Bank Revolving Credit as a Channel of Monetary Policy Targeting Interest Rates

This paper investigates the implication of bank revolving credit in the form of credit card loans as a channel of monetary policy targeting the federal funds rate since 1980. Credit cards have become increasingly popular and a necessity for many transactions and purchases in the United States. The revolving credit nature of credit card loans makes them an instant tool for consumer loans that can facilitate consumption. Using instrumental variable and two-stage least squares (2SLS) methodology, we analyze the implication of credit card loans to modern monetary policy that targets interest rates.

Macro-Financial Parameters Influencing Bitcoin Prices: Evidence from Symmetric and Asymmetric ARDL Models

Bitcoins are evolving as a modern class of investment assets and it is crucial for investors to manage their investment risk. This paper examines the impact of macroeconomic-financial indicators on Bitcoin price using symmetric and asymmetric version of autoregressive distributed lag (ARDL) models with structural breaks. The asymmetric long-run association ascertained between Bitcoin prices and the macroeconomic-financial indicators is evident. Our empirical results indicate that the Bitcoin cannot be used to hedge against the inflation, Federal funds rate, stock markets and commodity markets. We further find that Bitcoin can be regarded as a hedging device for the oil prices. Our findings have significant implications for market participants who consider including alternate investment assets in their portfolios.

Transition from the Taylor rule to the zero lower bound

This paper examines the Taylor rule in the context of United States monetary policy since 1965, particularly with respect to the zero-lower-bound era of the federal funds rate from 2009 to 2016. A nonlinear Taylor rule is developed which features smooth transitions in the first two moments of the federal funds rate. This flexible specification is found to usefully capture observed nonlinearity, while accounting for the well-documented structural changes in monetary policy formation at the Federal Reserve in the last 50 years, and especially in the recent zero-lower-bound era.

A Structural Measure of the Shadow Federal Funds Rate

We propose a shadow policy interest rate based on an estimated structural model that accounts for the zero lower bound. The lower bound constraint, if expected to bind, is contractionary and increases the shadow rate compared to an unconstrained systematic policy response. By contrast, forward guidance and other unconventional policies that extend the expected duration of zero-interest-rate policy are expansionary and decrease the shadow rate. By quantifying these distinct effects, our structural shadow federal funds rate better captures the stance of monetary policy given economic conditions than a shadow rate based only on the term structure of interest rates.

Dynamic Impact of Unconventional Monetary Policy on International REITs

In this paper, we estimate the dynamic impact of unconventional monetary policy in the US on international REITs. Unlike existing studies which are limited to conventional policy tools and undertake a static approach, we use an event study approach and estimate a time-varying parameter model to investigate the dynamic impact of forward guidance (FG) and large-scale asset purchases (LSAP) shocks on the international REIT returns. We also compare the effects of these unconventional tools with the effects of conventional federal funds rate (FFR) shocks. The results show that the response of international REITs to unconventional policy shocks depends on the time under consideration. FG shocks have greater time-variation in the impact on REIT returns compared to LSAP shocks, particularly with Australia, Belgium, and the US REIT markets. Furthermore, FG shocks broadly have a negative impact on REITs while the results for LSAP effects are mixed. We also find that in most countries, REITs time-varying response of FG shocks is related to changes in gold prices and financial conditions.

Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

Use of the Federal Reserve's repo operations and changes in dealer balance sheets

Before the 2008 financial crisis, the Federal Reserve (Fed) regularly conducted repurchase agreements (repos) in a fairly modest size with primary dealers to adjust the supply of reserves in the banking system and to keep the federal funds rate at the target set by the FOMC. During the economic downturn that followed the financial crisis, the Fed engaged in large scale asset purchases in order to provide additional monetary accommodation, and those purchases significantly increased the supply of reserves and eliminated the need for the Fed to engage in repo operations to increase reserves in the system.

A mixture autoregressive model based on Gaussian and Student’s t-distributions

We introduce a new mixture autoregressive model which combines Gaussian and Student’s t mixture components. The model has very attractive properties analogous to the Gaussian and Student’s t mixture autoregressive models, but it is more flexible as it enables to model series which consist of both conditionally homoscedastic Gaussian regimes and conditionally heteroscedastic Student’s t regimes. The usefulness of our model is demonstrated in an empirical application to the monthly U.S. interest rate spread between the 3-month Treasury bill rate and the effective federal funds rate.

Gross Capital Inflows in Indonesia: Exploring Bonanzas and Sudden Stops

This paper examines episodes of capital bonanzas and sudden stops in Indonesia by utilising binary response models and several episode-identification approaches. Our identification suggests that whenever bonanza episodes occurred, capital sudden stop episodes followed in a more extended period. The estimations demonstrate that domestic factors are relatively dominant in determining the capital bonanzas, and the federal funds rate has a more significant impact on inducing the probability of capital sudden stops in Indonesia. We also found that Turkey and South Africa are the most contagious economies for Indonesia. This paper proposes some policy reforms to enhance the stability of capital inflows in Indonesia, including financial regulation and public finance policies such as a reverse Tobin tax and market-driven public debt rules.

Are Rising U.S. Interest Rates Destabilizing for Emerging Market Economies?

Rising U.S. interest rates are often thought to be bad news for emerging market economies (EMEs) as they increase debt burdens, trigger capital outflows, and generally cause a tightening of financial conditions that can lead to financial crises. Indeed, as shown in Figure 1 below, the rise in the federal funds rate (the black line) during the Volcker disinflation of the early 1980s was associated with a sharp rise in the incidence of financial crises in EMEs (the green bars).