This paper offers a new approach that estimates the response of interest rates to inflation and the output gap at various points (quantiles) on the conditional distribution of interest rates. This offers an improvement on empirical estimates conducted only at the mean and also allows us to test the propositions that policy shows greater aggression to inflation in the reaction function in terms of a greater response coefficient as interest rates reach low levels, and increasing aggression as the lower bound is approached. We find support for the Taylor principle, a more aggressive response to inflation than under a Taylor rule, but no detectable evidence of increasing aggression as the zero lower bound is approached in the US and Japan. Copyright (c) 2009 The Ohio State University.

We construct the complete network of tail risk spillovers among major cryptocurrencies using the Least Absolute Shrinkage and Selection Operator (LASSO) quantile regression. We capture important features of the network, including major risk-driving and major risk-receiving currencies, and the evolution of the tail dependence among the currencies over time. Importantly, we reveal a striking finding that the right tail dependence among the cryptocurrencies is significantly stronger than the left tail counterpart. This unique characteristic may have contributed to the rise in popularity of cryptocurrencies over the last few years. Our portfolio analysis reveals that diversification in cryptocurrency investment can be accomplished simply by employing the naïve equal-weighted scheme even when transaction costs are taken into account.

We examine the persistence of returns on Bitcoin at different parts on the return distributions through the use of the quantile autoregressive (QAR) models. We find lower quantiles of the daily return distribution and upper quantiles of the weekly return distribution to exhibit positive dependence with past returns. The evidence points to overreaction in the Bitcoin market: investors overreact during days of sharp declines in the Bitcoin price and during weeks of market rallies.

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Making accurate forecasts of the future direction of interest rates is a vital element when making economic decisions. The focus on central banks as they make decisions about the future direction of interest rates requires the forecaster to assess the likely outcome of committee decisions based on new information since the previous meeting. We characterize this process as a dynamic ordered probit process that uses information to decide between three possible outcomes for interest rates: an increase, decrease or no change. When we analyse the predictive ability of two information sets, we find that the approach has predictive ability both in-sample and out-of-sample that helps forecast the direction of future rates. Copyright © 2008 John wiley & Sons, Ltd.