The dataset shows structure of interest rates

Note: 1. For the year 1995-96, interest rate on deposits of maturity above 3 years, and from 1996-97 onwards, interest rates on deposit for all the maturities refer to the deposit rates of 5 major public sector banks as at end-March. 2. From 1994-95 onwards, data on minimum general key lending rates prescribed by RBI refers to the prime lending rates of 5 major public sector banks. 3. For 2011-12, data on deposit rates and Base rates of 5 major public sector banks refer to the period up to July 31, 2010. From July 1, 2010 BPLR System is replaced by Base Rate System. Accordingly the data reflects the Base Rate of five major public sector banks. Data for 2010-11 for Call/Notice Money rates are average of April-July 2010. 4. Data for dividend rate and yield rate for units of UTI are based on data received from Unit Trust of India. 5. Data on annual(gross) redemption yield of Government of India securities are based on redemption yield which is computed from 2000-01 as the mean of the daily weighted average yield of the transactions in each traded security. The weight is calculated as the share of the transaction in a given security in the aggregated value. 6. Data on prime lending rates for IDBI, IFCI and ICICI for the year 1999-00 relates to long-term prime lending rates in January 2000. 7. Data on prime lending rates for State Financial Corporation for all the years and for other term lending institutions from 2002-03 onwards relate to long-term (over 36-month) PLR. 8. Data on prime lending rate of IIBI/ IRBI from 2003-04 onwards relate to single PLR effective July 31, 2003. 9. IDBI ceased to be term lending institution on its conversion into a banking entity effective October 11, 2004. 10. ICICI ceased to be a term-lending institution after its merger with ICICI Bank. 11. Figures in brackets indicate lending rate charged to small-scale industries. 12. IFCI has become a non-bank financial company. 13. IIBI is in the process of voluntary winding up.

Graph and download economic data for 1-Month AMERIBOR Term Structure of Interest Rates (DISCONTINUED) (AMBOR1M) from 2021-06-20 to 2023-12-27 about AMERIBOR, 1-month, interest rate, interest, rate, and USA.

The data and programs replicate tables and figures from "A Quantity-Driven Theory of Term Premia and Exchange Rates," by Greenwood, Hanson, Stein, and Sunderam. Please see the Readme and Data Construction file for additional details.

We present and estimate a continuous time term structure model that incorporates observable macroeconomic variables and latent variables with a clear macroeconomic interpretation. Our model is able to accurately describe the joint dynamics for US macroeconomic variables and the yield curve. However, the observable variables do not explain the long end of the term structure. Central tendencies of these macroeconomic variables do a much better job in this respect. These unobservable factors also play an important role in the description of the interest rate policy rule. Both observable and non-observable factors determine the risk premia and hence bond excess holding returns.

This paper estimates a model in which persistent fluctuations in expected consumption growth, expected inflation, and their time-varying volatility determine asset price variation. The model features Epstein-Zin recursive preferences, which determine the market price of macro risk factors. Analysis of the US nominal term structure data from 1953 to 2006 shows that agents dislike high uncertainty and demand compensation for volatility risks. Also, the time variation of the term premium is driven by the compensation for inflation volatility risk, which is distinct from consumption volatility risk. The central role of inflation volatility risk in explaining the time-varying term premium is consistent with other empirical evidence including survey data. In contrast, the existing long-run risks literature emphasizes consumption volatility risk and ignores inflation-specific time-varying volatility. The estimation results of this paper suggest that inflation-specific volatility risk is essential for fitting the time series of the US nominal term structure data.

Kurmann and Otrok (2013) establish that the effects on economic activity from news on future productivity growth are similar to the effects from unexpected changes in the slope of the yield curve. This comment shows that these results become substantially weaker in the light of a recent update in the utilization-adjusted total factor productivity series produced by Fernald (2014).

The yield curve, also called the term structure of interest rates, refers to the relationship between the remaining time-to-maturity of debt securities and the yield on those securities. Yield curves have many practical uses, including pricing of various fixed-income securities, and are closely watched by market participants and policymakers alike for potential clues about the markets perception of the path of the policy rate and the macroeconomic outlook. This page provides daily estimated real yield curve parameters, smoothed yields on hypothetical TIPS, and implied inflation compensation, from 1999 to the present. Because this is a staff research product and not an official statistical release, it is subject to delay, revision, or methodological changes without advance notice.

