Selected Readings –November 2010 1 SELECTED READINGS Focus on: Calendar effects November 2010
Selected Readings –November 2010 1
SELECTED READINGS
Focus on: Calendar effects
November 2010
Selected Readings –November 2010 2
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
Selected Readings –November 2010 3
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
Selected Readings –November 2010 4
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
Selected Readings –November 2010 5
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
Selected Readings –November 2010 6
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"
Selected Readings –November 2010 7
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
Selected Readings –November 2010 8
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.
Full text available on-line at:
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.
Full text available on-line at:
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.
Selected Readings –November 2010 9
Full text available on-line at:
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.
Full text available on-line at:
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.
Full text available on-line at:
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
Selected Readings –November 2010 10
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.
Full text available on-line at:
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.
Selected Readings –November 2010 11
Full text available on-line at:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1572361
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).
Full text available on-line at:
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.
Selected Readings –November 2010 12
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.
Full text available on-line at:
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.
Full text available on-line at:
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.
Full text available on-line at:
http://www.informaworld.com/smpp/content~content=a747992201~db=all
Selected Readings –November 2010 13
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.
Full text available on-line at:
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.
Full text available on-line at:
http://www.informaworld.com/smpp/content~content=a755210305~db=all
Selected Readings –November 2010 14
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.
Full text available on-line at:
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.
Selected Readings –November 2010 15
Full text available on-line at:
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.
Full text available on-line at:
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.
Full text available on-line at:
http://www.informaworld.com/smpp/content~content=a714022613~db=all
Selected Readings –November 2010 16
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.
No abstract available.
Full text available on-line at:
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.
Full text available on-line at:
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.
Selected Readings –November 2010 17
No abstract available.
Full text available on-line at:
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.
Full text available on-line at:
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
Selected Readings –November 2010 18
actually outperform ARIMA-based methods in terms of forecast error, indicating that
our simplicity does not involve any loss in accuracy.
Full text available on-line at:
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.
Full text available on-line at:
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.
No abstract available.
Selected Readings –November 2010 19
Full text available on-line at:
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.
No abstract available.
Full text available on-line at:
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.
No abstract available.
Full text available on-line at:
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).
Full text available on-line at:
http://www.wifo.ac.at/wwa/jsp/index.jsp?fid=23923&id=1211&typeid=8&display_mode=2&pub_language=2&language=2
Selected Readings –November 2010 20
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.
Full text available on-line at:
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.
Full text available on-line at:
http://www.wifo.ac.at/wwa/jsp/index.jsp?fid=23923&id=1019&typeid=8&display_mode=2&pub_language=2&language=2
Selected Readings –November 2010 21
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.
Full text available on-line at:
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.
No abstract available.
Full text available on-line at:
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.
No abstract available.
Full text available on-line at:
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.
No abstract available.
Selected Readings –November 2010 22
Full text available on-line at:
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.
No abstract available.
Full text available on-line at:
http://www.jstor.org/pss/2287705
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.
No abstract available.
Full text available on-line at:
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.
No abstract available.
Full text available on-line at:
http://www.jstor.org/pss/2287636
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
Selected Readings –November 2010 23
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.
Full text available on-line at:
http://www.ljmu.ac.uk/AFE/AFE_docs/cibef0302.pdf