Is exchange rate – customer order flow relationship linear? Evidence from the Hungarian FX market

over the last decade, the microstructure approach to exchange rates has become very popular. The underlying idea of this approach is that the order flows at different levels of aggregation contain valuable information to explain exchange rate movements. The bulk of empirical literature has focused on evaluating this hypothesis in a linear framework. This paper analyzes nonlinearities in the relation between exchange rates and customer order flows. We show that the relationship evolves over time and that it is different under different market conditions defined by exchange rate volatility. Further, we found that the nonlinearity can be captured successfully by the Threshold regression and Markov Switching models, which provide substantial explanatory power beyond the constant coefficients approach.customer order flows, nonlinear models, microstructure, exchange rate

Term Structure Persistence

Stationary I(0) models employed in yield curve analysis typically imply an unrealistically low degree of volatility in long-run short-rate expectations due to fast mean reversion. In this paper we propose a novel multivariate affine term structure model with a two-fold source of persistence in the yield curve: Long-memory and short-memory. Our model, based on an I(d) specification, nests the I(0) and I(1) models as special cases and the I(0) model is decisively rejected by the data. Our model estimates imply both mean reversion in yields and quite volatile long-distance short-rate expectations, due to the higher persistence imparted by the long-memory component. Our implied term premium estimates differ from those of the I(0) model during some relevant periods by more than 4 percentage points and exhibit a realistic countercyclical pattern

Can we use sesonally adjusted indicators in dynamic factor models?

We examine the short-term performance of two alternative approaches to forecasting using dynamic factor models. The fi rst approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the nonseasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show that the common practice of using seasonally adjusted data in this type of model is very accurate in terms of forecasting ability. Drawing on fi ve coincident indicators, we illustrate this result for US dataEn el trabajo se examina el comportamiento en predicción de dos aproximaciones alternativas a los modelos de factores dinámicos. La primera aproximación extrae el componente estacional de los indicadores individuales antes de estimar el modelo de factores dinámicos. La segunda utiliza series no ajustadas de estacionalidad en un modelo de factores que endógenamente realiza el ajuste estacional. Nuestro análisis de Montecarlo demuestra que el comportamiento de la primera aproximación es siempre igual o mejor que el de la segunda, sea cual sea el escenario simulado. Nuestros resultados tienen implicaciones muy relevantes sobre la literatura de modelos factoriales porque muestran que la práctica común de usar datos ajustados de estacionalidad en estos modelos es la apropiada y genera los mejores resultados en predicción. Usando cinco indicadores, ilustramos este resultado para el caso de Estados Unido

The Variance-Frequency Decomposition as an Instrument for VAR Identification: an Application to Technology Shocks

Abstract: This paper proposes a new framework to study identification in structural VAR models. The framework is based on the variance-frequency decomposition and focuses on the contribution of the identified shock to the variance of model variables in a given frequency range. We use the hours-productivity debate as a connecting thread in our discussion since the identification problem has attracted a lot of attention in this literature. To start, we employ the framework to study the business cycle properties of a set of different identification schemes for technology shocks. Grounded on the simulation results, we propose a new model-based procedure which delivers a precise estimate of the response of hours. Finally, we put all the schemes to work with real data, obtaining substantial evidence in favor of plausible RBC parametrizations, especially from identification restrictions that perform better in simulations. This analysis also reveals that the schemes that recover a very strong response of hours (higher than the implied by typical RBC parameterizations) tend to overstate the contribution of the technology shock to the fluctuations of hours worked at business cycle frequencies. Keywords: Business cycle, frequency domain, hours worked, productivity, vector autoregressions. Classification: C1, E

Structural shocks and dinamic elasticities in a long memory model of the US gasoline retail market

A structural multivariate long memory model of the US gasoline market is employed to disentangle structural shocks and to estimate the own-price elasticity of gasoline demand. Our main empirical findings are: 1) there is strong evidence of nonstationarity and mean-reversion in the real price of gasoline and in gasoline consumption; 2) accounting for the degree of persistence present in the data is essential to assess the responses of these two variables to structural shocks; 3) the contributions of the different supply and demand shocks to fluctuations in the gasoline market vary across frequency ranges; and 4) long memory makes available an interesting range of convergent possibilities for gasoline demand elasticities. Our estimates suggest that after a change in prices, consumers undertake a few measures to reduce consumption in the short- and medium-run but are reluctant to implement major changes in their consumption habits. Keywords: fractional integration, gasoline demand, price elasticity, structural model Classification: Q41, Q43, C3

Volatility Spillovers in a Long-Memory VAR: an Application to Energy Futures Returns

In this paper, we assess volatility spillovers across energy markets accounting for the persistence of the volatility series. To do so, we compute Diebold and Yilmaz (2015) measures of connectedness based on the forecast-error variance decomposition of an estimated fractionally integrated VAR (FIVAR). We use this method to study volatility spills among oil, unleaded gasoline, heating oil, and natural gas. Our main empirical findings are: 1) Accounting for persistence is essential to assess the magnitude of the spillover effects in these markets; 2) The traditional VAR magnifies the other’s contribution to the volatility variance; 3) There are substantial spillover effects across petroleum markets, but the link between these markets and the natural gas market appears to be broken in post 2008-crisis data. Keywords: fractional integration, spillovers, energy commodities. JEL Classification: G1, C5, Q

Hours worked - Productivity puzzle: identification in fractional integration settings

A recent finding of the structural VAR literature is that the response of hours worked to a technology shock depends on the assumption on the order of integration of the hours. In this work we relax this assumption, allowing for fractional integration and long memory in the process for hours and productivity. We find that the sign and magnitude of the estimated impulse responses of hours to a positive technology shock depend crucially on the assumptions applied to identify them. Responses estimated with short-run identification are positive and statistically significant in all datasets analyzed. Long-run identification results in negative often not statistically significant responses. We check validity of these assumptions with the Sims (1989) procedure, concluding that both types of assumptions are appropriate to recover the impulse responses of hours in a fractionally integrated VAR. However, the application of longrun identification results in a substantial increase of the sampling uncertainty. JEL Classification numbers: C22, E32. Keywords: technology shock, fractional integration, hours worked, structural VAR, identificatio

A fractionally integrated approach to monetary policy and inflation dynamics

This paper relaxes the standard I(0) and I(1) assumptions typically stated in the monetary VAR literature by considering a richer framework that encompasses the previous two processes as well as other fractionally integrated possibilities. First, a timevarying multivariate spectrum is estimated for post WWII US data. Then, a structural fractionally integrated VAR (VARFIMA) is fitted to each of the resulting time dependent spectra. In this way, both the coefficients of the VAR and the innovation variances are allowed to evolve freely. The model is employed to analyze inflation persistence and to evaluate the stance of US monetary policy. Our findings indicate a strong decline in the innovation variances during the great disinflation, consistent with the view that the good performance of the economy during the 80’s and 90’s is in part a tale of good luck. However, we also find evidence of a decline in inflation persistence together with a stronger monetary response to inflation during the same period. This last result suggests that the Fed may still play a role in accounting for the observed differences in the US inflation history. Finally, we conclude that previous evidence against drifting coefficients could be an artifact of parameter restriction towards the stationary region. Keywords: monetary policy, inflation persistence, fractional integration, timevarying coefficients, VARFIMA. JEL Classification: E52, C3