We investigate the relationship between long-term US stock market risks and the macroeconomic environment using a two-component GARCH-MIDAS model. Our results show that macroeconomic variables are important determinants of the secular component of stock market volatility. Among the various macro variables in our dataset the term spread, housing starts, corporate profits and the unemployment rate have the highest predictive ability for long-term stock market volatility. While the term spread and housing starts are leading variables with respect to stock market volatility, for industrial production and the unemployment rate expectations data from the Survey of Professional Forecasters regarding the future development are most informative.

Interactive chart of the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

We apply extreme value analysis to US sectoral stock indices in order to assess whether tail risk measures like value-at-risk and extremal linkages were significantly altered by 9/11. We test whether semi-parametric quantile estimates of downside risk and upward potential have increased after 9/11. The same methodology allows one to estimate probabilities of joint booms and busts for pairs of sectoral indices or for a sectoral index and a market portfolio. The latter probabilities measure the sectoral response to macro shocks during periods of financial stress (so-called tail-s). Taking 9/11 as the sample midpoint we find that tail-?s often increase in a statistically and economically significant way. This might be due to perceived risk of new terrorist attacks.

Context

Stock market data is widely analyzed for educational, business interests.

Content

The data is the price history and trading volumes of the fifty stocks in the index NIFTY 50 from NSE (National Stock Exchange) India. All data represent 1min change with pricing and trading values split across .cvs files for each stock along with a metadata file with some macro-information about the stocks themselves. The data spans from 26 OCT 20 -18 JAN 21

Usability

Algorithmic Trading, Anomaly Detection, and Visualizing Trends.

The data is contained in the winrar file - 'DataSet-AssociationMining-India.rar'

Once you open the above winrar file, you will see the below files & folders:

• File: "IndiaData-ForAssociationMining.xlsx" is the primary data retrieved from 'Refinitiv-Datastream' which was used in the project.

• Folder-1MetricsGT-NSE50 o This folder has MS-Excel macro files used to create return determinant data to be eventually used in the 'Final-Transaction-Table' from which associations would be mined. o This folder also has computed returns for different holding periods for different stocks considered in this study. File: "0_nYrRtnGTNSE50.xlsm" o This folder also has the 'Final-Sheet' used for mining of association rules.

• Folder: 2Analysis-GTNSE50 o This folder has the R-program used to mine associations. It also has the final sheets used in association mining for different holding periods. And the output of the association rules mined is also stored here (File name: RulesRHS_1YrRtnGTNSE50.csv and so on)

• Folder: 3Validation o This folder has data related to the validation carried out in the project. It has 2 sub-folders: § 1-MetricsForValidation: This folder has excel-macro files to compute the metrics required in the Final-Table for validation of the association rules. § 2-BetaCalc-PortRtns: This folder has the Final transaction sheet which will be later used to compute portfolio beta and portfolio returns for each association rule. This also has the computation of portfolio beta & portfolio returns for each of the 10 association rules analyzed in this paper.

• Folder: 4LogitRegression o This folder has the 'R' program used to carry out Logit regression and different model consistency test. It also has the input file for the Logit regression (Filename: India-LogitRegression-csv.csv) o The sub-folder 'Regression_OP' has the output of Logit regression for all association rules for different holding periods.

A monthly and quarterly data set spanning July 1995 to December 2016 of the following macro-economic variables 1. South African stock market 2. South African GDP3. United States GDP 4. South African interest rate 5. US interest rate 6. South African inflation rate 7. US inflation rate 8. South African Money Supply 9. Rand/Dollar Exchange 10. FTSE

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

The data and programs replicate tables and figures from "A Model of the International Monetary System", by Farhi and Maggiori.

This paper illustrates a behavioral mixed frequency macro-finance model where both real and financial variables are generated on a daily basis. Further, while financial sector data is collected at the same frequency as it is generated (i.e. daily), real data can only be collected on a quarterly basis. Under these circumstances, output and inflation, upon which data is available with a significant delay, become unsuitable as the sole information guide for monetary policy. We suggest that policy makers can deal with this information problem by reacting to the variable on which data is collected on high frequency basis: the stock price.

