Silveira, Rodrigo Lanna Franco da; Maciel, Leandro dos Santos; Mattos, Fabio L.; Ballini, Rosangela

doi:10.1016/j.rausp.2017.08.003

Acessibilidade / Reportar erro

Brasil

Revista de Administração (São Paulo)

Español English

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Finance and Accounting • Rev. Adm. (São Paulo) 52 (4) • Oct-Dec 2017 • https://doi.org/10.1016/j.rausp.2017.08.003 copiar

Persistência de volatilidade e efeito de inventário nos mercados de futuros de grãos: evidência de um modelo recursivo

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

Neste artigo, os autores procuraram investigar a persistência da volatilidade e inventory effect nos mercados futuros de grãos no período entre 1959 e 2014. A inovação do estudo consistiu na aplicação de um modelo de volatilidade recursivo TARCH(1,1) com rolagem das estimativas a partir de uma janela de tempo de quatro anos. Os resultados apontaram para uma alta persistência da volatilidade condicional nos mercados de milho e soja. Além disso, observou-se a presença dos efeitos sazonalidade, inventory e time-to-maturity na dinâmica de volatilidade dos preços de ambos mercados. Verificou-se ainda uma queda na persistência de curto-prazo no período recente, o que levou a uma diminuição da persistência de longo-prazo e da meia-vida nos mercados em estudo.

Palavras-chave:
Volatilidade; Persistência da volatilidade; Inventory effect; Mercado futuro de grãos

