Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates (EMVMACROINTEREST) from Jan 1985 to Jul 2025 about volatility, uncertainty, equity, interest rate, interest, rate, and USA.

Celsius Holdings' stock increased by 5.7% as the Fed maintained interest rates, signaling potential rate cuts amidst economic uncertainty. The company recently expanded by acquiring Alani Nu.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Over the current period, waterproofing contractors have faced an overall decline in revenue. While the residential construction market performed well for some of the current period, consistently slow commercial construction activity hindered growth. Over the past five years, industry-wide revenue has been declining at an expected CAGR of 2.2%, reaching an estimated $5.2 billion in 2024, when revenue is set to increase 0.1% and profit is expected to have fallen to 7.2%. The outbreak of COVID-19 had mixed effects on waterproofing contractors. Low interest rates meant to spur the economy led to a housing market boom, driving industry demand through private spending on home improvements and housing starts. Despite low interest rates, economic uncertainty and falling corporate profit led to falling commercial construction activity. As interest rates have been elevated from 2022 into 2024, when the Federal reserve has begun to cut rates, residential and commercial construction activity has fallen. Elevated wage and purchase costs have drove down average industry profit margins in recent years. Over the outlook period, waterproofing contractors will return to growth. Growing housing starts will bolster waterproofing contractors' growth as mortgage rates eventually drop. Private spending on home improvements returning to growth will be a boon to contractors. An uptick in commercial building construction activity over the outlook period as interest rates continue to drop will also promote growth. Tax incentives for energy-efficient residential and commercial buildings will greatly benefit waterproofing contractors. Overall, industry revenue is expected to grow at a CAGR of 1.6% to reach $5.6 in 2029.

As of July 22, 2025, the yield for a ten-year U.S. government bond was 4.38 percent, while the yield for a two-year bond was 3.88 percent. This represents an inverted yield curve, whereby bonds of longer maturities provide a lower yield, reflecting investors' expectations for a decline in long-term interest rates. Hence, making long-term debt holders open to more risk under the uncertainty around the condition of financial markets in the future. That markets are uncertain can be seen by considering both the short-term fluctuations, and the long-term downward trend, of the yields of U.S. government bonds from 2006 to 2021, before the treasury yield curve increased again significantly in the following years. What are government bonds? Government bonds, otherwise called ‘sovereign’ or ‘treasury’ bonds, are financial instruments used by governments to raise money for government spending. Investors give the government a certain amount of money (the ‘face value’), to be repaid at a specified time in the future (the ‘maturity date’). In addition, the government makes regular periodic interest payments (called ‘coupon payments’). Once initially issued, government bonds are tradable on financial markets, meaning their value can fluctuate over time (even though the underlying face value and coupon payments remain the same). Investors are attracted to government bonds as, provided the country in question has a stable economy and political system, they are a very safe investment. Accordingly, in periods of economic turmoil, investors may be willing to accept a negative overall return in order to have a safe haven for their money. For example, once the market value is compared to the total received from remaining interest payments and the face value, investors have been willing to accept a negative return on two-year German government bonds between 2014 and 2021. Conversely, if the underlying economy and political structures are weak, investors demand a higher return to compensate for the higher risk they take on. Consequently, the return on bonds in emerging markets like Brazil are consistently higher than that of the United States (and other developed economies). Inverted yield curves When investors are worried about the financial future, it can lead to what is called an ‘inverted yield curve’. An inverted yield curve is where investors pay more for short term bonds than long term, indicating they do not have confidence in long-term financial conditions. Historically, the yield curve has historically inverted before each of the last five U.S. recessions. The last U.S. yield curve inversion occurred at several brief points in 2019 – a trend which continued until the Federal Reserve cut interest rates several times over that year. However, the ultimate trigger for the next recession was the unpredicted, exogenous shock of the global coronavirus (COVID-19) pandemic, showing how such informal indicators may be grounded just as much in coincidence as causation.

