of 6.140 % pa in 2007. Bulgaria BG: 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 Bulgaria – Table BG.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.;;

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.; ;

This paper builds a DSGE model for a small open economy (SOE) in which the central bank systematically intervenes both the domestic currency bond and the FX markets using two policy rules: a Taylor-type rule and a second rule in which the operational target is the rate of nominal currency depreciation. For this, the instruments used by the central bank (bonds and international reserves) must be included in the model, as well as the institutional arrangements that determine the total amount of resources the central bank can use. The ‘corner’ regimes in which only one of the policy rules is used are particular cases of th e model. The model is calibrated and implemented in Dynare for 1) simple policy rules, 2) optimal simple policy rules, and 3) optimal policy under commitment. Numerical losses are obtained for ad-hoc loss functions for different sets of central bank preferences (styles). The results show that the losses are systematically lower when both policy rules are used simultaneously, and much lower for the usual preferences (in which only inflation and/or output stabilization matter). It is shown that this result is basically due to the central bank’s enhanced ability, when it uses the two policy rules, to influence capital flows through the effects of its actions on the endogenous risk premium in the (risk-adjusted) interest parity equation.

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.

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.; ;

Swaziland SZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 2.653 % pa in 2017. This records a decrease from the previous number of 3.048 % pa for 2016. Swaziland SZ: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 3.601 % pa from Dec 1982 (Median) to 2017, with 34 observations. The data reached an all-time high of 6.658 % pa in 2002 and a record low of 1.876 % pa in 2012. Swaziland SZ: 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 Eswatini – Table SZ.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.; ;

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.

Kenya KE: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 5.296 % pa in 2017. This records a decrease from the previous number of 8.047 % pa for 2016. Kenya KE: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 7.286 % pa from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 12.842 % pa in 2003 and a record low of -25.715 % pa in 1993. Kenya KE: 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 Kenya – Table KE.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.; ;

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.; ;

NY Property & Casualty Insurance Premiums Written 1998-2022

Benchmarking Insurer Performance

By State of New York [source]

About this dataset

This dataset contains total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Listings include essential financial security requirements that are required by Article 41 of the New York Insurance Law and provide insights into how the industry has evolved over time. This is an invaluable resource for researchers, analysts, policy makers, and insurance agents alike who wish to better understand the changing dynamics of the insurance market in New York. Download now and explore this unique dataset detailing net premiums written for insurers over a 20+ year period

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How to use the dataset

This dataset contains the total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Using this dataset, users can explore total property insurance premiums written over the course of twenty-four years in order to gain an understanding of the property insurance industry trends in New York State.

To use this dataset effectively, first download and read the Terms of Service before using the data. Once familiar with how to leverage data licenses effectively, you can analyze or visualize various facets of this large dataset. You may be interested in seeing changes over time and can compare these values with national averages or Gross Domestic Product (GDP) figures for periods analyzed.

Additionally, you could study any variation by geographic areas or other variables such as age groupings or type of policy written during a certain period. This dataset provides comprehensive insights that allow you to look at macro levels (loose overview) as well as more granular views depending on your questions and analysis methods. Regardless of your specific analysis goals; utilization of this open source data set should yield valuable insight into past trends which have potential impacts on future activities related to property and casualty insurance policies within New York State!

Research Ideas

• Identifying trends in Property & Casualty insurance rates over time in New York State to inform consumer decision making or policy strategies.
• Developing a risk management model by analyzing the financial security requirements of insurers in New York State and predicting potential premiums on different types of coverage areas.
• Comparing different insurers on their total net premiums written to compare their relative market size and influence within the state’s property & casualty insurance industry

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: total-property-insurance-premiums-written-annually-in-new-york-beginning-1998-1.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------------------------| | Net Premiums Written | The total amount of premiums written by the insurer in thousands. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

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.