Reference	Method	Variables	Period (data frequency)	Seasonality effect			Time-to-maturity effect			Inventory effect			Trade effect			Speculative activity effect			Loan rate effect			Price level effect			Volatility persistence effect
				Y	N	NS	Y	N	NS	Y	N	NS	Y	N	NS	Y	N	NS	Y	N	NS	Y	N	NS	Y	N	NS
^{Rutledge (1976)}Rutledge, 1976 Rutledge, D. J. S. (1976). A note on the variability of futures prices. Review of Economics and Statistics, 58(1), 118-120.	Statistical tests	Silver, cocoa, wheat and soybean oil	1969-1971 (daily)			X	X					X			X			X			X			X			X
^{Anderson (1985)}Anderson, 1985 Anderson, R. W. (1985). Some determinants of the volatility of futures prices. Journal of Futures Markets, 5, 331-348.	Tests for equality of variances and regression analysis	9 commodity futures prices (8 agricultural)	1966-1980 (daily)	X			X					X			X			X			X			X			X
^{Milonas (1986)}Milonas, 1986 Milonas, N. T. (1986). Price variability and the maturity effect in futures markets. Journal of Futures Markets, 6, 443-460.	Tests for equality of variances and regression analysis	11 futures prices (5 agricultural)	1972-1983 (daily)			X	X					X			X			X			X			X			X
^{Kenyon, Kling, Jordan, Seale, and McCabe (1987)}Kenyon et al., 1987 Kenyon, D., Kling, K., Jordan, J., Seale, W., & McCabe, N. (1987). Factors affecting agricultural futures price variance. Journal of Futures Markets, 7, 73-92.	Regression analysis	Corn, soybean, wheat, live cattle and live hog futures prices	1974-1983 (daily)	X					X			X			X			X	X			X			X
^{Glauber and Heifner's (1986)}Glauber and Heifner, 1986 Glauber, J. W., & Heifner, R. G. (1986). Forecasting futures price variability. Applied commodity price analysis, forecasting, and market risk management. In Proceedings of the NCR 134 conference (pp. 153–165).	Regression analysis	Soybean futures price	1961-1984 (daily)	X			X				X				X			X		X			X				X
^{Streeter and Tomek (1992)}Streeter and Tomek, 1992 Streeter, D. H., & Tomek, W. G. (1992). Variability in soybean futures prices: An integrated framework. Journal of Futures Markets, 12, 705-728.	Regression analysis	Soybean futures prices	1976-1986 (daily)	X			X				X			X			X				X	X					X
^{Khoury and Yourougou (1993)}Khoury and Yourougou, 1993 Khoury, N., & Yourougou, P. (1993). Determinants of agricultural futures price volatilities: Evidence from Winnipeg Commodity Exchange. Journal of Futures Markets, 13, 345-356.	Regression analysis	Canola, rye, feed barley, feed wheat, flaxseed, and oats futures prices	1980-1989 (daily)	X			X				X			X			X			X			X			X
^{Bessembinder and Seguin (1993)}Bessembinder and Seguin, 1993 Bessembinder, H., & Seguin, P. J. (1993). Price volatility, trading volume, and market depth: Evidence from futures markets. Journal of Financial and Quantitative Analysis, 28(1), 21-39.	Regression analysis	8 futures prices (2 agricultural)	1982-1990 (daily)			X	X					X	X					X			X			X	X
^{Yang and Brorsen (1993)}Yang and Brorsen, 1993 Yang, S. R., & Brorsen, B. W. (1993). Nonlinear dynamics of daily futures prices: Conditional heteroskedasticity or chaos?. Journal of Futures Markets, 13, 175-191.	GARCH and deterministic chaos processes	11 futures prices (7 agricultural)	1979-88 (daily)	X			X					X			X			X			X			X			X
^{Hennessy and Wahl (1996)}Hennessy and Wahl, 1996 Hennessy, D. A., & Wahl, T. I. (1996). The effects of decision making on futures price volatility. American Journal of Agricultural Economics, 78, 591-603.	Contingent claims methodology	Corn, soybeans and wheat	1985-94 (monthly)	X				X			X		X					X			X			X			X
^{Kocagil and Shachmurove (1998)}Kocagil and Shachmurove, 1998 Kocagil, A. E., & Shachmurove, Y. (1998). Return-volume dynamics in futures markets. Journal of Futures Markets, 18, 399-426.	Time-series analysis	16 futures prices (6 agricultural)	1980-1995 (daily)			X			X			X	X					X			X			X			X
^{Malliaris and Urrutia (1998)}Malliaris and Urrutia, 1998 Malliaris, A. G., & Urrutia, J. L. (1998). Volume and price relationships: Hypotheses and testing for agricultural future. Journal of Futures Markets, 18(4), 399-426.	Time-series analysis	Corn, wheat, oats, soybean, soybean meal, and soybean oil	1981-1995 (daily)			X			X			X	X					X			X			X			X
^{Hudson and Coble (1999)}Hudson and Coble, 1999 Hudson, D., & Coble, K. (1999). Harvest contract price volatility for cotton. Journal of Futures Markets, 19, 717-733.	GARCH models	Cotton futures prices (monthly)	1982-97 (monthly)	X				X				X		X			X					X					X
^{Goodwin and Schnepf (2000)}Goodwin and Schnepf, 2000 Goodwin, B. K., & Schnepf, R. (2000). Determinants of endogenous price risk in corn and wheat futures markets. Journal of Futures Markets, 20, 753-774.	GARCH and VAR models	Corn and wheat futures prices	1986-1997 (weekly)	X				X		X			X			X					X			X	X
^{Allen and Cruickshank (2000)}Allen and Cruickshank, 2000 Allen, D. E., & Cruickshank, S. N. (2000). Empirical testing of the Samuelson hypothesis: An application to futures markets in Australia, Singapore and the UK. Working paper. School of Finance and Business Economics, Edith Cowan University.	Regression analysis and ARCH models	12 commodity futures prices (9 agricultural)	1979-1998 (daily)			X	X					X			X			X			X			X			X
^{Chatrath, Adrangi, and Dhanda (2002)}Chatrath et al., 2002 Chatrath, A., Adrangi, B., & Dhanda, K. K. (2002). Are commodity prices chaotic?. Agricultural Economics, 27, 123-137.	Chaos tests	Corn, soybeans, wheat and cotton futures prices	1968-1995 (daily)	X			X					X			X			X			X			X			X
^{Yang, Balyeat, and Leatham (2005)}Yang et al., 2005 Yang, J., Balyeat, R. B., & Leatham, D. J. (2005). Futures trading activity and commodity cash price volatility. Journal of Business Finance & Accounting, 32, 297-323.	Granger causality tests	Corn, soybeans, wheat, sugar, coffee, live cattle and cotton futures prices	1992-2001			X			X			X	X					X			X			X			X
