Funds Interest Rate Research Articles

Analysis of Factors Affecting Bank Profitability with Total Assets Minimum 1 (One) Billion

<p>This research was conducted to examine the effect of variable banking ratio, funds interest rate, interest spread, fee based income ratio and inefficiency ratio of Profitability (ROA). Profitability is used to measure the effectiveness of management based on results generated from the loan repayment and investment. The ratio is important for the bank's profitability is Return On Assets (ROA). Financial ratios that affect the ROA is the banking ratio, funds interest rate, interest spread, fee based income ratio and inefficiency ratio. The sampling technique used was purposive sampling with the criteria of commercial bank serving the financial statements. The analysis technique used is the classical assumption of the analysis, multiple regression analysis and hypothesis test with a level of significance of 8,841%. The results of the research simultaneously (test F) states that the banking ratio, funds interest rate, interest spread, fee based income ratio and inefficiency ratio jointly affect the profitability (ROA) of banks. While the results show that the correlation coefficient between profitability (ROA) of banks with 5(five) independent variables of 60,336%. And the result of research partially (t) states that the variable interest spread did not have a significant effect on profitability (ROA) of banks. And variable banking ratio, funds interest rate, fee based income ratio and inefficiency ratio significant effect on profitability (ROA) of banks.</p>

Analysis of Factors Affecting Bank Profitability with Total Assets Minimum 1 (One) Billion

This research was conducted to examine the effect of variable banking ratio, funds interest rate, interest spread, fee based income ratio and inefficiency ratio of Profitability (ROA). Profitability is used to measure the effectiveness of management based on results generated from the loan repayment and investment. The ratio is important for the bank's profitability is Return On Assets (ROA). Financial ratios that affect the ROA is the banking ratio, funds interest rate, interest spread, fee based income ratio and inefficiency ratio. The sampling technique used was purposive sampling with the criteria of commercial bank serving the financial statements. The analysis technique used is the classical assumption of the analysis, multiple regression analysis and hypothesis test with a level of significance of 8,841%. The results of the research simultaneously (test F) states that the banking ratio, funds interest rate, interest spread, fee based income ratio and inefficiency ratio jointly affect the profitability (ROA) of banks. While the results show that the correlation coefficient between profitability (ROA) of banks with 5(five) independent variables of 60,336%. And the result of research partially (t) states that the variable interest spread did not have a significant effect on profitability (ROA) of banks. And variable banking ratio, funds interest rate, fee based income ratio and inefficiency ratio significant effect on profitability (ROA) of banks

Federal Reserve's Monetary Policy and Its Impact on China's Economic Development

The global financial crisis in 2008 caused great trauma to the economy and financial markets all over the world. Following the onset of the financial crisis, the monetary authorities of various nations implemented a range of monetary policies to deal with the crisis's negative impact on the real economy. The United States, as the world's largest country, the implementation of the Fed's monetary policy has a significant impact not only on its own macro-economy, but also on the world macro-economy. Following the onset of the financial crisis in 2008, the Federal Reserve implemented a series of monetary policy operations, including lowering the federal fund interest rate and the money market re-discount interest rate, to address the financial crisis' impact on the domestic economy of the United States. Unconventional monetary policy mainly includes reducing the federal funds rate to the zero range boundary. Based on previous studies, this paper finds that the monetary policies of the Federal Reserve mainly affect other countries' economies through four channels: asset price, exchange rate, interest rate and credit. At the same time, in view of the negative spillover effects brought by the Federal Reserve, China should adopt proactive financial policies to resist risks.

PREDICTION OF FEDERAL FUNDS RATE TIME SERIES

In this paper, we will try to adjust the behavior of the US Federal Funds interest rate to a model of autoregressive Levy-stable. We will conduct a series of tests after which it will offer the best model for this data series distributed in time, in this case, a linear model type AR (1) stationary whose distribution i.i.d. innovations would be a stable Lévy law and proceed thereafter to the estimation of nine parameters for this model.