Azerbaijan Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 2.216 % pa in 2017. This records a decrease from the previous number of 3.419 % pa for 2016. Azerbaijan Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 8.492 % pa from Dec 1998 (Median) to 2017, with 19 observations. The data reached an all-time high of 18.871 % pa in 2010 and a record low of 1.178 % pa in 1999. Azerbaijan 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 Azerbaijan – Table AZ.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.;;

This paper presents a new model for pricing OTC derivatives subject to collateralization. It allows for collateral posting adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral arrangement and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk. Date: 2020-07-22

Abstract copyright UK Data Service and data collection copyright owner. The Organisation for Economic Co-operation and Development (OECD) Insurance Statistics are presented in the following tables: Balance sheet and income The balance sheet and income dataset shows data for direct insurance and reinsurance by life, non-life and composite categories shown in US dollars or national currency. Data are available from 2008 onwards. Business written in the reporting country This dataset contains business written in the reporting country on a gross and net premium basis. It contains a breakdown by ownership between domestic companies, foreign-controlled companies and branches and agencies or foreign companies. It also comprises various type of premiums (gross premiums, premiums ceded, net written premium) as well as insurance type (life, non-life, composite) and facultative reinsurance may be included under (direct business or reinsurance accepted) according to practice in the reporting country. Data are expressed in national currency, USD or Euro (in millions) and presented from 1983 onwards. Commissions This dataset includes statistics related to commissions in the reporting country, containing a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies. The commissions in the reporting country can then be compared by ownership (domestic undertakings, foreign controlled undertakings, branches and agencies of foreign undertakings) by insurance type (life, non-life, composite) and facultative reinsurance (direct business, reinsurance accepted). Data are expressed in national currency, USD or Euro (in millions) and presented from 1993 onwards. Gross claims payments This dataset contains data related to gross claims payments in the reporting country, containing a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies. The core variable can be further analysed by type of insurance (life, non-life, composite). Data are expressed in national currency, USD or Euro (in millions) and starting from 1993 onwards. Gross operating expenses This dataset contains gross operating expenses in the reporting country, with a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies. This table also compares the core variable by type of insurance (life, non-life, composite) and currency (euro, USD). Data are available starting from 1993. Insurance activity indicators This comparative table comprises statistics on the insurance industry indicators as this sector is a key component of the economy by virtue of the amount of premiums it collects, the scale of its investment and the essential social and economic role it plays on personal and business risk coverage. This dataset includes insurance activity indicators such as market share, density, penetration, life insurance share, premiums per employee, retention ratio, ratio of reinsurance accepted, market share of foreign companies and market share of branches/agencies. Data are presented from 1983 onwards with annual datapoints. Insurance business by domestic and foreign risks This subset of OECD Insurance Statistics presents statistics on the insurance industry with a focus on domestic and foreign business risk. The type of risk can be further analysed by type of premium (net written premium, gross premiums, premium ceded), ownership (domestic company, foreign controlled undertakings, branches and agencies of foreign undertakings) and type of insurance (life, non-life, composite). Data are expressed in different currency terms and are presented from 1983 onwards. Insurance business written abroad by branches This dataset includes statistics pertaining to the insurance business written abroad by branches. It also includes variables such as premium type (gross premium, premium ceded, net written premium), branches and agencies, subsidiaries, insurance type (life, non-life, composite), partner country, direct business and reinsurance accepted. Data are expressed in national currency, USD or Euro (in millions) and are presented from 1983 onwards. Insurance business written in the reporting country This dataset includes statistics on business written in the reporting country by premiums (gross premium, premium ceded, net written premium), by classes of non-life insurance (freight insurance, general liability insurance, treaty reinsurance). Business should include all business written in the reporting country, whether in respect of domestic or foreign (worldwide) risks. Data are presented from 1987 onwards. General Insurance Statistics This dataset provides information on number of insurance companies and employees within the sector. The number of insurance undertakings is then examined by ownership (domestic undertakings, foreign controlled undertakings, branches and agencies of foreign undertakings) and by insurance type (life, non-life, composite, reinsurance). Number of insurance employees is also available by employer type (insurance undertakings, intermediaries). Data is available starting from 1983. Destinations of investments by direct insurance or reinsurance companies This dataset includes statistics related to outstanding investment by direct insurance companies, classified by investment category (real estate, mortgage loans, shares, bonds, loans, other investment), companies nationality, destination (foreign or domestic), ownership, insurance type, insurer type (direct insurer, reinsurer). Data are expressed in different currencies and are available from 1988 onwards. These data were first provided by the UK Data Service in October 2014.