^{Smith (2005)}Smith, 2005 Smith, A. (2005). Partially overlapping time series: A new model for volatility dynamics in commodity futures. Journal of Applied Econometrics, 20, 405-422.	Partially overlapping time series model	Corn futures prices	1991-2000 (daily)	X			X			X					X			X			X			X			X
^{Daal, Farhat, and Wei (2006)}Daal et al., 2006 Daal, E., Farhat, J., & Wei, P. P. (2006). Does futures exhibit maturity effect? New evidence from an extensive set of US and foreign futures contracts. Review of Financial Economics, 15(2), 113-128.	Regression analysis	61 futures contracts (23 agricultural)	1960-2000 (daily)			X		X				X			X			X			X			X			X
^{Duong and Kalev (2008)}Duong and Kalev, 2008 Duong, H. N., & Kalev, P. S. (2008). The Samuelson hypothesis in futures markets: An analysis using intraday data. Journal of Banking & Finance, 32(4), 489-500.	Non-parametric and regression-based tests; GARCH model	Tick-by-tick and bid-ask quote prices for 20 futures markets (10 agricultural)	1996-2003 (intraday)	X			X					X			X			X			X			X			X
^{Kalev and Duong (2008)}Kalev and Duong, 2008 Kalev, P. S., & Duong, H. N. (2008). A test of the Samuelson hypothesis using realized range. Journal of Futures Markets, 28, 680-696.	Nonparametric test and regression analysis	14 futures prices (10 agricultural)	1996-2003 (intraday)			X	X		X			X			X			X			X			X			X
^{Balcombe (2009)}Balcombe, 2009 Balcombe, K. (2009). The nature and determinants of volatility in agricultural prices: An empirical study from 1962–2008. A report to the Food and Agriculture Organization of the United Nations.	Decomposition and panel approaches	19 agricultural spot prices	Varies (monthly-annual)			X			X	X					X			X			X			X	X
^{Karali and Thurman (2010)}Karali and Thurman, 2010 Karali, B., & Thurman, W. N. (2010). Components of grain futures price volatility. Journal of Agricultural and Resource Economics, 35(2), 167-182.	GLS estimation	Corn, soybean, wheat, and oats futures price	1986-2007 (daily)	X			X			X					X			X			X			X		X
^{Karali, Dorfman, and Thurman (2010)}Karali et al., 2010 Karali, B., Dorfman, J. H., & Thurman, W. N. (2010). Delivery horizon and grain market volatility. Journal of Futures Markets, 30, 846-873.	Smoothed Bayesian estimator	Corn, soybeans, and oats futures prices	Varied (daily)	X			X			X					X			X			X			X			X
^{Carpantier (2010)}Carpantier, 2010 Carpantier, J.-F. (2010). Commodities inventory effect. Discussion Paper 2010/40. Belgium: Center for Operations Research and Econometrics, Université Catholique de Louvain, Louvain-la-Neuve.	GJR-GARCH and EGARCH models	15 commodity spot prices (5 agricultural)	1994-2009 (daily)			X			X	X					X			X			X			X			X
^{Verma and Kumar (2010)}Verma and Kumar, 2010 Verma, A., & Kumar, C. V. R. S. V. (2010). An examination of the maturity effect in the Indian commodities futures market. Agricultural Economics Research Review, 23, 335-342.	Regression analysis	Wheat and pepper futures prices	2004-2007 (daily)			X	X					X			X			X			X			X			X
^{Stigler and Prakash (2011)}Stigler and Prakash, 2011 Stigler, M., & Prakash, A. (2011). The role of low stocks in generating volatility and panic. In A. Prakash (Ed.), Safeguarding food security in volatile global markets (pp. 314–328). Rome: Food and Agriculture Organization of the United Nations (FAO).	Markov regime-switching GARCH	16 commodity spot prices	1985-2009 (daily)			X			X	X					X			X			X			X			X
^{Carpantier and Dufays (2012)}Carpantier and Dufays, 2012 Carpantier, J.-F., & Dufays, A. (2012). Commodities volatility and the theory of storage. Discussion Paper 2012/37. Belgium: Center for Operations Research and Econometrics, Université Catholique de Louvain, Louvain-la-Neuve.	GJR-GARCH model	16 commodity spot prices (7 agricultural)	1994-2011 (weekly)			X			X	X					X			X			X			X			X
^{Vivian and Wohar (2012)}Vivian and Wohar, 2012 Vivian, A., & Wohar, M. E. (2012). Commodity volatility breaks. Journal of International Financial Markets, Institutions and Money, 22(2), 395-422.	Iterative cumulative sum of squares and GARCH	28 commodities (13 agricultural)	1985-2010 (daily)			X			X			X			X			X			X			X	X
^{Gupta and Rajib (2012)}Gupta and Rajib, 2012 Gupta, S. K., & Rajib, P. (2012). Samuelson hypothesis & Indian commodity derivatives market. Asia-Pacific Financial Markets, 19(4), 331-352.	GARCH, EGARCH and TGARCH	8 commodity futures prices (1 agricultural)	2008-2009 (daily)			X		X		X			X					X			X			X	X
^{Ghoshray (2013)}Ghoshray, 2013 Ghoshray, A. (2013). Dynamic persistence of primary commodity prices. American Journal of Agricultural Economics, 95(1), 153-164.	Bootstrap methods	24 commodity spot prices (18 agricultural)	1900-2008 (annual)			X			X			X			X			X			X			X	X
^{Karali and Power (2013)}Karali and Power, 2013 Karali, B., & Power, G. J. (2013). Short- and long-run determinants of commodity price volatility. American Journal Agricultural Economics, 95(3), 724-738.	Spline-GARCH model and SUR framework	11 commodity futures prices (5 agricultural)	1990-2005 (daily)	X					X	X					X			X	X					X	X
^{Khan (2014)}Khan, 2014 Khan, B. F. (2014). Determinants of futures price volatility of storable agricultural commodities: The case of cotton (Thesis in Agricultural and Applied Economics). Texas Tech University.	GARCH model	Cotton futures prices	2001-2010 (weekly)		X				X	X			X			X					X	X			X
^{He et al. (2014)}He et al., 2014 He, L. -Y., Yang, S., Xie, W. -S., & Han, Z. -H. (2014). Contemporaneous and asymmetric properties in the price–volume relationships in China's agricultural futures markets. Emerging Markets Finance and Trade, 50, 148-166.	Time series analysis	Soybean, soy meal, corn, hard wheat, strong gluten wheat, and sugar	Until June-2010 (daily)			X			X			X	X					X			X			X			X
^{Dawson (2015)}Dawson, 2015 Dawson, P. J. (2015). Measuring the volatility of wheat futures prices on the LIFFE. Journal of Agricultural Economics, 66(1), 20-35.	GARCH model	Wheat futures market	1996-2012 (daily)			X			X			X			X			X			X			X	X