International monetary spillovers and macroeconomic stability in developing countries

<abstract> <p>This paper analyses the impact of international spillovers on macroeconomic stability in developing countries. Specifically, the study investigates the impact of United States (US) monetary policy spillovers in the form of US inflation and Federal funds interest rate on Uganda and Kenya's inflation rates, interest rates and the exchange rates, key macroeconomic indicators of importance to macroeconomic stability. The focus on international spillovers from the USA is due to the dominant role it plays in determining global economic conditions. The study applies the Generalized Vector Autoregressive (GVAR) approach to quantify spillovers across these economies. The results shows that despite recent efforts towards East African regional integration, international spillovers from global economies like the US are more significant in determining macroeconomic stability in developing countries, underscoring the importance of global policy coordination. Specifically, we find an amplification of return and volatility spillovers after the onset of the Global financial crisis.</p> </abstract>

Cryptocurrency reaction to FOMC Announcements: Evidence of heterogeneity based on blockchain stack position

We examine the response of a broad set of digital assets to US Federal Fund interest rate and quantitative easing announcements, specifically examining associated volatility spillover and feedback effects. We classify each digital asset into one of three categories: Currencies; Protocols; and Decentralised Applications (dApps). Currency-based digital assets experience idiosyncratic spillovers in the period immediately after US monetary policy announcements, while application or protocol-based digital assets are largely immune to policy volatility spillover and feedback. Mineable digital assets are found to be more susceptible to monetary policy volatility spillovers and feedback than non-mineable. Responses indicate a diverse market within which, not all assets are comparable to Bitcoin.

Outside the Box: Unconventional Monetary Policy in the Great Recession and Beyond

In November 2008, the Federal Reserve faced a deteriorating economy and a financial crisis. The federal funds rate had already been reduced to virtually zero. Thus, the Federal Reserve turned to unconventional monetary policies. Through “quantitative easing,” the Fed announced plans to buy mortgage-backed securities and debt issued by government-sponsored enterprises. Subsequent purchases would eventually lead to a five-fold expansion in the Fed’s balance sheet, from $900 billion to $4.5 trillion, and leave the Fed holding over 20 percent of all mortgage-backed securities and marketable Treasury debt. In addition, Fed policy statements in December 2008 began to include explicit references to the likely path of the federal funds interest rate, a policy that came to be known as “forward guidance.” The Fed ceased its direct asset purchases in late 2014. Starting in October 2017, it has allowed the balance sheet to shrink gradually as existing assets mature. From December 2015 through June 2018, the Fed has raised the federal funds interest rate seven times. Thus, the time is ripe to step back and ask whether the Fed’s unconventional policies had the intended expansionary effects—and by extension, whether the Fed should use them in the future.

Cryptocurrency Reaction to FOMC Announcements: Evidence of Heterogeneity Based on Blockchain Stack Position

We examine the response of a broad set of digital assets to US Federal Fund interest rate and quantitative easing announcements, specifically examining associated volatility spillover and feedback effects. We classify each digital asset into one of three categories: Currencies; Protocols; and Decentralised Applications (dApps). Currency-based digital assets experience idiosyncratic spillovers in the period immediately after US monetary policy announcements, while application or protocol-based digital assets are largely immune to policy volatility spillover and feedback. Mineable digital assets are found to be more susceptible to monetary policy volatility spillovers and feedback than non-mineable. Responses indicate a diverse market within which, not all assets are comparable to Bitcoin.

The Federal Funds Interest Rate Meets the Prime Rates

This paper explores the impacts of the Federal Funds Rate (FFR), a significant money market indicator, on the prime lending rates offered by commercial banks. Prior to 1994, the FFR had lagged effects on prime rates, but since the second quarter of 1994, the Federal Reserve Bank has implemented monetary policy which explicitly and publicly target the FFR. Commercial banks have since relied on the FFR to promptly modify the prime rate in response to changing policies. The results of the study signify that transparency in monetary policy targeting can facilitate desired effects, thereby increasing efficiency.

Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models

Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. A comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models is performed, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE–VAR and Factor Augmented DSGEs and tested against standard, Bayesian and Factor Augmented VARs. Moreover, small scale models including the real gross domestic product, the harmonized consumer price index and the nominal short-term federal funds interest rate, are comparatively assessed against medium scale models featuring additionally sticky nominal prices, wage contracts, habit formation, variable capital utilization and investment adjustment costs. The investigated period spans 1960:Q4–2010:Q4 and forecasts are produced for the out-of-sample testing period 1997:Q1–2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.