Replication package for "Robust difference-in-differences analysis when there is a term structure" (code and data)

We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time-varying mean or shifting endpoint. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii) long-range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for shifting endpoints in yield curve factors provides substantial and significant gains in out-of-sample predictive accuracy, relative to stationary and random walk benchmarks. Forecast improvements are largest for long-maturity interest rates and for long-horizon forecasts.

The data and programs replicate tables and figures from "Perceptions about Monetary Policy", by Bauer, Pflueger, and Sunderam. Please see the Readme file for additional details.

Investment Banking Market Outlook

The global investment banking market size was valued at approximately USD 95 billion in 2023 and is projected to reach around USD 150 billion by 2032, growing at a CAGR of about 5.2% during the forecast period. This robust growth can be attributed to several factors, including a surge in merger and acquisition (M&A) activities, expanding equity and debt capital markets, and the rising need for financial advisory services globally.

One significant growth factor in the investment banking market is the increasing volume of mergers and acquisitions. Corporations are continuously seeking strategic acquisitions to expand their market presence, diversify their portfolios, and achieve operational synergies. This trend is particularly pronounced in sectors such as technology, healthcare, and energy, where innovation and consolidation are driving M&A activity. Investment banks play a crucial role in facilitating these transactions by providing advisory services, structuring deals, and arranging financing, thus fueling the demand for their services.

Another key driver of market growth is the expanding equity capital markets. The global economy's recovery post-pandemic, coupled with low interest rates, has fostered a favorable environment for equity offerings. Companies are increasingly raising capital through initial public offerings (IPOs) and follow-on public offerings (FPOs) to fund their growth and expansion plans. Investment banks act as intermediaries in these transactions, underwriting shares, and managing the issuance process, thereby benefiting from the increasing volume of equity market activities.

Debt capital markets are also contributing significantly to the growth of the investment banking market. In a low-interest-rate environment, corporations and governments are leveraging debt financing to fund large-scale projects and manage their capital structures. Investment banks assist in structuring, underwriting, and distributing debt instruments such as bonds and syndicated loans. As organizations continue to seek cost-effective financing options, the demand for investment banking services in the debt capital markets is expected to remain strong.

Regionally, North America remains a dominant player in the investment banking market, supported by its robust financial infrastructure, large corporate base, and high M&A activity. Europe follows closely, with significant contributions from the UK, Germany, and France. The Asia Pacific region is emerging as a lucrative market, driven by rapid economic growth, increasing cross-border transactions, and a growing number of high-net-worth individuals. Latin America and the Middle East & Africa are also witnessing gradual growth, supported by economic reforms and increasing investments in infrastructural development.

Service Type Analysis

Mergers & Acquisitions (M&A) represent a substantial segment within the investment banking market. This segment has been buoyed by the increasing complexity and volume of corporate consolidation activities. Investment banks provide critical advisory services, including valuation analysis, negotiation assistance, and regulatory compliance, ensuring smooth transaction execution. The globalization of businesses and the quest for competitive advantage have made M&A an indispensable tool for corporate growth, thus propelling the demand for investment banking services in this domain.

The Equity Capital Markets (ECM) segment is another vital area of the investment banking market. It encompasses services related to public and private equity offerings, including IPOs, FPOs, and private placements. Investment banks facilitate these transactions by underwriting securities and managing the issuance process, ensuring that companies can effectively raise capital. The recent surge in technology and biotech IPOs highlights the growing significance of ECM services, as companies in these sectors seek to capitalize on favorable market conditions and investor appetite.

