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24 results for “currency”
Dataset with Risk estimates of major currency pairs on the Forex market
<p>This dataset includes Value at Risk (VaR) and Expected Shortfall (ES) estimations of the major currency pairs on the Forex market. Notably, it provides daily VaR and ES estimates for the AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, and USDJPY FX assets for January 2021 to September 2022. The reported risk estimates were calculated by various parametric and non-parametric models, including Variance-Covariance (VS), Historical Simulation (HS), Monte Carlo (MC), and Garch(1,1) at both 95% and 99% confidence levels. To enable model evaluation, the last column of each CSV file, named pnl, provides the actual daily returns of the FX asset.</p> <p>The data and code used to create this dataset are available at <a href="https://doi.org/10.5281/zenodo.7411148">Zenodo</a> and <a href="https://marketplace.infinitech-h2020.eu/assets/portfolio-value-at-risk-estimation">INFINITECH Marketplace</a>, respectively.</p>
Different currencies for calculating resource phenology result in opposite inferences about trophic mismatches
<p>Shifts in phenology are among the key responses of organisms to climate change. When rates of phenological change differ between interacting species they may result in phenological asynchrony. Studies have found conflicting patterns concerning the direction and magnitude of changes in synchrony, which have been attributed to biological factors. A hitherto overlooked additional explanation is differences in the currency used to quantify resource phenology, such as abundance and biomass. Studying an insectivorous bird, Sanderling, and its prey, we show that the median date of cumulative arthropod biomass occurred, on average, 6.9 days after the median date of cumulative arthropod abundance. In some years this difference could be as large as 21 days. For 23 years, hatch dates of Sanderlings became less synchronized with the median date of arthropod abundance, but more synchronized with the median date of arthropod biomass. The currency-specific trends can be explained by our finding that mean biomass per arthropod specimen increased with date. Using a conceptual simulation, we show that estimated rates of phenological change for abundance and biomass can differ depending on temporal shifts in the size distribution of resources. We conclude that studies of trophic mismatch based on different currencies for resource phenology can be incompatible with each other.</p>
Can Digital Currencies End Financial Exclusion in Indonesia? Economic Realities and Policy Ambitions
<p>Digitalisation is driving Indonesia’s economic development. The empowerment<br> of an efficient system of digital payments is a key underlying factor for the<br> establishment of an inclusive and well-developed digital economy. As a first<br> and fundamental step, the Bank of Indonesia launched the “National Cashless<br> Movement” (GNNT) in 2014. This vision was further advanced with the “Indonesia<br> Payment System Blueprint 2025” (IPS), which aimed at boosting digitalisation in<br> the banking industry through open banking and technology developments. As a<br> result, Indonesia’s digital economy and finance is recording remarkable upward<br> trends – such as an increase of 36.9 per cent in e-commerce and 52.6 per cent in<br> fintech lending transactions between 2020 and 2021. Similarly, cashless payments<br> are experiencing a spectacular growth. The usage of the Quick Response Indonesia<br> Standard (QRIS) system, which enhances cashless payment, has doubled. Credit<br> card transactions and e-money usage increased by 20 per cent and 51.6 per cent,<br> respectively, in the same two-year period.</p> <p>In this context of vibrant, quick transformations, a diverse ecosystem – which<br> comprises incumbent financial institutions, start-ups and technology companies<br> – is trying to advance Indonesia’s digital payments while targeting the goal of<br> financial inclusion. The rise of digital payment solutions indeed provides a unique<br> window of opportunity to tackle the current 92 million unbanked Indonesians<br> and 62 million small and medium-sized enterprises (SMEs) that are excluded from<br> the formal economy in Indonesia.</p>
Dataset: First Trust Emerging Markets Local Currency Bond ETF (FEMB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares Currency Hedged MSCI Germany ETF (HEWG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Different currencies for calculating resource phenology result in opposite inferences about trophic mismatches
