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This paper analyzes the dynamics of Canadian dollar–denominated (CAD) interest rate swap yields. It applies autoregressive distributive lag (ARDL) models, using monthly time series data, to estimate the effects of the current short-term interest rate on interest rate swap yields after controlling for relevant macro-financial variables. It shows that the current short-term interest rate is a crucial driver of the CAD swap yields of different maturity tenors. Previous empirical research testing the Keynesian hypothesis, which maintains that the current short-term interest rate has a decisive influence on the long-term interest rate, has discerned similar patterns for interest rate swaps denominated in other currencies. Thus, the findings of this paper lend additional support to the Keynesian hypothesis by showing that the same pattern holds for CAD interest rate swap yields.

ObjectiveTo examine the associations between maternal education and household income during early childhood with asthma-related outcomes in children aged 9–12 years in the UK, the Netherlands, Sweden, Australia, the USA and Canada.MethodsData on 31 210 children were obtained from 7 prospective birth cohort studies across six countries. Asthma-related outcomes included ever asthma, wheezing/asthma attacks and medication control for asthma. Relative social inequalities were estimated using pooled risk ratios (RRs) adjusted for potential confounders (child age, sex, mother ethnic background and maternal age) for maternal education and household income. The Slope Index of Inequality (SII) was calculated for each cohort to evaluate absolute social inequalities.ResultsEver asthma prevalence ranged from 8.3% (Netherlands) to 29.1% (Australia). Wheezing/asthma attacks prevalence ranged from 3.9% (Quebec) to 16.8% (USA). Pooled RRs for low (vs high) maternal education and low (vs high) household income were: ever asthma (education 1.24, 95% CI 1.13 to 1.37; income 1.28, 95% CI 1.15 to 1.43), wheezing/asthma attacks (education 1.14, 95% CI 0.97 to 1.35; income 1.22, 95% CI 1.03 to 1.44) and asthma with medication control (education 1.16, 95% CI 0.97 to 1.40; income 1.25, 95% CI 1.01 to 1.55). SIIs supported the lower risk for children with more highly educated mothers and those from higher-income households in most cohorts, with few exceptions.ConclusionsSocial inequalities by household income on the risk of ever asthma, wheezing/asthma attacks, and medication control for asthma were evident; the associations were attenuated for maternal education. These findings support the need for prevention policies to address the relatively high risks of respiratory morbidity in children in families with low socioeconomic status.

Objectives. To estimate changes in medical cannabis patient activity after Canada’s recreational cannabis legalization. Methods. I used linear regressions of interrupted times series models to analyze medical cannabis patient registrations per 10 000 residents, purchases per 100 registrations, and packages per purchase in Canada’s 10 provinces between April 2017 and December 2022. I tested relationships between the recreational law’s passage in June 2018, recreational sales starting in October 2018, and the arrival of edibles and vapes in December 2019. Results. Medical patient registrations initially increased; they slowed after the law passed and started decreasing after edibles became available. Medical purchasing frequencies initially decreased; they decreased further in proportion to recreational sales but stabilized after edibles became available. Medical purchase sizes were initially stable; they began increasing after edibles became available. Conclusions. Canada saw substantial decreases in medical cannabis patient registrations, but the remaining patients stabilized their purchasing frequencies and increased their purchase sizes. Public Health Implications. Other countries might see significant changes in patient usage of their medical cannabis systems after nationwide recreational cannabis legalization. ( Am J Public Health. 2024;114(S8):S673–S680. https://doi.org/10.2105/AJPH.2024.307721 )

The Russia–Ukraine war and new sanctions against Russia have created economic losers and winners. Supply chain shocks are made by two factors: the market’s extraordinary swings and the breadth of commodities exported by Russia and Ukraine including energy and raw material. This paper adopts the cross-quantilogram approach to visualize the effects of energy price shocks on the exchange rate movements during this war. Our findings indicate that energy price hikes are associated with the appreciation of the Canadian dollar against the Euro and Japanese yen. Considering the ongoing war in Ukraine, the best feasible policy responses are discussed.

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. This study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020 and macroeconomic variables such as EPU. This study investigates the moderating effects of EPU on investments in fixed and intangible assets and its effect on Tobin’s Q and the market price per share. The outcomes demonstrate that EPU reduces the costs of both tangible and intangible assets for businesses. In addition, companies with a higher Tobin’s Q and market price per share are more impacted by uncertain corporate investment policies. However, financial leverage is negatively correlated with share price and positively correlated with earnings per share and earnings per unit. Tobin’s Q positively correlates with financial leverage, indicating that firms that raise capital through debt are more likely to create value for investors. The research indicates that market-dependent enterprises are more susceptible to the unpredictability of monetary policy. According to this study, consistent application and open communication of economic policies are likely to increase the efficacy of company investments, resulting in more effective resource allocation and business decision-making.

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.

This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.

Despite major structural shifts in the international monetary system over the past six decades, the US dollar remains the dominant international reserve currency. Using a newly compiled database of individual economies’ reserve holdings by currency, this paper finds that financial links have been an increasingly important driver of reserve currency configurations since the global financial crisis, particularly for emerging market and developing economies. The paper also finds a rise in inertial effects, implying that the US dollar dominance is likely to endure. But historical precedents of sudden changes suggest that new developments, such as the emergence of digital currencies and new payments ecosystems, could accelerate the transition to a new landscape of reserve currencies.

This policy brief presents a new comprehensive dataset on the currency compositions of international reserves of 64 economies from 1996 to 2023. The dataset contains country-specific shares in international reserves for the eight major international currencies, i.e. the United States Dollar (USD), the Euro (EUR), the Japanese Yen (JPY), the British Pound (GBP), the Canadian Dollar (CAD), the Australian Dollar (AUD), the Chinese Yuan or Renminbi (CNY), and the Swiss Franc (CHF). The dataset provides an up-to-date and comprehensive account of publicly available data on the denomination of international reserves, including data on international currencies other than the USD, EUR, JPY, and GBP. While the USD and the EUR remain the dominant global reserve currencies, the data indicate their significance has diminished as countries diversify their reserves. Currencies, including the CNY, have gained prominence, hinting at a gradual fragmentation of the international monetary system. While the USD should retain its leading role in the medium term, ongoing geoeconomic shifts suggest a move towards a multipolar reserve currency landscape. The eventual look of this landscape will depend on the credibility of reserve currency candidates and their ability to retain the characteristics that make them desirable as reserve currencies in the face of future economic and political developments.