|
on Econometric Time Series |
| By: | Abbate, Angela; Marcellino, Massimiliano |
| Abstract: | We explore whether modelling parameter time variation improves the point, interval and density forecasts of nine major exchange rates vis-a-vis the US dollar over the period 1976-2015. We fi nd that modelling parameter time variation is needed for an accurate calibration of forecast confi dence intervals, and is better suited at long horizons and in high-volatility periods. The biggest forecast improvements are obtained by modelling time variation in the volatilities of the innovations, rather than in the slope parameters. We do not find evidence that parameter time variation helps to unravel exchange rate predictability by macroeconomic fundamentals. However, an economic evaluation of the diff erent forecast models reveals that controlling for parameter time variation and macroeconomic fundamentals leads to higher portfolios returns, and to higher utility values for investors. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:11559&r=ets |
| By: | Kapetanios, George; Marcellino, Massimiliano; Venditti, Fabrizio |
| Abstract: | In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs (FAVAR). When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and large (parametric) Bayesian VARs with time-varying parameters. The tool can also be used for structural analysis. As an example, we study the time-varying effects of oil price innovations on sectoral U.S. industrial output. We find that the changing interaction between unexpected oil price increases and business cycle fluctuations is shaped by the durable materials sector, rather by the automotive sector on which a large part of the literature has typically focused. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:11560&r=ets |
| By: | David E. Allen (University of Sidney, University of South Australia, Australia); Michael McAleer (National Tsing Hua University Taiwan; Erasmus School of Economics Erasmus University Rotterdam, The Netherlands; Yokohama National University, Japan); Robert Powell (Edith Cowan University, Perth, Australia); Abhay K. Singh (Edith Cowan University, Perth, Australia) |
| Abstract: | This paper applies two measures to assess spillovers across markets: the Diebold Yilmaz (2012) Spillover Index and the Hafner and Herwartz (2006) analysis of multivariate GARCH models using volatility impulse response analysis. We use two sets of data, daily realized volatility estimates taken from the Oxford Man RV library, running from the beginning of 2000 to October 2016, for the S&P500 and the FTSE, plus ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index, from 3 January 2005 to 31 January 2015. Both data sets capture both the Global Financial Crisis (GFC) and the subsequent European Sovereign Debt Crisis (ESDC). The spillover index captures the transmission of volatility to and from markets, plus net spillovers. The key difference between the measures is that the spillover index captures an average of spillovers over a period, whilst volatility impulse responses (VIRF) have to be calibrated to conditional volatility estimated at a particular point in time. The VIRF provide information about the impact of independent shocks on volatility. In the latter analysis, we explore the impact of three different shocks, the onset of the GFC, which we date as 9 August 2007 (GFC1). It took a year for the financial crisis to come to a head, but it did so on 15 September 2008, (GFC2). The third shock is 9 May 2010. Our modelling includes leverage and asymmetric effects undertaken in the context of a multivariate GARCH model, which are then analysed using both BEKK and diagonal BEKK (DBEKK) models. A key result is that the impact of negative shocks is larger, in terms of the effects on variances and covariances, but shorter in duration, in this case a difference between three and six months |
| Keywords: | Spillover Index; Volatility Impulse Response Functions (VIRF); BEKK; DBEKK; Asymmetry; GFC; ESDC |
| JEL: | C22 C32 C58 G32 |
| Date: | 2016–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20160084&r=ets |
| By: | Todd Prono |
| Abstract: | Covariances between contemporaneous squared values and lagged levels form the basis for closed-form instrumental variables estimators of ARCH processes. These simple estimators rely on asymmetry for identification (either in the model's rescaled errors or the conditional variance function) and apply to threshold ARCH (l) and ARCH (p) with p |
| Keywords: | ARCH ; Closed form estimation ; Heavy tails ; Instrumental variables ; Regular variation ; Three-step estimation |
| JEL: | C13 C22 C58 |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2016-83&r=ets |
| By: | Mikio Ito; Akihiko Noda; Tatsuma Wada |
| Abstract: | A time-varying cointegration model for foreign exchange rates is presented. Unlike previous studies, we allow the loading matrix in the vector error correction (VEC) model to be varying over time. Because the loading matrix in the VEC model is associated with the speed at which deviations from the long-run relationship disappear, we propose a new degree of market comovement\ based on the time-varying loading matrix to measure the strength or robustness of the long-run relationship over time. Since exchange rates are determined by macrovariables, cointegration among exchange rates implies those macroeconomic variables share common stochastic trends. Therefore, the proposed degree measures the degree of market comovement. Our main finding is that the market comovement has become stronger over the past quarter century, but the rate at which market comovement strengthens is decreasing with two major turning points: one in 1995 and the other one in 2008. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1610.04334&r=ets |
| By: | Marcello Rambaldi; Vladimir Filimonov; Fabrizio Lillo |
| Abstract: | Given a stationary point process, an intensity burst is defined as a short time period during which the number of counts is larger than the typical count rate. It might signal a local non-stationarity or the presence of an external perturbation to the system. In this paper we propose a novel procedure for the detection of intensity bursts within the Hawkes process framework. By using a model selection scheme we show that our procedure can be used to detect intensity bursts when both their occurrence time and their total number is unknown. Moreover, the initial time of the burst can be determined with a precision given by the typical inter-event time. We apply our methodology to the mid-price change in FX markets showing that these bursts are frequent and that only a relatively small fraction is associated to news arrival. We show lead-lag relations in intensity burst occurrence across different FX rates and we discuss their relation with price jumps. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1610.05383&r=ets |