	Soybeans		Corn
	R _t	\|R _t \|	R _t	\|R _t \|
Observations (n)	13,977	13,977	13,980	13,980
Mean (%)	0.0242^a a Statistically distinguishable from zero at 10%.	1.0157^a a Statistically distinguishable from zero at 10%.	-0.0064	0.9676^a a Statistically distinguishable from zero at 10%.
Median (%)	0.0461	0.6993	0.0000	0.6676
Maximum (%)	12.5279	15.0626	8.6618	10.4088
Minimum (%)	-15.0626	0.0000	-10.4088	0.0000
Std. deviation (%)	1.4586	1.0471	1.3868	0.9916
Skewness	-0.2465	2.4103	-0.0213	2.1846
Kurtosis	7.9065	13.8311	6.6350	10.1911
Jarque-Bera stat.	14,161.28	81,853.13	7697.60	41,242.95

Parameter	(I) Full sample		(II) 1959-1972		(III) 1973-1988		(IV) 1989-2004		(V) 2005-2014
	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.
Mean equation
Constant	0.0036	0.7941	-0.0075	0.6913	0.0109	0.8965	0.0631	0.2915	0.2357	0.0510
h _t	0.0205	0.1968	0.0559	0.1302	0.0056	0.8468	-0.0479	0.3735	-0.1184	0.1617
R(-1)	0.0017	0.8495	0.0028	0.8761	0.0013	0.9377	-0.0117	0.5004	0.0179	0.4047