The benchmark interest rate in Romania was last recorded at 6.50 percent. This dataset provides the latest reported value for - Romania Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Fund Management Activities industry is undergoing a period of transformation, characterised by technological disruptions and shifting investor preferences. Firms that have embraced this innovation and demonstrated their ability to adapt have been well-positioned to navigate these challenges. That being said, companies have still been plagued by numerous economic headwinds, resulting in particularly volatile revenue in recent years. Revenue is expected to fall at a compound annual rate of 1.1% over the five years through 2025 to €175.7 billion, including a forecast rise of 3.7% in 2025. Economic uncertainty has been rife in recent years, with investors remaining cautious amid muted economic growth, sticky inflation and higher interest rates. Notably, 2022 was a tough year for capital markets, with the rising base rate environment triggering mass sell-offs in fixed-income markets and clobbering bond values. Stock markets didn’t fare much better, with the MSCI World Index ending the year down by 13.1%. Optimism was hard to come by going into 2023, but capital markets defied expectations, partially due to a solid performance from large cap tech stocks and investors pricing in rate cuts at the tail-end of the year, supporting capital inflows. This momentum is set to continue over the two years through 2025 despite inflation proving sticky, with investors remaining excited around AI and pricing in further rate cuts from central banks. A notable shift over recent years has been the transition to passive investing, reflected in the growing demand for ETFs. In response, many core portfolios are shifting to passives, with active managers increasingly pushing toward niche segments like ESG. Revenue is slated to swell at a compound annual rate of 6.3% over the five years through 2030 to €238.9 billion. Investment activity is set to remain healthy as investors expect further rate cuts and the excitement around AI persists. However, uncertainty lingers, with markets unsure about the impact of the US’s protectionist trade policies. Technological advancements will continue to gather pace in the coming years, with developments like robo-advisers becoming increasingly accurate and supporting investment returns. The excitement around ESG is expected to cool, with superpowers like the US showing resistance and some states actively discouraging ESG integration. This mindset will likely trickle across Europe, forcing fund managers to adapt their offerings and use phrases like ‘sustainable investing’ instead.

A data analysis investigating the influence of same-day and separate-day verification phases during repeated measurements of VO2peak on typical error (TE) and the proportion of individuals classified as responders (i.e. the response rate) following four weeks of high-intensity interval training (HIIT) or a no-exercise control period.

Data needed to reproduce the results from the manuscript “Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties" by L. Miskovic, J. Beal, M. Moret, and V. Hatzimanikatis

1. Data generated with the ORACLE workflow that was used in the iSCHRUNK training:

Classification label vectors for the three analyzed metabolic concentration cases:

Reference case: class_vector_train_ref.mat

Extreme1 case: class_vector_train_ex1.mat

Extreme2 case: class_vector_train_ex2.mat

Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.

Reference case: training_set_ref.mat

Extreme1 case: training_set_ex1.mat

Extreme2 case: training_set_ex2.mat

Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.

Reference case: ccXTR_ref.mat

Extreme1 case: ccXTR_ex1.mat

Extreme2 case: ccXTR_ex2.mat

Thermodynamics-based Flux Analysis (TFA) models for the three cases:

Reference case: tfa_ref.mat

Extreme1 case: tfa_ex1.mat

Extreme2 case: tfa_ex2.mat

1. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 4).

Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.

ccXTR_ValidNeg.mat

Parameter sets used in validation

validation_set_neg.mat

1. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Table 3).

Negative control:

Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.

Reference case: ccXTR_ValidRef_neg_agg.mat

Extreme1 case: ccXTR_ValidEx1_neg_agg.mat

Extreme2 case: ccXTR_ValidEx2_neg_agg.mat

Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.

Reference case: validation_set_ref_neg_agg.mat

Extreme1 case: validation_set_ref_neg_agg.mat

Extreme2 case: tvalidation_set_ref_neg_agg.mat

Positive control:

Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.

Reference case: ccXTR_ValidRef_pos_agg.mat

Extreme1 case: ccXTR_ValidEx1_pos_agg.mat

Extreme2 case: ccXTR_ValidEx2_pos_agg.mat

Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.

Reference case: validation_set_ref_pos_agg.mat

Extreme1 case: validation_set_ex1_pos_agg.mat

Extreme2 case: validation_set_ex2_pos_agg.mat

1. Reassignment study: validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 6 and Table 4).

Negative control:

Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers.

Reference case: ccXTR_Valid_reassignment_neg.mat

Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.

Reference case: validation_set_neg_reassignment.mat

Positive control:

Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers.

Reference case: ccXTR_Valid_reassignment_pos.mat

Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.

Reference case: validation_set_pos_reassignment.mat

Volatility in financial markets has been high in recent years, which has, at times, benefitted the brokerage industry through greater trading activity as investors look to capitalise on price swings. Most notably, the COVID-19 pandemic, the Ukraine conflict and aggressive interest hikes from Central Banks facing rampant inflation have incited severe volatility. Revenue is expected to grow at a compound annual rate of 2.7% over the five years through 2023-24 to £38.1 billion, including estimated growth of 3.9% in 2023-24. Although volatility can benefit the industry, it can also deter investors, incentivising them to delay investments until economic uncertainty subsides. In recent years, uncertainty has mainly stemmed from the aggressive interest rate hikes and their expected trajectory, hitting stock and bond markets in 2022 and hurting trading activity. Although interest rate uncertainty persisted going into 2023-24, stock markets improved thanks to exceptional growth from large-cap tech stocks and a sharp rally at the end of the year as investors bet on the end of rate hikes. Competition has softened as considerable consolidation activity has occurred between SMEs in the brokerage industry. However, the Markets in Financial Instruments Directive II has ramped up operating costs for brokerage firms, hurting profitability. Continued investment in software to help automate compliance procedures have benefitted margins, although the brokerage industry remains labour-intensive. Revenue is forecast to grow at a compound annual rate of 3.5% over the five years through 2028-29 to £45.2 billion, while the average industry profit margin is expected to reach 24.8%. The market narrative for interest rates is higher for longer, weighing on stock markets and hitting demand for brokers as trading activity slows. However, rate cuts are expected to occur in the second half of 2024-25, supporting bond values and stocks driving revenue growth in the short term. Further regulations related to Basel III are set to come into force in January 2025, adding pressure to brokers' operating costs. Due to Brexit, large international brokers are also shifting employees to overseas domiciles, adding downward pressure to revenue growth.

