Globalization of the Economy - Europa

Selected Readings –November 2010 1

SELECTED READINGS

Focus on: Calendar effects

November 2010


INDEX

INTRODUCTION............................................................................................................. 5

1 WORKING PAPERS AND ARTICLES ................................................................ 7

1.1 Paulo Soares Esteves and Paulo M.M. Rodrigues, 2010. “Calendar Effects in Daily ATM Withdrawals”,Banco de Portugal, Economics and Research Department Working Papers No. 12/ 2010. 7

1.2 Evans Kevin P. and Speight Alan E.H., 2010. “Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility”, Research in International Business and Finance, Volume 24, Issue 1, Pages 82-101...........................................................................................7

1.3 Stefan Benjamin Grimbacher, Laurens A. P. Swinkels and Pim Van Vliet, 2010. “An Anatomy of Calendar Effects”, SSRN Working Paper.......................................................................8

1.4 Charles Amélie, 2010. “The day-of-the-week effects on the volatility: The role of the asymmetry”,European Journal of Operational Research,Volume 202, Issue 1, 1 April 2010, Pages 143-152.....................................................................................................................................................8

1.5 Gerhard Thury, 2010. “Calendar Effects in Austrian Industrial Production”,WIFO Working Papers with number 65/1994. ................................................................................................9

1.6 Mokhtar Darmoul and Mokhtar Kouki, 2009. “Calendar effect and intraday volatility patterns of euro-dollar exchange rate : new evidence of Europe lunch period”, HAL, Université Paris1 Panthéon-Sorbonne, Document de Travail du Centre d'Economie de la Sorbonne - 2009.70. ....................................................................................................................................................9

1.7 Maria Rosa Borges, 2009. “Calendar Effects in Stock Markets: Critique of Previous Methodologies and Recent Evidence in European Countries”, Department of Economics at the School of Economics and Management (ISEG), Technical University of Lisbon Working Papers No. 2009/37. .............................................................................................................................................9

1.8 Eleftherios Giovanis, 2009. “Calendar Effects in Fifty-five Stock Market Indices”, Global Journal of Finance and Management, ISSN 0975 - 6477 Volume 1, Number 2 (2009), pp. 75-98. 10

1.9 Fecht, Falko, Nyborg Kjell G. and Rocholl, Jörg, 2008. “Liquidity management and overnight rate calendar effects: Evidence from German banks”,The North American Journal of Economics and Finance, Volume 19, Issue 1, March 2008, Pages 7-21............................................11

1.10 Ushad Subadar Agathee, 2008. “Calendar Effects and the Months of the Year: Evidence from the Mauritian Stock Exchange”, International Research Journal of Finance and Economics, ISSN 1450-2887, Issue 14 (2008)..........................................................................................................11

1.11 Brian C. Monsell, 2007. “Issues in Modeling and Adjusting Calendar Effects in Economic Time Series”..........................................................................................................................................12

1.12 Bing Zhang and Xindan Li, 2006. “Do Calendar Effects Still Exist in the Chinese Stock Markets?”,Journal of Chinese Economic and Business Studies, Volume 4, Issue 2 July 2006 , pages 151 – 163. ....................................................................................................................................12

1.13 Catherine Kyrtsou, Alexandros Leontitsis and Costas Siriopoulos, 2006. “Exploring The Impact Of Calendar Effects On The Dynamic Structure And Forecasts Of Financial Time Series”,International Journal of Theoretical and Applied Finance (IJTAF), Volume: 9, Issue: 1(2006) pp. 1-22.....................................................................................................................................13


1.14 Alexandros Leontitsis and Costas Siriopoulos, 2006. “Nonlinear forecast of financial time series through dynamical calendar correction”, Applied Financial Economics Letters, Volume 2, Issue 5 September 2006 , pages 337 – 340...........................................................................................13

1.15 Veera Lenkkeri , Wessel Marquering and Ben Strunkmann-Meister, 2006. “The Friday Effect in European Securitized Real Estate Index Returns”,The Journal of Real Estate Finance and Economics,Volume 33, Number 1, 31-50.....................................................................................14

1.16 Peter Reinhard Hansen, Asger Lunde and James M. Nason, 2005. “Testing the significance of calendar effects”, Federal Reserve Bank of Atlanta Working Paper No. 2005-02. ....................14

1.17 Yucel Eray M., 2005. “Does Ramadan Have Any Effect on Food Prices: A Dual-Calendar Perspective on the Turkish Data”,University Library of Munich, Germany in MPRA Paper No. 1141. 15

1.18 Dimitar Tonchev and Tae-Hwan Kim, 2004, “Calendar effects in Eastern European financial markets: evidence from the Czech Republic, Slovakia and Slovenia”, Applied Financial Economics, Volume 14, Issue 14 October 2004 , pages 1035 – 1043.................................................15

1.19 Bell W. R., Martin D. E. K., 2004. “Modeling Time-Varying Trading-Day Effects in Monthly Time Series”, ASA Proceedings of the Joint Statistical Meetings.....................................16