Variance equation
Constant	0.0050	0.0008	0.0017	0.2298	0.0511	0.0000	0.0394	0.0000	0.0543	0.0000
ε²_t-1	0.0944	0.0000	0.0900	0.0000	0.1050	0.0000	0.0634	0.0000	0.0649	0.0000
D_t-1ε²_t-1	-0.0525	0.0000	-0.0690	0.0000	-0.0512	0.0000	-0.0375	0.0000	-0.0205	0.0581
GARCH(-1)	0.9300	0.0000	0.9400	0.0000	0.9132	0.0000	0.9446	0.0000	0.9306	0.0000
SE₁ (1º quarter)	0.0029	0.0000	0.0024	0.0003	-0.0027	0.6436	0.0046	0.0995	0.0147	0.1457
SE₂ (2º quarter)	0.0058	0.0000	0.0040	0.0000	0.0242	0.0016	0.0350	0.0000	0.0128	0.1758
SE₃ (3º quarter)	0.0029	0.0202	0.0044	0.0007	-0.0074	0.3607	-0.0063	0.1848	0.0312	0.0127
ME	-0.0003	0.0000	-0.0001	0.0091	-0.0016	0.0001	-0.0015	0.0000	-0.0015	0.0136
R-squared	-0.0002		-0.0005		0.0003		0.0004		0.0018
Adjusted R-squared	-0.0004		-0.0011		-0.0002		-0.0001		0.0010
S.E. of regression	1.4589		0.8296		1.8176		1.3408		1.6482
Durbin-Watson stat.	1.8892		2.0438		1.7614		1.9746		2.0020

Parameter	(I) Full sample		(II) 1959-1972		(III) 1973-1988		(IV) 1989-2004		(V) 2005-2014
	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.
Mean equation
Constant	0.0093	0.4471	0.0073	0.8301	-0.0106	0.8189	0.1604	0.0084	-0.1031	0.4503
h _t	-0.0146	0.5967	-0.0137	0.8078	0.0133	0.7641	-0.1603	0.0043	0.0564	0.4735
R(-1)	0.0401	0.0000	0.0294	0.1227	0.0585	0.0004	0.0481	0.0024	0.0247	0.2085

Variance equation
Constant	0.0197	0.0000	0.0415	0.0000	0.0365	0.0000	0.0221	0.0000	0.0259	0.2201
ε²_t-1	0.0875	0.0000	0.1516	0.0000	0.0917	0.0000	0.0477	0.0000	0.0436	0.0000
D_t-1ε²_t-1	-0.0192	0.0000	-0.0778	0.0000	-0.0074	0.5349	-0.0200	0.0008	0.0243	0.0199
GARCH(-1)	0.9210	0.0000	0.8513	0.0000	0.8992	0.0000	0.9528	0.0000	0.9245	0.0000
SE₁ (1º quarter)	-0.0026	0.0877	-0.0123	0.0000	-0.0046	0.4464	0.0012	0.6981	0.0554	0.0001
SE₂ (2º quarter)	0.0012	0.5344	-0.0108	0.0013	0.0066	0.2759	0.0328	0.0000	0.0858	0.0000
SE₃ (3º quarter)	0.0026	0.2613	0.0044	0.3236	0.0231	0.0231	-0.0067	0.1177	0.0477	0.0139
ME	-0.0005	0.0000	-0.0005	0.0000	-0.0007	0.0000	-0.0005	0.0001	0.0002	0.7026
R-squared	0.0025		-0.0009		0.0081		0.0047		0.0007
Adjusted R-squared	0.0024		-0.0015		0.0076		0.0042		-0.0001
S.E. of regression	1.3852		0.7851		1.4314		1.2752		1.9821
Durbin-Watson stat.	1.9715		2.0547		1.9181		1.9834		1.9958