Unemployment, Consumer Confidence, Business Confidence, Inflation and Monetary Policy

This study examines how unemployment rate dynamically responds to shocks to consumer confidence, business confidence, inflation and monetary policy. Based on the vector autoregressive (VAR) analysis of the monthly data from 1978:M2 to 2012:M5, the results show that unemployment negatively responds to shocks to consumer confidence and business confidence. While consumer confidence is not responsive to FED interest rate shock, business confidence negatively responds to FED fund interest rate shock. The results also show consumer confidence and business confidence Granger-cause unemployment, and inflation Granger-causes consumer confidence. However, inflation and Fed Fund interest rate do not Granger-cause unemployment movement. For the 12-month horizon, the unemployment forecast error is 71% due to its own shock, 11% due to consumer confidence shock, 13% due to business confidence shock, 1.10% due to inflation shock, and 3% due to Fed Fund interest rate shock.

To Save Or To Spend: That Is The Tax Question?

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-family: Times New Roman; font-size: x-small;">While the effects of interest rates on consumption and savings has always been a concern to economists, there is still a great debate regarding the magnitude of the after-tax interest rate effect on savings. The response of consumption and savings to such changes in after tax interest rates potentially alter the United States private savings rate, which may influence our economic growth. While the Federal Reserve has executed the most aggressive expansionary monetary policy in history by lowering the Federal Funds interest rate by more than 10 times in the past year in an attempt to increase consumption to rebound from the current recession, this may ultimately reduce savings.<span style="mso-spacerun: yes;">  </span>If savings fall, it potentially reduces our ability to spend in the future? In Solow's Growth model, the biggest determinant of future growth in an economy is the savings rate. The current savings rate in the US is at an all time low and is much lower than that of other nations, such as Germany and Japan. So, how can policy makers increase savings in the US? Can the U.S. increase savings by lowering the capital gains tax rate, which changes the after tax return on capital investment? If so, how effective will President Bush's proposed capital gains tax cut be on altering our savings rate?<span style="mso-spacerun: yes;">  </span>To better understand after-tax interest rate effects on savings, one needs to discover the interest elasticity of savings to different types of taxes, such as capital gains and consumption taxes. If the private savings rate is sensitive to the after-tax interest rate return, then a capital gains tax, income tax, and sales tax will have very different effects on savings. The more sensitive the savings rate is to the after tax interest rate, the more an increase in the after tax interest rate will cause an increase in savings.<span style="mso-spacerun: yes;">  </span>To assess the current interest elasticity of consumption/savings on the after tax real return of interest, I build on Boskin's (1978) model with a simultaneous equations model and more recent data. Specifically, I estimate the log of consumption on wealth, unemployment, interest rates, and disposable private income, and the after tax return of the Moody's AAA Bond Rate. To avoid the potential endogeneity bias between these variables, I use a simultaneous equation approach. Results will help determine if the U.S. can increase its savings by lowering the tax rates on investment. In general, this paper finds that the elasticity of substitution is close to .2, which suggests that the elasticity of substitution is quite small and stable. As Feldstein suggests this shows that a reduction in an income or sales tax will generate greater savings than a reduction in the capital gains income tax. </span></p>

Voluntary Termination of Life Insurance Policies

In this paper, one error-correction model (ECM) that is able to avoid the problem of producing noise within traditional multiple cointegration vectors has been employed to explore the dynamics of surrender behavior. The evidence shows that both the emergency fund hypothesis and interest rate hypothesis are sustained in the short run as well as in the long run. A unique cointegration relationship within the surrender dynamics has been validated. In addition, a new hypothesis test that stresses the competition for the withdrawal of life insurance policy cash values has also been conducted. Such a crowding-out effect between policy loans and policy surrenders might be attributed to the motivation that keeps a life policy in force, the existence of surrender charges, and the automatic premium loan provision.