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Molecular analysis of bacterial microbiota on Brazilian currency note surfaces
<p>MiSeq forward and reverse reads paired reads of the V4 region of the 16S rRNA gene. Data were generated from banknotes involving five denominations (2, 5, 10, 20, and 50), 30 notes each. Currency were randomly collected from three different street markets or "feiras" in the <em>metropolitan</em> region of <em>São Paulo.</em></p>
Are cryptocurrencies currencies? Bitcoin as legal tender in El Salvador
<p>A currency's essential feature is to be a medium of exchange. We leverage a quasi-natural experiment––El Salvador as the first country to make Bitcoin legal tender––to study a cryptocurrency's potential to be used in daily transactions. The government also launched and provided incentives to download and use a digital wallet named Chivo, which shares features with Central Bank Digital Currencies (CBDCs) and allows users to trade bitcoins and dollars. Were Chivo Wallet and Bitcoin actually adopted after this "big push"? Conducting a representative face-to-face survey and relying on blockchain data to obtain all Chivo transactions, we document how usage of digital payments and Bitcoin is low, concentrated, and has been decreasing over time. We find that privacy concerns are key barriers to adoption, which speaks to a policy debate on crypto and CBDCs that has had anonymity at its core. We also estimate the technology's adoption cost and its network externalities.</p>
Tang Dynasty Currency: Kai Yuan Tong Boa
Date Made: 718-732 CE (Tang Dynasty, 618-906) Materials: Bronze (AE) Place Made: Asia; China Measurements: Diameter: 25 mm; 1 in Accession Number: MH 2012.20.39 Credit Line: Purchase with the Marian Hayes (Class of 1925) Art Purchase Fund View this object on the Collections Database: http://museums.fivecolleges.edu/detail.php?museum=all&t=objects&type=all&f=&s=mh_2012_20_39&record=0 Photographs and photogrammetry by Laura Shea (Digital Collections Coordinator and Museum Photographer), Mount Holyoke College Art Museum; Copyright info: Contact the Mount Holyoke College Art Museum South Hadley, Massachusetts, USA 01075 413-538-2245 http://www.mtholyoke.edu/artmuseum/ The Mount Holyoke College Art Museum is dedicated to teaching and learning; therefore, we value your feedback and welcome any scholarly observations. Please feel free to contact us if you would like to learn more about the objects you see here or to request 3D models of other objects in our collections. Source: Objaverse 1.0 / Sketchfab
Golden international currency symbols
The symbols of currencies ( British Pound, US Dollar, Japanese Yen and the Euro ) made of gold.Gold texture is included. Source: Objaverse 1.0 / Sketchfab
"The Bahmani 'Currency Reform' of the Early Fifteenth Century in Light of the Akola Hoard"
<p><strong>Supplemental Data for Phillip B. Wagoner and Pankaj Tandon</strong></p> <p><strong>“The Bahmani ‘Currency Reform’ of the Early Fifteenth Century in Light of the Akola Hoard”</strong></p> <p><strong><em>American Journal of Numismatics</em> vol. 29 (2017)</strong></p> <p>The enclosed files contain all of the supplemental data from the Akola Hoard as reported in <em>AJN</em> 29 by Wagoner and Tandon. The files include the complete Excel spreadsheet and PDF copy, as well as CSV-formatted files of each sheet from the master spreadsheet, plus PNG images of histograms created from the data. The contents of the journal article and supplemental materials are licensed as CC-BY by the authors and the American Numismatic Society.</p>
Digital Currencies 2024
<p><strong>About Dataset</strong></p> <p>This dataset consists of seven columns and 2740 rows collected from thirteen different sources for digital currencies. The dataset includes information on the opening price, closing price, highest price, lowest price, and volume, as well as the percentage change and the currencies collected in March 2024.</p> <p>Here's a description of the contents based on the available columns in the data:</p> <p>Last Price: The most recent recorded price of Bitcoin. Open Price: The opening price of Bitcoin at the start of the specified time period. Max: The maximum price of Bitcoin during the specified time period. Min: The minimum price of Bitcoin during the specified time period. Size: This may refer to the trading volume of Bitcoin during the specified time period, but requires further clarification to confirm its meaning. Change Persent: The percentage change in the price of Bitcoin compared to the previous time period, it seems there's a typographical error and it might mean "Change Percent". Class: The classification of the currency, in this context, all the data is classified under "Bitcoin". This data could be useful in financial market analytics, especially for those interested in cryptocurrencies and the dynamics of Bitcoin prices. It can be used to study price changes, market fluctuations, or even to develop models for predicting cryptocurrency prices.