	Summary statistics
	Mean	Median	SD
Alpha (α ₁ )
Soybeans	0.0796	0.0786	0.0265
Corn	0.0792	0.0774	0.0458
p-Value	0.3689	0.0000	0.0000

Beta (β)
Soybeans	0.9314	0.9329	0.0262
Corn	0.9004	0.9012	0.0453
p-Value	0.0000	0.0000	0.0000

Gamma (γ)
Soybeans	-0.0480	-0.0524	0.0279
Corn	-0.0228	-0.0285	0.0526
p-Value	0.0000	0.0000	0.0000

Half-life (ϑ)
Soybeans	65.94	21.24	2276.53
Corn	98.47	56.52	2827.86
p-Value	0.3123	0.0000	0.0000

	Alpha (α)			Beta (β)			Gamma (γ)			Half-life
	Mean	Med	SD	Mean	Med	SD	Mean	Med	SD	Mean	Med	SD
Soybeans
1959-72	0.0920	0.0942	0.0212	0.9402	0.9382	0.0177	-0.0689	-0.0682	0.0182	141.22	137.56	6233.85
1973-88	0.0899	0.0921	0.0252	0.9209	0.9245	0.0264	-0.0441	-0.0520	0.0255	135.90	50.48	2042.34
1989-04	0.0768	0.0830	0.0276	0.9329	0.9310	0.0320	-0.0611	-0.0622	0.0180	57.22	46.44	678.03
2005-14	0.0572	0.0578	0.0112	0.9383	0.9380	0.0126	-0.0155	-0.0146	0.0190	68.25	60.68	30.67

Comparison between periods (p-value) ^a a p-Value for the hypothesis test that the population statistics are equal.
1959-72 × 1973-88	0.0010	0.0151	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.9605	0.0000	0.0000
1959-72 × 1989-04	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.5399	0.3974	0.0000	0.0000
1959-72 × 2005-14	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0247	0.5570	0.0000	0.0000
1973-88 × 1989-04	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0203	0.0000	0.0000
1973-88 × 2004-14	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0966	0.0000	0.0000
1989-04 × 2004-14	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0012	0.4151	0.0000	0.0000

Corn
1959-72	0.1504	0.1467	0.0298	0.8458	0.8440	0.0351	-0.0832	-0.0802	0.0225	14.62	13.96	210.93
1973-88	0.0747	0.0751	0.0310	0.9195	0.9309	0.0316	-0.0181	-0.0215	0.0417	137.54	30.21	4036.63
1989-04	0.0693	0.0757	0.0271	0.9053	0.8954	0.0474	-0.0216	-0.0270	0.0203	45.84	24.18	166.50
2005-14	0.0418	0.0425	0.0349	0.9084	0.9110	0.0298	0.0189	0.0098	0.0723	27.05	18.66	22.29

Comparison between periods (p-value) ^a a p-Value for the hypothesis test that the population statistics are equal.
1959-72 × 1973-88	0.0000	0.0000	0.0330	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.1596	0.0000	0.0000
1959-72 × 1989-04	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
1959-72 × 2005-14	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0033	0.0000	0.0000
1973-88 × 1989-04	0.0000	0.4668	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.1496	0.0001	0.0000
1973-88 × 2004-14	0.0000	0.0000	0.0000	0.0000	0.0000	0.0014	0.0000	0.0000	0.0000	0.1695	0.0000	0.0000
1989-04 × 2004-14	0.0000	0.0000	0.0000	0.0029	0.0029	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000

Departamento de Administração da Faculdade de Economia, Administração e Contabilidade da Universidade de São Paulo Avenida Professor Luciano Gualberto, 908, sala F184, 05508-900 São Paulo / SP Brasil, Tel./Fax 55 11 3818-4002 - São Paulo - SP - Brazil
E-mail: rausp@usp.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] * Corresponding author at: Unicamp/Instituto de Economia, CP 6135, sala 68, CEP 13085-970 Campinas, SP, Brazil. E-mail:rlanna@unicamp.br (R.L. Silveira).