The Debt Capital Markets (DCM) segment plays a crucial role in the investment banking landscape. It involves the issuance and trading of debt instruments such as bonds, loans, and structured finance products. Investment banks assist clients in structuring, pricing, and distributing these instruments, providing access to a broad base of investors. The low-interest-rate environment has amplified the appeal of debt financing, making DCM services integral to corporate and governmental fundraising efforts. As organizations strive to optimize thei

The Brazilian regulation for applying the Liability Adequacy Test (LAT) to technical provisions in insurance companies requires that the current estimate is discounted by a term structure of interest rates (hereafter TSIR). This article aims to analyze the LAT results, derived from the use of various models to build the TSIR: the cubic spline interpolation technique, Svensson's model (adopted by the regulator) and Vasicek's model. In order to achieve the objective proposed, the exchange rates of BM&FBOVESPA trading days were used to model the ETTJ and, consequently, to discount the cash flow of the insurance company. The results indicate that: (i) LAT is sensitive to the choice of the model used to build the TSIR; (ii) this sensitivity increases with cash flow longevity; (iii) the adoption of an ultimate forward rate (UFR) for the Brazilian insurance market should be evaluated by the regulator, in order to stabilize the trajectory of the yield curve at longer maturities. The technical provision is among the main solvency items of insurance companies and the LAT result is a significant indicator of the quality of this provision, as this evaluates its sufficiency or insufficiency. Thus, this article bridges a gap in the Brazilian actuarial literature, introducing the main methodologies available for modeling the yield curve and a practical application to analyze the impact of its choice on LAT.

In the context of predicting the term structure of interest rates, we explore the marginal predictive content of real-time macroeconomic diffusion indexes extracted from a data rich real-time data set, when used in dynamic Nelson-Siegel (NS) models of the variety discussed in Svensson (NBER technical report, 1994; NSS) and Diebold and Li (Journal of Econometrics, 2006, 130, 337-364; DNS). Our diffusion indexes are constructed using principal component analysis with both targeted and untargeted predictors, with targeting done using the lasso and elastic net. Our findings can be summarized as follows. First, the marginal predictive content of real-time diffusion indexes is significant for the preponderance of the individual models that we examine. The exception to this finding is the post Great Recession period. Second, forecast combinations that include only yield variables result in our most accurate predictions, for most sample periods and maturities. In this case, diffusion indexes do not have marginal predictive content for yields and do not seem to reflect unspanned risks. This points to the continuing usefulness of DNS and NSS models that are purely yield driven. Finally, we find that the use of fully revised macroeconomic data may have an important confounding effect upon results obtained when forecasting yields, as prior research has indicated that diffusion indexes are often useful for predicting yields when constructed using fully revised data, regardless of whether forecast combination is used, or not. Nevertheless, our findings also underscore the potential importance of using machine learning, data reduction, and shrinkage methods in contexts such as term structure modeling.

This data set contains the U.S. Treasury yield curve rates on a daily basis for a variety of maturities ranging from 1-month bills to 30-year bonds. Panel-formatted, it can be used for analyses of term structures of interest rates, forecasting of monetary policy, and time-series analysis of sovereign risk-free standards. It is especially appropriate for empirical applications of finance including bond pricing, cost of borrowing by municipalities, and macro-financial risk measurement.

RateWatch provides 3 datasets containing interest rates for deposits, loans and fees at branch level for U.S. financial institutions covering over 96,000 locations. Data is gathered from institutions of all types and sizes. The largest depth of data is around consumer products such as CDs, Savings, Checking, Money Markets, Auto Loans, Home Equity Loans and Mortgages. Within each category, details are available for multiple terms and/or dollar tiers. Data includes several identifying fields including institution name, address, routing number, asset size, institution type, MSA codes, latitude, longitude, state and city FIPS code. Consult relevant data dictionary for more information. Coverage: January, 2001 - June, 2022 File structure: text delimited (delimiter: pipe). "Join" file provides cross reference between an institution's rate setting location and its other branches. A given Institution may have multiple rate setting locations that may vary by product type.

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

We build and estimate an equilibrium model of the term structure of interest rates based on a recursive utility specification. We contrast it with an arbitrage-free model, where prices of risk are estimated freely without preference constraints. In both models, nominal bond yields are affine functions of macroeconomic state variables. The equilibrium model accounts for the tent-shaped pattern and magnitude of coefficients from predictive regressions of excess bond returns on forward rates and the hump-shaped pattern in the term structure of volatilities, while the reduced-form no-arbitrage model does not account for these important features of the yield curve.