1.20 Peter Reinhard Hansen and Asger Lunde, 2003. “Testing the Significance of Calendar Effects”, Brown University, Department of Economics Working Papers No. 2003-03. .................16

1.21 Miguel Balbina and Nuno C. Martins, 2002. “The Analysis of Calendar Effects on the Daily Returns of the Portuguese Stock Market: the Weekend and Public Holiday Effects”, Banco de Portugal, Economics and Research Department, Economic Bulletin, December 2002. .................16

1.22 Thomas Hellström, 2002. “Trends and Calendar Effects in Stock Returns”. .......................17

1.23 Godfrey Gregory A. and Powell Warren B., 2000. “Adaptive estimation of daily demands with complex calendar effects for freight transportation”,Elsevier Journal Transportation Research Part B: Methodological, Volume 34, Issue 6, August 2000, Pages 451-469.....................17

1.24 Sullivan Ryan, Timmermann Allan and White Halbert, 1998. “Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns”, Department of Economics, UC San Diego University of California at San Diego, Discussion Paper 98-16.. ...........................................18

1.25 Maria Helena Nunes, 1997. “The impact of precipitation and calendar effects on cement sales”, Banco de Portugal, Economics and Research Department, Economic Bulletin, March 1997. 18

1.26 Stephanie Cano, Patricia Getz, Jurgen Kropf, Stuart Scott and George Stamas,1996. “Adjusting for a calendar effect in employment time series”, Bureau of Labor Statistics, Proceedings paper No. 112...................................................................................................................19

1.27 Bell W. R., 1995. “Correction to « Seasonal Decomposition of Deterministic Effects »”, (n° RR84/01), Research Report, Statistical Research Division, U.S. Bureau of the Census, Washington D.C., RR95/01..................................................................................................................19

1.28 Gerhard Thury and Michael Wüger, 1992. “Adjustment of Economic Time Series for Outliers, Seasonal, and Calendar Effects”, WIFO-Monatsberichte , 9/1992, pp. 488-495]. ..........19

1.29 Johnston Elizabeth Tashijan, Kracaw William A. and McConnell John J., 1991. “Day-of-the-Week Effects in Financial Futures: An Analysis of GNMA, T-Bond, T-Note, and T-Bill Contracts”, Journal of Financial and Quantitative Analysis (1991), 26: 23-44 Cambridge. .........20


1.30 Gerhard Thury, 1989. “Calendar Effects in Industrial Production Time Series”, WIFO-Monatsberichte, 8/1989, pp. 521-526...................................................................................................20

1.31 Pauly R. and Schell A., 1989. “Calendar Effects in Structural Time Series Models with Trend and Season”, Empirical Economics,Volume 14, Number 3, 241-256. ..................................21

1.32 Thury G. ,1986. “The Consequences of Trading Day Variation and Calendar Effects for ARIMA Model Building and Seasonal Adjustment”, Empirica, Vol. 13-1, pp 3-25.......................21

1.33 Bell W. R. and Hillmer S. C., 1983. “Modeling Time series with Calendar Variation”, Journal of the American Statistical Association, Vol. 78, n°383, pp 526-534. .................................21

1.34 Cleveland W. S., 1983. “Seasonal and Calendar Adjustment”, Handbook of Statistics Vol. 3 : Time Series in the Frequency Domain, Brillinger D. R. and Krishnaiah P. R. (eds), North-Holland, pp 39-72. ................................................................................................................................21

1.35 Cleveland W. S., Devlin S. J., 1982. “Calendar effects in monthly time series: Modeling and adjustment”, Journal of the American Statistical Association, Vol. 77, n°379, pp 520-528...........22

1.36 Cleveland W.P. and M.R. Grupe, 1981. “Modeling time series when calendar effects are present”, Board of Governors of the Federal Reserve System (U.S.) Special Studies Papers No. 162. 22

1.37 Cleveland W. S., Devlin S. J., 1980. “Calendar effects in monthly time series: Detection by spectrum analysis and graphical methods”, Journal of the American Statistical Association, Vol. 75, n°371, pp 487-496. ..........................................................................................................................22

1.38 Ken Holden, John Thompson and Yuphin Ruangrit. “The Asian Crisis and Calendar Effects on Stock Returns in Thailand”. ..............................................................................................22


INTRODUCTION The main objective of any macroeconomic or financial modeling is ultimately to

predict the direction and level of change in a given series. However, macroeconomic

and financial series are quantitative measures of given behavior of underlying agents,

(consumers, businesses, traders, etc.). Given that the behavior of economic agents is

significantly affected by the calendar, it is obvious that the calendar can have effects

on economic and financial time series. The most common and most widely studied of

calendar effects is seasonality in time-series, which has been a widely researched and

studied subject. “Calendar effects” that are the subject of these selected readings are

second-order effects that are left in the data, once the seasonal adjustment has been

carried out.

In modeling economic and financial time series, the first order of business is to

identify the truly stochastic part of the series, that is the part that cannot be predicted

by a trend, seasonality, or another regular occurrence. Certain variations in the series

occur regularly, and as such can be modeled before submitting the series to a deeper

analysis of the irregular components. The chief example is seasonal component of a

time-series. Retail sales will rise toward the end of the year, and fall in the beginning

of the next, as the Christmas tradition of gift-giving stimulates retail sales in

December, and moderates them in January (when consumers are “recovering” from

Christmas). Such behavior is clearly tied to the calendar, and as such can be modeled

by including regressors that allow for adjustment of series for such seasonal variation.

