Exchange rate volatility,macroeconomic announcements and the choice of intraday periodicity filtering method

Filtering out the intraday periodicity of volatility is crucial for using high frequency data in econometric analysis. This paper studies the effects of filtering on statistical inference as regards the impact of news on exchange rate volatility. The properties of different methods are studied using a five-minute frequency EUR/USD data set and simulated returns. The simulation results suggest that all the methods tend to produce downward-biased estimates of news coefficients, some more biased than others. The study supports the Flexible Fourier Form method as the best for seasonality filtering.     

Sectoral trends and cycles in Germany

We examine the comovements between the output indexes of three German sectors (manufacturing, mining, and agriculture) and the three corresponding sectoral stock market indexes. It is found that data with and without seasonal adjustment give mixed results on the long-run interaction between the sectoral indexes. Compared with data that are non-seasonally adjusted, the adjusted data offer weaker evidence on the cointegration relationship between a) the sectoral output indexes, b) sectoral stock indexes, and c) individual pairs of real and financial indexes. On short-run comovement, seasonally adjusted data offer stronger evidence on the presence of common synchronized and non-synchronized cyclical components. First version received: March 2000/Final version received: September 2001 RID="*" ID="*"  We would like to thank Michael Dooley, Juergen von Hagen, Kenneth Kletzer, Peter Kugler, Jacky So and two anonymous referees, as well as the participants of the Fifth Global Finance Conference in Mexico City, the seminars at the University of California at Santa Cruz, University of Munich, and University of Basel for their helpful comments and suggestions. This research was supported by CGES at UC Berkeley and UC Santa Cruz faculty research funds.     

Seasonal concentration of the hotel demand in Costa del Sol: A decomposition by nationalities

Costa del Sol is a mature Mediterranean destination in the South of Spain with a degree of seasonality that has shown little variation during the last 20 years. In this paper, we apply the decomposition of the Gini index of seasonal concentration by nationalities. The main outcome of this decomposition is the estimation of relative contributions to the overall seasonal concentration and marginal effects by nationalities. These measures can be of great help in the design of counter-seasonal policies. Our results suggest that planners interested in reducing seasonality may concentrate their efforts in consolidating markets with negative marginal effect over the index of concentration, like the British and Nordic ones. In addition, marketing targets may be focused primarily in the winter season for the markets with positive relative marginal effect.     

Changes in seasonal patterns : Are they cyclical?

This paper explores the hypothesis that the seasonal patterns of macroeconomic variables vary with expansions and contractions. Graphical techniques and generalized predictive tests for structural stability are used to identify and test patterns of changing seasonality. A Monte Carlo exercise shows the power of the tests against interesting alternatives. The empirical results suggest that seasonal patterns are unstable and that in many cases changes are linked to the stages of the business cycle. The forecasting costs incurred by treating seasonality as constant are discussed and evaluated.     

Forecasting the NN5 time series with hybrid models

We propose a simple way of predicting time series with recurring seasonal periods. Missing values of the time series are estimated and interpolated in a preprocessing step. We combine several forecasting methods by taking the weighted mean of forecasts that were generated with time-domain models which were validated on left-out parts of the time series. The hybrid model is a combination of a neural network ensemble, an ensemble of nearest trajectory models and a model for the 7-day cycle. We apply this approach to the NN5 time series competition data set.     

A SHARP model of bid–ask spread forecasts

This paper proposes an accurate, parsimonious and fast-to-estimate forecasting model for integer-valued time series with long memory and seasonality. The modelling is achieved through an autoregressive Poisson process with a predictable stochastic intensity that is determined by two factors: a seasonal intraday pattern and a heterogeneous autoregressive component. We call the model SHARP, which is an acronym for seasonal heterogeneous autoregressive Poisson. We also present a mixed-data sampling extension of the model, which adopts the historical information flow more efficiently and provides the best (among all the models considered) forecasting performances, empirically, for the bid–ask spreads of NYSE equity stocks. We conclude by showing how bid–ask spread forecasts based on the SHARP model can be exploited in order to reduce the total cost incurred by a trader who is willing to buy or sell a given amount of an equity stock.     

Deterministic seasonality in Dickey–Fuller tests: should we care?

This paper investigates the properties of Dickey–Fuller tests for seasonally unadjusted quarterly data when deterministic seasonality is present but it is neglected in the test regression. While for the random walk case the answer is straightforward, an extensive Monte Carlo study has to be performed for more realistic processes and testing strategies. The most important conclusion is that the common perception that deterministic seasonality has nothing to do with testing for the long-run properties of the data is incorrect. Further numerical evidence on the shortcomings of the general-to-specific t-sig lag selection method is also presented.

Seasonality of hotel demand in the main Spanish provinces: Measurements and decomposition exercises

This paper analyses the seasonal concentration of tourist activity in the main Spanish provinces for the period 1999–2012, taking hotel nights as the indicator of reference. We propose using several standard summary measures in order to evaluate the level, evolution and some decompositions. Our main results can be summarized as follows: first, across the whole country and especially since 2007, there is a growth in seasonality; second, seasonal concentration is greatest in the Balearic Islands and two of the Catalan provinces, and least in Madrid and the Canary Island provinces; third, although the overall patterns typically agree, nevertheless, in some provinces the indexes we deal with show some discrepancies; fourth, the decomposition of the monthly concentration by major markets typically indicates the main role played by the foreign component; finally, the overall evidence does not support the thesis that the domestic market offsets the foreign one.     

Seasonality in tourist flows: Decomposing and testing changes in seasonal concentration

This paper shows that changes in timing and magnitude of seasonality in tourist flows can be measured by decomposing the change in the Gini index, a widely used index of seasonal concentration, into two components. One of them tracks the change in the pattern of seasonal fluctuations and the second captures the change in their magnitude. To assess whether changes in the seasonal pattern and magnitude are significant, a technique for testing hypotheses concerning the two components is developed. We examine changes in the seasonal concentration of tourist arrivals in six tourist destinations in the Veneto Region, one of the most tourism-oriented regions in Europe, from 2006 to 2016. The magnitude of seasonality significantly decreased in some destinations with a diversified tourism product, such as Euganean Spas and Lake Garda. The seasonal pattern remained substantially stable for all destinations except Venice, where a non-negligible shift in the seasonal pattern occurred.