Shadow Banking Market Outlook

The global shadow banking market size was valued at approximately USD 65 trillion in 2023 and is projected to reach USD 100 trillion by 2032, growing at a compound annual growth rate (CAGR) of 4.8% during the forecast period. This market's growth is propelled by the increasing demand for alternative financing solutions, particularly in the wake of stringent regulatory measures imposed on traditional banking systems post the 2008 financial crisis. Shadow banking systems offer a range of financial activities, closely mirroring those of regulated banks, but often operate outside conventional banking regulations, fostering innovation and flexibility.

One of the primary growth factors for the shadow banking market is the ability of non-bank financial entities to provide credit and liquidity to the economy, especially during periods of financial stress. With traditional banking becoming heavily regulated, shadow banks have emerged as crucial providers of credit, filling the gaps left by commercial banks. They offer tailored financial products that cater to specific needs of borrowers, often with greater flexibility in terms of credit terms and risk management. Additionally, the use of advanced financial technologies and analytics has significantly enhanced the efficiency and reach of shadow banking activities, making them increasingly attractive to borrowers and investors alike.

Another factor boosting the shadow banking market is the technological advancements that have enabled these entities to operate at lower costs and serve a broader customer base. The proliferation of fintech companies has introduced sophisticated platforms for peer-to-peer lending, crowdfunding, and other forms of credit intermediation, empowering shadow banks to reach underserved markets and provide competitive financial products. Moreover, the integration of big data analytics, AI, and machine learning into shadow banking practices has allowed these institutions to better assess risks, streamline operations, and enhance user experience, thus supporting market growth.

Additionally, the globalization of financial markets has played a pivotal role in the expansion of the shadow banking sector. Emerging markets, with their burgeoning middle-class populations and rapid economic growth, have shown a particular demand for financial services that traditional banks fail to adequately meet. Shadow banks, with their varied portfolio of services, have adeptly filled this gap, offering innovative solutions tailored to the unique requirements of these regions. This trend is expected to continue, particularly in Asia Pacific, which is witnessing rapid urbanization and digitalization, making it a key player in the shadow banking market.

Regionally, North America currently holds a significant share of the shadow banking market, driven by a well-established financial infrastructure and the presence of numerous institutional investors seeking alternative investment avenues. However, Asia Pacific is anticipated to exhibit the highest growth rate during the forecast period, fueled by increasing urbanization, technological advancements, and a rising need for diverse financial services. Europe also plays a vital role, given its strong regulatory framework aimed at ensuring financial stability while promoting innovation within the shadow banking sector. Meanwhile, Latin America and the Middle East & Africa are gradually recognizing the potential of shadow banking, albeit at a slower pace due to regulatory and economic challenges.

Component Analysis

The component analysis of the shadow banking market reveals significant insights into its structure, which is primarily divided into credit intermediation, asset management, and others. Credit intermediation is the largest component, encompassing activities such as lending, borrowing, and risk transformation conducted by entities outside the traditional banking system. This segment is integral as it forms the backbone of shadow banking, providing an alternative source of credit to individuals and businesses, often characterized by reduced regulatory oversight. The flexibility offered by credit intermediaries in terms of loan structuring, interest rates, and collateral requirements makes them an attractive option for borrowers who might not meet the stringent criteria set by traditional banks.

Asset management, on the other hand, involves the management of financial assets on behalf of investors, including hedge funds, private equity, and other investment funds. This segment is crucial as it channels vast amounts of capital into various

This table contains 39 series, with data for starting from 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

ABSTRACT We aim at obtaining a simple econometric model that allows us to build a confidence interval for the dispersion of the bids made by financial institutions at the central bank weekly auctions of short-term securities in Brazil. Under competitive conditions (e. g., no coalition between a few financial institutions) we assume that the bids’ dispersion is associated with the volatility of the daily interest rate futures prices and the daily interest rates that had prevailed during the days prior to the auction. Based on that assumption, our model succeeds in separating the two auctions with extremely high volatility. ln one of them, the high dispersion could be predicted using the other interest rate markets’ data; in the other the dispersion fell outside the confidence interval for the predicted dispersion. This can be used as empirical evidence of an attempt to comer the market that has indeed occurred at that date.