This is the crux of seasonal adjustment, which has been extensively studied in the

past, and remains a lively topic of research today.

However, once the data is seasonally adjusted, there still remain effects tied to the

calendar, such working-day effects, holiday effects (such as Halloween and Easter

effect), etc. Obviously, in addition to the seasons of the year, agents’ behavior is

affected by other calendar considerations. There is generally, and almost universally,

lowered economic activity on the weekends. Hence any monthly time series will be

influenced by the number of weekends that fall in a given month. Easter is another

interesting effect for countries that celebrate it, as the date of Easter is not fixed,

hence it is not as straight-forward to model as some other holidays. There are other


well-known and documented calendar effects that are presented and studied in the

papers below. These effects are a subject of interest to both economic and finance

researchers, and have been studied at some length. Also, both of the pre-eminent

seasonal adjustment software packages TRAMO-SEATS and X-12 ARIMA have

built-in capacity to adjust for different calendar effects.

What follows is a non-exhaustive collection of scholarly articles that describe

calendar effects, gauge their magnitude, and provide solutions for dealing with them

in time series analysis.

Contact point: GianLuigi Mazzi, "Responsible for Euro-indicators and statistical

methodology", Estat - D5 "Key Indicators for European Policies"

[email protected].

mailto:[email protected]


1 WORKING PAPERS AND ARTICLES

1.1 Paulo Soares Esteves and Paulo M.M. Rodrigues, 2010. “Calendar Effects in Daily ATM Withdrawals”,Banco de Portugal, Economics and Research Department Working Papers No. 12/ 2010.

This paper analyses the calendar effects present in Automated Teller Machines

(ATM) withdrawals of residents, using daily data for Portugal for the period from

January 1st 2001 to December 31st 2008. The results presented may allow for a better

understanding of consumer habits and for adjusting the original series for calendar

effects. Considering the Quarterly National Accounts’ procedure of adjusting data for

seasonality and working days effects, this correction is important to ensure the use of

the ATM series as an instrument to nowcast private consumption.

Full text available on-line at:

http://www.bportugal.pt/en-US/BdP%20Publications%20Research/wp201012.pdf

1.2 Evans Kevin P. and Speight Alan E.H., 2010. “Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility”, Research in International Business and Finance, Volume 24, Issue 1, Pages 82-101.

This paper provides an analysis of intraday volatility using 5-min returns for Euro-

Dollar, Euro-Sterling and Euro-Yen exchange rates, and therefore a new market

setting. This includes a comparison of the performance of the Fourier flexible form

(FFF) intraday volatility filter with an alternative cubic spline approach in the

modelling of high frequency exchange rate volatility. Analysis of various potential

calendar effects and seasonal chronological changes reveals that although such effects

cause deviations from the average intraday volatility pattern, these intraday timing

effects are in many cases only marginally statistically significant and are insignificant

in economic terms. Results for the cubic spline approach imply that significant

macroeconomic announcement effects are larger and far more quickly absorbed into

exchange rates than is suggested by the FFF model, and underscores the advantage of

the cubic spline in permitting the periodicity in intraday volatility to be more closely

http://www.bportugal.pt/en-US/BdP Publications Research/wp201012.pdf


identified. Further analysis of macroeconomic announcement effects on volatility by

country of origin (including the US, Eurozone, UK, Germany, France and Japan)

reveals that the predominant reactions occur in response to US macroeconomic news,

but that Eurozone, German and UK announcements also cause significant volatility

reactions. Furthermore, Eurozone announcements are found to impact significantly

upon volatility in the pre-announcement period.


http://www.sciencedirect.com/science/article/B7CPK-4W6XW25-1/2/a39205f86edca8525b4581e73ce23d35

1.3 Stefan Benjamin Grimbacher, Laurens A. P. Swinkels and Pim Van Vliet, 2010. “An Anatomy of Calendar Effects”, SSRN Working Paper.

This paper studies the interaction of the five most well-established calendar effects:

the Halloween effect, January effect, turn-of-the-month effect, weekend effect and

holiday effect. We find that Halloween and turn-of-the month (TOM) are the

strongest effects fully diminishing the other three effects to zero. The equity premium

over the sample 1963-2008 is 7.2% if there is a Halloween or TOM effect, and -2.8%

in all other cases. These findings are robust across different samples over time and

stock markets.


http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1593770

1.4 Charles Amélie, 2010. “The day-of-the-week effects on the volatility: The role of the asymmetry”,European Journal of Operational Research,Volume 202, Issue 1, 1 April 2010, Pages 143-152.