Subsovereign Finance as Discipline: A Critical Macro-Finance Perspective on U.S. Territorial Markets

DOI: 10.5281/zenodo.15566317
Resource Type: Dataset
Publication Date: 2025-05-31
Author: Anon
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Copyright: © 2025 The Authors
Programming Language: Python
Repository Status: Active, replicable
Publisher: Zenodo
Version: 1.0

Description

This dataset and code repository support the macro-financial empirical study: Illiquidity Without Democracy, which analyzes the UBS Puerto Rico bond fund crisis through a critical macro-finance lens. By compiling liquidity and return data from the full Dow 30 alongside Puerto Rican financial equities, the project investigates how symbolic liquidity withdrawal operates as an instrument of financial subjugation in colonially governed markets.

The uploaded materials include:

• 35 Excel files containing firm-level trading data (Dow 30 and PR equities)

• A Python script (cmf.py) implementing Amihud illiquidity and Fama–MacBeth regressions

• A README.md file with detailed reproducibility instructions

Grounded in Bonizzi et al. (2022), this work contributes to the theory of financialized colonialism, where credit ratings, liquidity signals, and bond pricing are not merely technical indicators—but expressions of asymmetrical power. Our analysis empirically substantiates that liquidity constraints for Puerto Rican firms intensified during key institutional moments, revealing systemic exclusion embedded in neoliberal financial governance.

This study is grounded in Critical Macro-Finance (CMF), which we conceptualize as an extension of Jensen and Meckling’s Agency Theory. In its original formulation, Agency Theory views the firm as a nexus of contracts—a legal and economic structure shaped by negotiated relationships among stakeholders, each with divergent incentives. CMF builds on this foundation by embedding those contractual relationships within broader institutional and political-economic contexts.

Whereas traditional agency models emphasize micro-level incentive alignment, CMF expands the analytic frame to include systemic asymmetries in power, information, and legal infrastructure—especially in fragile or postcolonial states. From this perspective, contracts are not formed in a vacuum but are shaped by historical legal origins, regulatory capture, financial hierarchy, and global capital flows. Thus, CMF repositions the firm not only as a nexus of contracts but also as a node in a stratified macro-financial system where access to capital, enforceability of rights, and institutional trust are unequally distributed.

This theoretical orientation allows us to interrogate how formal governance structures conceal deeper distortions in financial inclusion, enforcement asymmetries, and systemic risk transmission, particularly in contexts where legal institutions fail to uphold equitable contracting environments.

Keywords

• Financialized colonialism

• Critical macro-finance

• Market microstructure

• Illiquidity

• Fama–MacBeth regression

• Puerto Rico debt crisis

• UBS bond fund

• Neoliberalism

• Event study

• Dow 30

• Amihud illiquidity

Historical dataset of the 12 month LIBOR rate back to 1986. The London Interbank Offered Rate is the average interest rate at which leading banks borrow funds from other banks in the London market. LIBOR is the most widely used global "benchmark" or reference rate for short term interest rates.

Statistics of macro-policy information and weak market efficiency changes.

Macro-policy information and weak efficiency changes.

Dataset created for the Master Thesis "What drives cryptocurrency market dynamics? Analysing external variable influence on cryptocurrency prices" as part of the Management of Technologies Masters at the TPM Faculty, TUDelft. The dataset contains the combined data for a range of variables for a number of crypto currencies. Each variable is in a column. Variables included for each crypto currency are: Price, Volume, Empirical wavelet transform, Google Trends. Additional variables are: Twitter (and Influencer) sentiment, Open status of some stock exchanges and Close price and volume for SP500, Oil and Gold.

Evaluation metrics and their calculations.

Convergence time(s) of different methods.

Access LSEG's Economic database, featuring global data coverage, consumer confidence data, and macro data indicators.

Top ten factors for composite indices.

View LSEG's extensive Economic Data, including content that allows the analysis and monitoring of national economies with historical and real-time series.

Natural gas fell to 3.39 USD/MMBtu on July 1, 2025, down 2.01% from the previous day. Over the past month, Natural gas's price has fallen 8.32%, but it is still 39.08% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on July of 2025.