Sequential Impact of General Interest Rate Changes on the Interest Expense of Publicly Traded Restaurant Firms

It is generally known that restaurant firms are very capital intensive, largely due to the significant amount of capital expenditures required to expand. Therefore, restaurant firms are critically dependent upon borrowed capital for financing new locations. This would imply that interest expense is a significant expense for most restaurant firms. Therefore, any changes in the interest rates in the general economy have the potential of having a major impact on restaurant firm earnings, even without an accompanying change in the eating-out behavior of consumers. The interest of this study was in determining the sequential impact of the general interest rates (federal funds target rate) on the firm specific effective after-tax interest rate, which are, in turn, related to the cost of capital of the firm. Empirical evidence on this relationship is provided, along with magnitude and lag information of the relationship. Both short-term and long-term debt, as well as quarterly and annual data of firms, are used. Findings indicate that changes in the federal funds interest rate have small sequential impacts on the effective interest rates for restaurant firms. In other words, changes in federal rates affect the effective interest rates of restaurant firms in a decreasing fashion. The more recent the changes in federal rates, the less the impact on the effective interest rates.

Modeling Federal Funds rates: a comparison of four methodologies

Monthly Federal Fund interest rate values, set by the Federal Open Market Committee, have been the subject of much speculation prior to the announcement of their new values each period. In this study we use four competing methodologies to model and forecast the behavior of these short term Federal Fund interest rates. These methodologies are: time series, Taylor, econometric and neural network. The time series forecasts use only past values of Federal Funds rates. The celebrated Taylor rule methodology theorizes that the Federal Fund rate values are influenced solely by deviations from a desired level of inflation and from potential output. The econometric and neural network models have inputs used by both the time series and Taylor rule. Using monthly data from 1958 to the end of 2005 we distinguish between sample and out-of-sample sets to train, evaluate, and compare the models’ effectiveness. Our results indicate that the econometric modeling performs better than the other approaches when the data are divided into two sets of pre-Greenspan and Greenspan periods. However, when the data sample is divided into three groups of low, medium and high Federal Funds, the neural network approach does best.

Day-to-Day Monetary Policy and the Volatility of the Federal Funds Interest Rate

We propose a model of the interbank money market with an explicit role for central bank intervention and periodic reserve requirements, and study the interaction of profit-maximizing banks with a central bank targeting interest rates at high frequency. The model yields predictions on biweekly patterns of the federal funds rate`s volatility and on its response to changes in target rates and in intervention procedures, such as those implemented by the Federal Reserve in 1994. Theoretical results are consistent with empirical patterns of interest rate volatility in the U.S. market for federal funds.

Day-To-Day Monetary Policy and the Volatility of the Federal Funds Interest Rate

We propose a model of the interbank money market with an explicit role for central bank intervention and periodic reserve requirements, and study the interaction of profit-maximizing banks with a central bank targeting interest rates at high-frequency. The model yields predictions on biweekly patterns of the federal funds rate's volatility and on its response to changes in target rates and in intervention procedures, such as those implemented by the Fed in 1994. Theoretical results are consistent with empirical patterns of interest rate volatility in the U.S. market for federal funds.

Day-To-Day Monetary Policy and the Volatility of the Federal Funds Interest Rate

We propose a model of the interbank money market with an explicit role for central bank intervention and periodic reserve requirements, and study the interaction of profit-maximizing banks with a central bank targeting interest rates at high frequency. The model yields predictions on biweekly patterns of the federal funds rate’s volatility and on its response to changes in target rates and in intervention procedures, such as those implemented by the Federal Reserve in 1994. Theoretical results are consistent with empirical patterns of interest rate volatility in the U.S. market for federal funds.

Day-to-Day Monetary Policy and the Volatility of the Federal Funds Interest Rate

Day-to-Day Monetary Policy and the Volatility of the Federal Funds Interest Rate

Day-to-Day Monetary Policy and the Volatility of the Federal Funds Interest Rate

We propose a model of the interbank money market with an explicit role for central bank intervention and periodic reserve requirements, and study the interaction of profit-maximizing banks with a central bank targeting interest rates at high frequency. The model yields predictions on biweekly patterns of the federal funds rate’s volatility and on its response to changes in target rates and in intervention procedures, such as those implemented by the Federal Reserve in 1994. Theoretical results are consistent with empirical patterns of interest rate volatility in the U.S. market for federal funds.