In this paper, we propose to evaluate whether asymmetry influences the day-of-the-

week effects on volatility. We also investigate empirically the impact of the day-of-

the-week effect in major international stock markets using GARCH family models

from a forecast framework. Indeed the existence of calendar effects might be

interesting only if their incorporation in a model results in better volatility forecasts.






http://www.sciencedirect.com/science/article/B6VCT-4W741YJ-3/2/d53269868d6242d93aee80971f0d302a

1.5 Gerhard Thury, 2010. “Calendar Effects in Austrian Industrial Production”,WIFO Working Papers with number 65/1994.

No abstract available.


http://www.wifo.ac.at/wwa/jsp/index.jsp?fid=23923&id=11&typeid=8&display_mode=2&pub_language=2&language=2

1.6 Mokhtar Darmoul and Mokhtar Kouki, 2009. “Calendar effect and intraday volatility patterns of euro-dollar exchange rate : new evidence of Europe lunch period”, HAL, Université Paris1 Panthéon-Sorbonne, Document de Travail du Centre d'Economie de la Sorbonne - 2009.70.

Dans cet article, nous étudions le comportement ainsi que les caractéristiques

systématiques de l'effet calendrier perdus dans la volatilité du taux de change

intrajournalière de l'euro face au dollar à cinq minutes d'intervalles. Nous obtenons

par le biais de cette analyse une différenciation de ces effets à travers deux types de

filtres essentiels dans le traitement des rendements, tout en éliminant la forme de

fourrier FFF qui condamne les structures de persistance des chocs à prendre une

forme exponentielle. Ainsi, nous avons ressorti de nouvelles caractéristiques de la

volatilité du taux de change euro-dollar, telle que l'heure de déjeuner en Europe.


http://halshs.archives-ouvertes.fr/docs/00/42/97/59/PDF/09070.pdf

1.7 Maria Rosa Borges, 2009. “Calendar Effects in Stock Markets: Critique of Previous Methodologies and Recent Evidence in European Countries”, Department of Economics at the School of Economics and Management (ISEG), Technical University of Lisbon Working Papers No. 2009/37.

This paper examines day of the week and month of the year effects in seventeen

European stock market indexes in the period 1994-2007. We discuss the shortcomings

of model specifications and tests used in previous work, and propose a simpler





http://halshs.archives-ouvertes.fr/docs/00/42/97/59/PDF/09070.pdf


specification, usable for detecting all types of calendar effects. Recognizing that

returns are non-normally distributed, autocorrelated and that the residuals of linear

regressions are variant over time, we use statically robust estimation methodologies,

including bootstrapping and GARCH modeling. Although returns tend to be lower in

the months of August and September, we do not find strong evidence of across-the-

board calendar effects, as the most favorable evidence is only country-specific.

Additionally, using rolling windows regressions, we find that the stronger country-

specific calendar effects are not stable over the whole sample period, casting

additional doubt on the economic significance of calendar effects. We conclude that

our results are not immune to the critique that calendar effects may only be a

“chimera” delivered by intensive data mining.


http://pascal.iseg.utl.pt/~depeco/wp/wp372009.pdf

1.8 Eleftherios Giovanis, 2009. “Calendar Effects in Fifty-five Stock Market Indices”, Global Journal of Finance and Management, ISSN 0975 - 6477 Volume 1, Number 2 (2009), pp. 75-98.

This paper examines the calendar anomalies/effects in 55 Stock market exchange

indices of 51 countries around the world. The calendar effects which are examined are

the turn-of-the-Month effect, the day-of-the-Week effect, the Month-of the-Year

effect and the semi-Month effect. The methodology which is followed is the test

hypothesis of two unequal data samples with bootstrapping simulated t-statistics.

Simultaneously, with the same procedure a seasonality test is applied in order to

investigate if more frequent seasonality on expected returns or in volatility is

presented. The conclusion is that we reject all calendar effects in a global level, except

from the turn-of-the-Month effect, which is present in 36 stock indices and that there

is higher seasonality in volatility rather on expected returns, concerning the day of the

week and the month of the year effects. The main purpose of the paper is to present a

methodology appropriate for data mining which rejects the existence and persistence

of main calendar anomalies as the Monday and January effects, while previous

methodologies accept them. So this paper presents an alternative approach in the

estimation of calendar anomalies and data mining, as well gives some guide notes for

financial strategy.

http://pascal.iseg.utl.pt/~depeco/wp/wp372009.pdf




1.9 Fecht, Falko, Nyborg Kjell G. and Rocholl, Jörg, 2008. “Liquidity management and overnight rate calendar effects: Evidence from German banks”,The North American Journal of Economics and Finance, Volume 19, Issue 1, March 2008, Pages 7-21.

We document a general pattern in the euro area overnight interbank rate (EONIA) and

analyze how German banks compared to other EMU banks respond to these

predictable changes in the price for reserve holdings. At the beginning of the

maintenance period, when the EONIA is typically above average, we observe that

German banks hold substantially less reserves than their daily average required

reserves. Thus in contrast to other EMU banks, German banks back load the

fulfillment of their reserve requirements over the reserve maintenance period and

thereby benefit from the general pattern in the EONIA. Looking at the disaggregate

data we find than this is particularly the case for the Landesbanks. We argue that the

end of the calender month effect in the EONIA may be driven by a temporary

shortage of liquidity, relative to reserve requirements, at the beginning of the

maintenance period (which coincides with the end of the calendar month).


http://www.sciencedirect.com/science/article/B6W5T-4PP2D19-2/1/3a8d730f484db7d5498857f4dbf4a1b0

1.10 Ushad Subadar Agathee, 2008. “Calendar Effects and the Months of the Year: Evidence from the Mauritian Stock Exchange”, International Research Journal of Finance and Economics, ISSN 1450-2887, Issue 14 (2008).