</p> <p>Applications in Machine Learning and Beyond This dataset, focusing on Bitcoin prices and their fluctuations, has a wide range of applications, especially within the realm of machine learning and financial analysis:</p> <p>Price Prediction: Utilizing historical data to train models that can predict future Bitcoin prices. Techniques like time series analysis, regression models, and more sophisticated neural networks (e.g., LSTM) could be applied. Volatility Modeling: Analyzing the variability in Bitcoin prices over time. Machine learning models can help understand patterns in price fluctuations, potentially leading to insights for investors about risk and volatility. Trend Analysis: Identifying long-term trends in Bitcoin's market performance. Machine learning algorithms can detect underlying patterns and trends, helping investors make informed decisions. Anomaly Detection: Spotting unusual patterns or outliers in Bitcoin prices that could indicate market manipulation, fraud, or significant market events. Machine learning models, especially unsupervised algorithms, are adept at detecting anomalies. Sentiment Analysis: By integrating this dataset with social media and news sentiment data, models can assess how public sentiment impacts Bitcoin prices. This involves natural language processing (NLP) techniques to gauge sentiment and correlate it with price movements. Portfolio Management: In the broader scope of financial management, machine learning models can use such datasets to optimize cryptocurrency portfolios, balancing risk and return based on historical performance. Risk Assessment: Analyzing the data to evaluate the financial risk associated with Bitcoin investments. Machine learning can provide probabilistic estimates of future price drops or gains, aiding in risk management strategies. Overall, the detailed data on Bitcoin's pricing and trading volume offers a rich foundation for various analytical and predictive modeling efforts in both academic research and practical financial applications.</p> <p>Collected and Preprocessing: Wisam Abdullah , Dr. Modhar , and Dr. Ahmed Alsardly are lecturers in Tikrit University.</p>
Modern GB Currency (v0.1) .Blend FREE Low Poly
Introducing the Modern GB Currency made by ACBRadio, the programs used to make this object are as follows: Blender 2.92.0, & G.I.M.P 2.10.4…The 'Textures' inclued in this object are the following: Diffuse (One single texture, to rule them all)…free of Charge Note: Will be adding in the £50, £20 and even a better 20p and 50p soon, may even add in some limited editions if any come my way and possibly transparant notes 360 backdrop's total poly count: Triangles: 4k Vertices: 2k :D Source: Objaverse 1.0 / Sketchfab
Are cryptocurrencies currencies? Bitcoin as legal tender in El Salvador
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Currency Choice in Contracts
<p>Replication of Figures in: Drenik, Kirpalani and Perez (forthcoming), ``Currency Choice in Contracts", The Review of Economic Studies.</p>
Updated January 2022 Data Compiled by Auer, Cornelli, and Frost in "Rise of the central bank digital currencies: drivers, approaches and technologies" (2020) and added to by Bowen
<p>Auer, Cornelli and Frost pulled all of the data together and sorted and analyzed it for their paper "Rise of the central bank digital currencies: drivers, approaches and technologies" (2020). Bowen added the tabs "All Country Data," "Developed Country Data," and "Averaged Data for Speeches" for her own dissertation.</p>
Data from: Allocation to male vs. female floral function varies by currency and responds differentially to density and moisture stress
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CRYPTOCURRENCIES AND DIGITAL CURRENCIES: NAVIGATING THE INTERSECTION OF INNOVATION AND CYBERSECURITY
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Eviews_Revisiting Weak Currency_MLC
<p>MLC test for 23 countries against VN</p>
ISSUES AND IMPLEMENTATION OF FOREIGN CURRENCY OPERATIONS ACCORDING WITH IFRS AT JOINT STOCK COMPANY IN UZBEKISTAN
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.