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

The PPP hypothesis revisited: Evidence using a multivariate long-memory model

This paper examines the PPP hypothesis analysing the behaviour of the real exchange rates vis-à-vis the US dollar for four major currencies (namely, the Canadian dollar, the euro, the Japanese yen and the British pound). An innovative approach based on fractional integration in a multivariate context is applied to annual data from 1970 to 2011. Long memory is found to characterise the Canadian dollar, the British pound and the euro, but in all four cases the results are consistent with the relative version of PPP

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

International portfolio flows and exchange rate volatility in emerging Asian markets

This paper investigates the effects of equity and bond portfolio inflows on exchange rate volatility using monthly bilateral data for the US vis-a-vis seven Asian developing and emerging countries (India, Indonesia, Pakistan, the Philippines, South Korea, Taiwan and Thailand) over the period 1993:01-2015:11. GARCH models and Markov switching specifications with time-varying transition probabilities are estimated in addition to a benchmark linear model. The evidence suggests that high (low) exchange rate volatility is associated with equity (bond) inflows from the Asian countries toward the US in all cases, with the exception of the Philippines. Therefore, capital controls could be an effective tool to stabilise the foreign exchange market in countries where flows affect exchange rate volatility

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

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

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