The objective of this paper is to examine possible month of the year effect in an

emerging market, in particular, the Stock Exchange of Mauritius (SEM). Monthly

SEMDEX returns were computed from 1989 to 2006. The results show that returns on

average are lowest in the month of March and highest in the month of June. However,

equality mean-return tests show that returns are statistically the same across all

months.





Also, the regression analysis reveals that returns are not dependent on the months of

the year, except for January. Overall, the results seem to be more consistent with the

prediction of the efficient market hypothesis.


http://www.eurojournals.com/irjfe%2014%20ushad.pdf

1.11 Brian C. Monsell, 2007. “Issues in Modeling and Adjusting Calendar Effects in Economic Time Series”.

The effectiveness of alternate models for estimating trading day and moving holiday

effects in economic time series are examined. Several alternative approaches to

modeling Easter holiday effects will be examined, including a method suggested by

the Australian Bureau of Statistics that includes a linear effect. In addition, a more

parsimonious technique for modeling trading day variation will be examined by

applying the day-of-week constraints from the weekday/weekend trading day contrast

regressor found in TRAMO and X-12- ARIMA to stock trading day.


http://www.census.gov/ts/papers/ices2007bcm.pdf

1.12 Bing Zhang and Xindan Li, 2006. “Do Calendar Effects Still Exist in the Chinese Stock Markets?”,Journal of Chinese Economic and Business Studies, Volume 4, Issue 2 July 2006 , pages 151 – 163.

The paper uses rolling sample tests to investigate time-varying calendar effects in the

Chinese stock market, based on the GARCH (1, 1)-GED model. The Friday effect

existed with low volatility at the early stage, but it seems to have disappeared since

1997. The positive Tuesday effect began to appear then. There is a small-firm January

effect with high volatility. The turn-of-the month effect has also disappeared in the

Chinese stock market since 1997.


http://www.informaworld.com/smpp/content~content=a747992201~db=all

http://www.eurojournals.com/irjfe 14 ushad.pdf

http://www.census.gov/ts/papers/ices2007bcm.pdf



1.13 Catherine Kyrtsou, Alexandros Leontitsis and Costas Siriopoulos, 2006. “Exploring The Impact Of Calendar Effects On The Dynamic Structure And Forecasts Of Financial Time Series”,International Journal of Theoretical and Applied Finance (IJTAF), Volume: 9, Issue: 1(2006) pp. 1-22.

Several recently developed chaotic forecasting methods give better results than the

random walk forecasts. However they do not take into account specific regularities of

stock returns reported in empirical finance literature, such as the calendar effects. In

this paper, we present a method for filtering the day-of-the-week and the holiday

effect in a time series. Our main objective is twofold. On the one hand we study how

the underlying dynamics of the Nasdaq Composite, and TSE 300 Composite returns

series can be influenced by the presence of calendar effects. On the other hand we

adapt our method to chaotic forecasting. Its computational advantages lead to

significant improvements of forecasts.


http://www.worldscinet.com/ijtaf/09/0901/S0219024906003433.html

1.14 Alexandros Leontitsis and Costas Siriopoulos, 2006. “Nonlinear forecast of financial time series through dynamical calendar correction”, Applied Financial Economics Letters, Volume 2, Issue 5 September 2006 , pages 337 – 340.

A method is presented that takes into account the day-of-the-week and the turn-of-the-

month effect and the holiday effect and embodies them to neural network forecasting.

It adjusts the time series in order to make its dynamics less distorted. After a predicted

value is calculated by the network, the inverse adjustment is made to obtain the final

predicted value. If there are no calendar effects on the time series this method has

approximately the same performance as its classic counterpart. Empirical results are

presented, based on NASDAQ Composite, and TSE 300 Composite indices using

daily returns form 1984 to 2003.



http://www.worldscinet.com/ijtaf/09/0901/S0219024906003433.html



1.15 Veera Lenkkeri , Wessel Marquering and Ben Strunkmann-Meister, 2006. “The Friday Effect in European Securitized Real Estate Index Returns”,The Journal of Real Estate Finance and Economics,Volume 33, Number 1, 31-50.

This study extends research on the day-of-the-week effect towards European real

estate indices. We examine this anomaly for several European securitized real estate

index returns between 1990 and 2003. Although the countries under analysis have

unique country-specific patterns, we find that eight out of eleven European countries

exhibit abnormally high Friday returns. Moreover, two different Europe indices also

exhibit the Friday anomaly. The anomaly is robust with respect to extreme

observations, alternative specifications and several well-known calendar effects.


http://www.springerlink.com/content/121637g007318h22/

1.16 Peter Reinhard Hansen, Asger Lunde and James M. Nason, 2005. “Testing the significance of calendar effects”, Federal Reserve Bank of Atlanta Working Paper No. 2005-02.