Gold prices hold steady near record highs as geopolitical tensions and economic risks drive demand for the safe-haven asset.

Modern models of s-process nucleosynthesis in stars require stellar reaction rates of high precision. Most neutron-capture cross-sections in the s-process have been measured, and for an increasing number of reactions the required precision is achieved. This does not necessarily mean, however, that the stellar rates are constrained equally well, because only the capture of the ground state of a target is measured in the laboratory. Captures of excited states can contribute considerably to stellar rates that are already at typical s-process temperatures. We show that the ground-state contribution X to a stellar rate is the relevant measure to identify reactions that are or could be well constrained by experiments and apply it to (n,{gamma}) reactions in the s-process. We further show that the maximum possible reduction in uncertainty of a rate via determination of the ground-state cross-section is given directly by X. An error analysis of X is presented, and it is found that X is a robust measure with mostly small uncertainties. Several specific examples (neutron capture of ^79^Se, ^95^Zr, ^121^Sn, ^187^Os, and ^193^Pt) are discussed in detail. The ground-state contributions for a set of 412 neutron-capture reactions around the s-process path are presented in a table. This allows identification of reactions that may be better constrained by experiments and that cannot be constrained solely by measuring ground-state cross-sections (and thus require supplementary studies).

This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Accurate simulations usually entail a high computational effort, while coarse simulations are obtained at a lower cost. The cost can be measured, for example, by the run time of the simulator or the financial cost of the computing resources. In this setting, simulation results obtained at several levels of fidelity can be combined in order to estimate quantities of interest (the optimal value of the output, the probability that the output exceeds a given threshold, etc.) in an efficient manner. We propose a new Bayesian sequential strategy called maximal rate of stepwise uncertainty reduction (MR-SUR), that selects additional simulations to be performed by maximizing the ratio between the expected reduction of uncertainty and the cost of simulation. This generic strategy unifies several existing methods, and provides a principled approach to develop new ones. We assess its performance on several examples, including a computationally intensive problem of fire safety analysis where the quantity of interest is the probability of exceeding a tenability threshold during a building fire.

Brokers benefit in these times as the higher the business's net profit, the higher the valuation, meaning the higher commission earned. However, uncertainty-inducing events like Brexit, COVID-19 and high inflation have severely dented business confidence leading to a dip in business transactions. As a result, industry revenue is estimated to fall at a compound annual rate of 5% to £268.1 million over the five years through 2024-25. The COVID-19 pandemic forced businesses to shift away from expansion and towards cost-cutting and restructuring, limiting the need for brokers. The high-inflation landscape in 2022 and 2023 led to the Bank of England raising the base rate to its highest since the global financial crisis. High-interest rates weigh on buyers' purchasing power, constraining brokers' commissions. Although larger brokers like Christie Group were able to maintain revenue growth by raising fees in 2022-23, having less exposure to price competition thanks to their established brand image, smaller players saw their revenue drop in line with M&A activity. With a renewed decline in M&A activity during 2023-24 amid rising interest rates and inflation, even Christie Group cited a decrease in its transactional brokerage incomes. 2024-25 is set to mark a turnaround for the industry as business confidence recovers in anticipation of interest rate cuts and subsiding economic uncertainty, driving revenue growth of 3.1%. Over the five years through 2029-30, business brokers' revenue is estimated to grow at a compound annual rate of 5% to £342.3 million. SMEs are positioned for solid growth in the coming years as interest rates come down, alleviating cost of debt pressures, and economic growth picks up. This will lift revenue growth through greater M&A activity as they seek to expand. The critical change for business brokers will be the ability to leverage artificial intelligence (AI) powered tools and data analytics to raise productivity. AI and data analytics will boost the efficiency of the day-to-day tasks of brokers, like market research, valuations, matching buyers and sellers and due diligence. Brokers can become more client-focused and focus their time on strategic activities like deal negotiation, client engagement and relationship building.

Validation of the robust distributions of the top 3 parameters for both a negative and a positive control of HXK over XTR.

Robust ranges of the top 3 parameters over 3 concentrations.