This paper studies tests of calendar effects in equity returns. It is necessary to control

for all possible calendar effects to avoid spurious results. The authors contribute to the

calendar effects literature and its significance with a test for calendar-specific

anomalies that conditions on the nuisance of possible calendar effects. Thus, their

approach to test for calendar effects produces robust data-mining results.

Unfortunately, attempts to control for a large number of possible calendar effects have

the downside of diminishing the power of the test, making it more difficult to detect

actual anomalies. The authors show that our test achieves good power properties

because it exploits the correlation structure of (excess) returns specific to the calendar

effect being studied. We implement the test with bootstrap methods and apply it to

stock indices from Denmark, France, Germany, Hong Kong, Italy, Japan, Norway,

Sweden, the United Kingdom, and the United States. Bootstrap p-values reveal that

calendar effects are significant for returns in most of these equity markets, but end-of-

the-year effects are predominant. It also appears that, beginning in the late 1980s,

calendar effects have diminished except in small-cap stock indices.

http://www.springerlink.com/content/121637g007318h22/



http://www.frbatlanta.org/filelegacydocs/wp0502.pdf

1.17 Yucel Eray M., 2005. “Does Ramadan Have Any Effect on Food Prices: A Dual-Calendar Perspective on the Turkish Data”,University Library of Munich, Germany in MPRA Paper No. 1141.

The effects of a specific religious tradition on the food prices establish the central

theme of this paper. In specific, I investigate whether the month Ramadan has any

effect on food prices. I perform the analysis under two alternative calendar

conventions, namely the Gregorian and Hijri calendars. Under both conventions, the

paper reveals the effects of Ramadan, yet these effects are better captured when the

latter is used. This highlights the importance of the calendar choice on econometric

analysis, on the basis of a simple-yet-genuine socio-economic exercise. Possible

benefits from this exercise in pedagogical terms as well as in inflation forecasting are

also addressed.


http://mpra.ub.uni-muenchen.de/1141/1/MPRA_paper_1141.pdf

1.18 Dimitar Tonchev and Tae-Hwan Kim, 2004, “Calendar effects in Eastern European financial markets: evidence from the Czech Republic, Slovakia and Slovenia”, Applied Financial Economics, Volume 14, Issue 14 October 2004 , pages 1035 – 1043.

This paper uses a new data set from three Eastern European countries (Czech

Republic, Slovakia and Slovenia) to investigate whether the so-called calendar effects

are present in the newly developing financial markets in those countries. Five

calendar effects are examined in both mean by OLS regression and variance by

GARCH; the day of the week effect, the January effect, the half-month effect, the turn

of the month effect and the holiday effect. In the empirical analysis, very weak

evidence has been found for the calendar effects in the three countries, and these

effects, where they exist, have different characteristics in the different stock markets.



http://www.frbatlanta.org/filelegacydocs/wp0502.pdf

http://mpra.ub.uni-muenchen.de/1141/1/MPRA_paper_1141.pdf



1.19 Bell W. R., Martin D. E. K., 2004. “Modeling Time-Varying Trading-Day Effects in Monthly Time Series”, ASA Proceedings of the Joint Statistical Meetings.



http://www.census.gov/ts/papers/jsm2004dem.pdf

1.20 Peter Reinhard Hansen and Asger Lunde, 2003. “Testing the Significance of Calendar Effects”, Brown University, Department of Economics Working Papers No. 2003-03.

When evaluating the significance of calendar effects, such as those associated with

Monday and January, it is necessary to control for all possible calendar effects to

avoid spurious results. The downside of having to control for a large number of

possible calendar effects is that it diminish the power and makes it harder to detect

real anomalies.

This paper contributes to the discussion of calendar effects and their significance. We

derive a test for calendar specific anomalies, which controls for the full space of

possible calendar effects. This test achieves good power properties by exploiting a

particular correlation structure, and its main advantage is that it is capable of

producing data-mining robust significance.

We apply the test to stock indices from Denmark, France, Germany, Hong Kong,

Italy, Japan, Norway, Sweden, UK, and USA. Our findings are that calendar effects

are significant in most series, and it is primarily end-of-the-year effects that exhibit

the largest anomalies. In recent years it seems that the calendar effects have

diminished except in small cap stock indices.


http://www.econ.brown.edu/2003/2003-3_paper.pdf

1.21 Miguel Balbina and Nuno C. Martins, 2002. “The Analysis of Calendar Effects on the Daily Returns of the Portuguese Stock Market: the Weekend and Public Holiday Effects”, Banco de Portugal, Economics and Research Department, Economic Bulletin, December 2002.

http://www.census.gov/ts/papers/jsm2004dem.pdf

http://www.econ.brown.edu/2003/2003-3_paper.pdf




http://www.bportugal.pt/en-US/BdP%20Publications%20Research/AB200212_e.pdf

1.22 Thomas Hellström, 2002. “Trends and Calendar Effects in Stock Returns”.

This paper presents statistical investigations regarding the value of the trend concept

and calendar effects for prediction of stock returns. The examined data covers 207

stocks on the Swedish stock market for the time period 1987-1996. The results show a

very weak trend behavior. The massive better part of returns falls into a region, where

it is very difficult to claim any correlation between past and future price trends. It is

also shown that seasonal variables, such as the month of the year, affect the stock

returns more than the average daily returns. This is consequential for all methods,

where the seasonal variables are not taken into account in predicting daily stock

returns.


http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.20.1538&rep=rep1&type=pdf

1.23 Godfrey Gregory A. and Powell Warren B., 2000. “Adaptive estimation of daily demands with complex calendar effects for freight transportation”,Elsevier Journal Transportation Research Part B: Methodological, Volume 34, Issue 6, August 2000, Pages 451-469.

We address the problem of forecasting spatial activities on a daily basis that are

subject to the types of multiple, complex calendar effects that arise in many

applications. Our problem is motivated by applications where we generally need to

produce thousands, and frequently tens of thousands, of models, as arises in the

prediction of daily origin-destination freight flows. Exponential smoothing-based

models are the simplest to implement, but standard methods can handle only simple

seasonal patterns. We propose a class of exponential smoothing-based methods that

handle multiple calendar effects. These methods are much easier to implement and

apply than more sophisticated ARIMA-based methods. We show that our techniques

http://www.bportugal.pt/en-US/BdP Publications Research/AB200212_e.pdf




actually outperform ARIMA-based methods in terms of forecast error, indicating that

our simplicity does not involve any loss in accuracy.


http://www.sciencedirect.com/science/article/B6V99-405SWHN-1/2/b01bb76a39e28699e06c07d3801a1485

1.24 Sullivan Ryan, Timmermann Allan and White Halbert, 1998. “Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns”, Department of Economics, UC San Diego University of California at San Diego, Discussion Paper 98-16..

Economics is primarily a non-experimental science. Typically, we cannot generate

new data sets on which to test hypotheses independently of the data that may have led

to a particular theory. The common practice of using the same data set to formulate

and test hypotheses introduces data-snooping biases that, if not accounted for,

invalidate the assumptions underlying classical statistical inference. A striking

example of a data-driven discovery is the presence of calendar effects in stock returns.

There appears to be very substantial evidence of systematic abnormal stock returns

related to the day of the week, the week of the month, the month of the year, the turn

of the month, holidays, and so forth. However, this evidence has largely been

considered without accounting for the intensive search preceding it. In this paper we

use 100 years of daily data and a new bootstrap procedure that allows us to explicitly

measure the distortions in statistical inference induced by data-snooping. We find that

although nominal P-values of individual calendar rules are extremely significant, once

evaluated in the context of the full universe from which such rules were drawn,

calendar effects no longer remain significant.


http://www.escholarship.org/uc/item/2z02z6d9

1.25 Maria Helena Nunes, 1997. “The impact of precipitation and calendar effects on cement sales”, Banco de Portugal, Economics and Research Department, Economic Bulletin, March 1997.




http://www.escholarship.org/uc/item/2z02z6d9



http://www.bportugal.pt/en-US/BdP%20Publications%20Research/AB199705_e.pdf

1.26 Stephanie Cano, Patricia Getz, Jurgen Kropf, Stuart Scott and George Stamas,1996. “Adjusting for a calendar effect in employment time series”, Bureau of Labor Statistics, Proceedings paper No. 112.



http://www.amstat.org/sections/srms/Proceedings/papers/1996_112.pdf

1.27 Bell W. R., 1995. “Correction to « Seasonal Decomposition of Deterministic Effects »”, (n° RR84/01), Research Report, Statistical Research Division, U.S. Bureau of the Census, Washington D.C., RR95/01.



http://www.census.gov/srd/papers/pdf/rr95-01.pdf

1.28 Gerhard Thury and Michael Wüger, 1992. “Adjustment of Economic Time Series for Outliers, Seasonal, and Calendar Effects”, WIFO-Monatsberichte , 9/1992, pp. 488-495].

The article tests a procedure for spotting and eliminating statistical outliers in

economic time series which does not require previous information on the location of

outliers and which estimates model parameters and outliers simultaneously. Results

for three different time series suggest that this approach may be an alternative to

intervention models. Furthermore, allowing for calendar and outlier effects

significantly reduces the variance of model-based seasonal adjustment procedures and

may prove clearly superior to conventional empirical-technical methods (Census X-

11).



http://www.bportugal.pt/en-US/BdP Publications Research/AB199705_e.pdf

http://www.amstat.org/sections/srms/Proceedings/papers/1996_112.pdf

http://www.census.gov/srd/papers/pdf/rr95-01.pdf




1.29 Johnston Elizabeth Tashijan, Kracaw William A. and McConnell John J., 1991. “Day-of-the-Week Effects in Financial Futures: An Analysis of GNMA, T-Bond, T-Note, and T-Bill Contracts”, Journal of Financial and Quantitative Analysis (1991), 26: 23-44 Cambridge.