BackgroundMillennium Development Goal 4 calls for an annual rate of reduction (ARR) of the under-five mortality rate (U5MR) of 4.4% between 1990 and 2015. Progress is measured through the point estimates of the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). To facilitate evidence-based conclusions about progress toward the goal, we assessed the uncertainty in the estimates arising from sampling errors and biases in data series and the inferior quality of specific data series. Methods and FindingsWe implemented a bootstrap procedure to construct 90% uncertainty intervals (UIs) for the U5MR and ARR to complement the UN IGME estimates. We constructed the bounds for all countries without a generalized HIV epidemic, where a standard estimation approach is carried out (174 countries). In the bootstrap procedure, potential biases in levels and trends of data series of different source types were accounted for. There is considerable uncertainty about the U5MR, particularly for high mortality countries and in recent years. Among 86 countries with a U5MR of at least 40 deaths per 1,000 live births in 1990, the median width of the UI, relative to the U5MR level, was 19% for 1990 and 48% for 2011, with the increase in uncertainty due to more limited data availability. The median absolute width of the 90% UI for the ARR from 1990 to 2011 was 2.2%. Although the ARR point estimate for all high mortality countries was greater than zero, for eight of them uncertainty included the possibility of no improvement between 1990 and 2011. For 13 countries, it is deemed likely that the ARR from 1990 to 2011 exceeded 4.4%. ConclusionsIn light of the upcoming evaluation of Millennium Development Goal 4 in 2015, uncertainty assessments need to be taken into account to avoid unwarranted conclusions about countries' progress based on limited data. Please see later in the article for the Editors' Summary

IntroductionWind turbine generators (WTGs) and electric vehicles (EVs) are used as source-side and load-side resources, respectively. And the uncertainties of WTGs output and EVs charging load seriously affect the frequency stability of the power system. For the wind speed prediction error and the uncertainty of EV off-grid time, a cooperative frequency modulation (FM) strategy is proposed.MethodsFirstly, the wind speed interval is divided based on the operating characteristics of WTGs and the load reduction rate. On the basis, a load reduction operation strategy based on rotor speed control and pitch Angle control is proposed to enable WTGs to have bidirectional FM capability. The adjustable capacity of WTGs is determined based on the wind speed prediction error and the operation strategy of load reduction; Secondly, based on the controllable domain model of an EV considering the off-grid time uncertainty, the adjustable capacity of EV clusters is determined by state grouping of the state of charge (SOC) according to its charging urgency. By defining EV FM capability parameters and charging urgency parameters, the EV priority list for FM is determined and the power allocation strategy is proposed; Then, based on the urgency of EV charging and the economy of WTGs load reduction operation, a cooperative FM task assignment strategy is proposed.ResultsSimulations demonstrate the strategy enhances FM capability and improves FM effect by 6.05% compared to fixed-proportion task allocation. It strengthens frequency stability by leveraging complementary strengths.DiscussionConsideration of wind speed prediction errors can improve WTG adjustable capacity estimation, boosting FM accuracy; Coordinated task allocation minimizes WTGs intermittent FM output impacts, ensuring stable grid frequency. This dual-source-load approach offers a robust solution for modern power systems with high renewable penetration.

A detailed mechanism for combustion of iso-octane with 116 species and 754 reactions has been reduced using a directed relation graph with error propagation (DRGEP) and DRGEP with sensitivity analysis (DRGEPSA) methods under high-temperature conditions. Two skeletal mechanisms, i.e., a 63-species mechanism with a maximum error of 7.2% and a 51-species mechanism with a maximum error of 28.5% on autoignition delay times have been generated. These two skeletal mechanisms are shown to reproduce ignition delays, laminar flame speeds, species and temperature profiles in good agreement with those of the detailed mechanism. Uncertainty in the ignition predictions by detailed and two skeletal mechanisms induced by the uncertainties in reaction rate coefficients has been studied. Probability distribution of autoignition predictions demonstrated that the 63-species mechanism can still keep the uncertainty characteristics, while the 51-species mechanism has significant discrepancy compared with the detailed one. Further analysis of autoignition shows that the structure and integrality of the reaction system in the 51-species mechanism has changed. Global sensitivities of 63-species and detailed mechanisms on ignition have been investigated using the high-dimensional model representation (HDMR) method. The highly important reactions for ignition in the detailed mechanism are the same as those in the 63-species mechanism, and sensitivity coefficients of the listed reactions agree well with each other. The most important reactions in the first-order sensitivity on autoignition in the detailed mechanism are the same as those in the 63-species mechanism, especially for the five most important reactions. The most important 10 reactions contribute almost 75% to the overall variance in ignition delay under the present conditions, while the second-order effects are quite small and almost negligible. The top ranked reactions show that small-molecule chemistry (C0–C4) contributes significantly to uncertainties in the ignition predictions at high temperatures.

Results comparison for different uncertainty treatments.