This paper provides a comprehensive study of weekly seasonal effects in GNMA, T-

bond, T-note, and T-bill futures returns. Two distinct patterns are found in returns on

GNMA, T-bond, and T-note contracts, while no seasonals are noted for T-bill futures.

A negative Monday seasonal is found for GNMA and T-bond contracts. A positive

Tuesday seasonal is found on GNMA, T-bond, and T-note contracts. Our evidence

indicates that the significance of weekly seasonals depends in an important way on

the time period studied. The negative Monday phenomenon occurs only in the data

before 1982, while the positive Tuesday effect is present only after 1984. In addition,

we find that both seasonal phenomena occur only during months prior to a delivery

month. This effect appears to be related to the calendar month. More specifically, the

Monday effect is apparently concentrated during February, while the Tuesday effect is

concentrated during May.


http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=4472816

1.30 Gerhard Thury, 1989. “Calendar Effects in Industrial Production Time Series”, WIFO-Monatsberichte, 8/1989, pp. 521-526.

Adjusting for the changing number of working days is a prerequisite for any advanced

statistical analysis of monthly production series. A series of industrial output has been

constructed using auxiliary variables which adequately reflect the particularities of the

Austrian calendar. It exhibits a regular component and a stable seasonal pattern. Its

adjustment for seasonal variations and short-term extrapolation with time series

techniques therefore poses no major difficulties.



http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=4472816




1.31 Pauly R. and Schell A., 1989. “Calendar Effects in Structural Time Series Models with Trend and Season”, Empirical Economics,Volume 14, Number 3, 241-256.

Calendar effects are analysed in the class of structural time series models one of the

two main model based approaches in time series decomposition. While Bell and

Hillmer (1983) modeled calendar variation in the ARIMA model based approach, we

represent structural models in the generalized regression form which allows to apply

classical estimation and test procedures. It turns out that the expected high

computaional complexity 0(T 3) in the generalized regression model can be reduced to

0(T). As all parameters are estimated by maximizing the likelihood the Likelihood

Ratio statistics can be used to test effects of the calendar composition.


http://www.springerlink.com/content/g7u4233q14j10254/

1.32 Thury G. ,1986. “The Consequences of Trading Day Variation and Calendar Effects for ARIMA Model Building and Seasonal Adjustment”, Empirica, Vol. 13-1, pp 3-25.



http://www.springerlink.com/content/w36684q34074723g/

1.33 Bell W. R. and Hillmer S. C., 1983. “Modeling Time series with Calendar Variation”, Journal of the American Statistical Association, Vol. 78, n°383, pp 526-534.



http://www.jstor.org/pss/2288114

1.34 Cleveland W. S., 1983. “Seasonal and Calendar Adjustment”, Handbook of Statistics Vol. 3 : Time Series in the Frequency Domain, Brillinger D. R. and Krishnaiah P. R. (eds), North-Holland, pp 39-72.


http://www.springerlink.com/content/g7u4233q14j10254/

http://www.springerlink.com/content/w36684q34074723g/




http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B7P6H-4FV9D8T-1C&_user=10&_coverDate=12%2F31%2F1983&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1484113247&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=587a59ad3e7c72f86b4deb25e2fdd67b&searchtype=a

1.35 Cleveland W. S., Devlin S. J., 1982. “Calendar effects in monthly time series: Modeling and adjustment”, Journal of the American Statistical Association, Vol. 77, n°379, pp 520-528.




1.36 Cleveland W.P. and M.R. Grupe, 1981. “Modeling time series when calendar effects are present”, Board of Governors of the Federal Reserve System (U.S.) Special Studies Papers No. 162.



http://www.census.gov/ts/papers/Conference1983/ClevelandGrupe1983.pdf

1.37 Cleveland W. S., Devlin S. J., 1980. “Calendar effects in monthly time series: Detection by spectrum analysis and graphical methods”, Journal of the American Statistical Association, Vol. 75, n°371, pp 487-496.




1.38 Ken Holden, John Thompson and Yuphin Ruangrit. “The Asian Crisis and Calendar Effects on Stock Returns in Thailand”.

This paper reports work in progress. It systematically examines daily returns of the

Thai Stock Market Index to determine whether there is evidence of calendar effects







http://www.census.gov/ts/papers/Conference1983/ClevelandGrupe1983.pdf



due to the day of the week, the month of the year, days before or after holidays and

within-month effects. Particular attention is given to the evidence of these ‘anomalies’

prior to, during and after the so-called Asian crisis. Daily data are used and runs from

3rd January 1995 to 29th December 2000 providing a total of 1473 observations. This

total is subdivided into three periods; i.e. prior to (344 observations), during (570

observations) and after the crisis (559 observations). In each case the number of

observations is sufficient to permit comparisons between the behaviour within the

various periods. Most of the previous empirical evidence examines each effect in

isolation. The approach adopted within this paper is to start with a general model

which incorporates a range of different calendar effects before estimating the

conditional volatility models. The intention is to test the final model by examining the

forecast performance.


http://www.ljmu.ac.uk/AFE/AFE_docs/cibef0302.pdf

http://www.ljmu.ac.uk/AFE/AFE_docs/cibef0302.pdf

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Globalization of the Economy - Europa