commodity returns and capital asset pricing

Commodity Returns and Capital Asset PricingAuthor(s): Duncan M. Holthausen and John S. HughesSource: Financial Management, Vol. 7, No. 2 (Summer, 1978), pp. 37-44Published by: Wiley on behalf of the Financial Management Association InternationalStable URL: http://www.jstor.org/stable/3665242 .

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Commodity Returns and

Capital Asset Pricing

Duncan M. Holthausen and John S. Hughes

Duncan M. Holthausen is Associate Professor in the Department of Economics and Business at North Carolina State University at Raleigh. John S. Hughes is Associate Professor at the Graduate School of Business Administration at Duke University.

Introduction

The capital asset pricing model has most often been applied to securities markets. Recent studies have used models of capital market equilibrium to examine also the pricing of commodity contracts. Dusak [3] first applied the capital asset pricing model to commodity futures prices for corn, wheat, and soybeans. More recently, Black [1] has derived expressions for the expected change in the price of futures contracts and expressions for the values of forward commodity contracts and commodity options. To date, however, there has not been a test of the capital asset pricing model for spot commodity prices. The spot price of a commodity is the price at which it can be bought or sold for immediate delivery.

There are some reasons why the capital asset pricing model may not apply as well to spot commodity prices as it does to security prices. Because most commodities are produced only during certain seasons

0 1978 Financial Management Association.

while consumption is fairly uniform, available supplies are not constant over time. In addition, commodities may be held in inventory as a convenience to meet production needs, suggesting another element of return not ordinarily addressed in capital asset theory. Since a commodity has storage costs, price changes may include a return to storage. As a result, return distributions may not be stationary. Whether such commodity return characteristics are serious enough to compromise the capital asset pricing model remains an empirical question. The purpose of this paper is to offer evidence on the descriptive validity of certain assumptions upon which the capital asset pricing model is usually predicated and on the return generating process which the model implies. In general, our results do not support the model.

Although this study is descriptive rather than nor- mative, it should still be of some use to financial managers who are obliged to trade in commodity

37



FINANCIAL MANAGEMENT/SUMMER 1978

markets. It would be interesting to know if the

assumptions of market efficiency or the equilibrium relationship between expected return and nondiversifiable risk hold as well in commodity markets as they appear to in security markets. If commodity markets are inefficient, or if expected returns depend on factors other than market risk, then these conditions may suggest the existence of advantageous trading strategies. On the other hand, if capital asset pricing theory does apply well to commodity markets, then that may suggest that opportunities for trading advan-

tages do not exist. In either case, financial managers should benefit by a further understanding of how com-

modity prices in fact behave.

Research Methodology The capital asset pricing model in risk premium

form may be stated as follows:

ai - r = (am - r), (1)

where

Ca, r

Oam

/i

- the expected return on the ith commodity, = the return on the risk-free asset, - the expected return on the market portfolio,

'im 2 '

am

aim the covariance of returns on the ith commodity with returns on the market portfolio, and

2am = the variance of returns on the market portfolio.

Sufficient assumptions for this condition are that investors prefer more wealth to less, they are risk- averse, and they are permitted unrestricted short sales; capital markets are perfect; returns on risky capital assets (e.g., commodities) are multivariate normal; a riskless asset exists with a constant return; trading takes place continuously through time; and returns on risky capital assets follow a continuous time random walk. (Implicit in the random walk assumption is that

expected returns and variances are constant or func- tions of price alone. This condition, however, will hold only if supplies are constant. See Merton [7, p. 873].)

With the last two assumptions included, the parameters given in Equation (1) should be interpreted as instantaneous (i.e., the limit of the parameters per unit time as the unit of time goes to zero). Equation (1) describes an equilibrium condition between expected returns and systematic risk, or, alternatively, between expected returns of individual assets and the market. An econometric analogue to Equation (1) can

be specified as follows:'

Ri, t - r = ?i + fi (Rm, t - r) + i, t, (2)

where

Ri, t the return on the ith commodity in period t, Rm, t the return on the market in period t,

7i _ a constant, and Ei, t a normal random variate with mean zero,

constant variance, and zero serial covariance.

To test the model, monthly returns on 19 leading commodities were determined from prices obtained from the Journal of Commerce and the Wall Street Journal for the years from 1970 through 1974. Returns were calculated as the log of the ratio of successive prices.

Ideally, the market portfolio index should include

corporate securities, personal assets such as real es- tate, and assets held by unincorporated businesses. Stocks of commodities held by corporations would be

implicitly included in a securities market index such as the Standard & Poor's Composite Index of 500 firms, which we have used. But, stocks held by individuals and unincorporated businesses need to be explicitly added to the Standard & Poor's Index. Unfortu- nately, information on these stocks is not readily available, forcing us to use proxy indices based on all commodities. Had we known the appropriate weights to place on the S&P Index relative to the commodity index, we could have combined the two into a single securities-commodities index. Since we did not, we included both the S&P Index and a commodity index as separate regressors in each regression.

We examined three alternative commodity indices: the Journal of Commerce (JOC) Index of spot prices for 30 commodities, the Commodity Research Bureau (CRB) Index of futures contract prices for 27 commodities, and our own geometric mean (GM) index computed for each commodity by taking the geometric mean returns on 18 spot commodities (ex- cluding the commodity being studied to eliminate any possible bias).

Returns on the riskless asset were determined from discounts quoted in the Wall Street Journal for 13- week U.S. Treasury Bills maturing in approximately one month. These returns were also calculated as the log of the ratio of successive prices with the prices im- puted from the quoted discounts. The assumption that

1. This derivation follows Merton [6]. See the appendix.

38



D. M. HOLTHAUSEN AND J. S. HUGHES/COMMODITY RETURNS

returns are a random walk was tested by estimating serial correlation coefficients up to and including six- month lags. The assumption of normality was tested by performing chi-square goodness-of-fit tests on the decile distributions of actual sample returns compared with corresponding theoretical normal returns. Other assumptions on investor behavior and market characteristics do not lend themselves to testing from return data alone.

Time series regressions were run for the 19 commodities from return observations over the five-year period described above. These regressions were of the form given by Equation (2) except that returns on two indices independently representing security and commodity markets were used in place of the single market index shown. A separate regression was performed using returns from each of the three commodity indices combined with returns from the security market index. In addition, for cross-sectional work, regressions were run on the security market index by itself and each commodity market index by itself. Finally, cross-sectional regressions of sample mean returns on various combinations of beta es- timates, f, and sample standard deviations were run

as a further test of the capital asset pricing model rela- tion between expected return and risk. While this procedure introduces an error in the variables problem, there should still be value in comparing the results across separate regressions. Another limitation of this analysis is that it is performed only on es- timates obtained from one underlying time series regression. As a result, no test for stability is possible.

Empirical Findings

Exhibit 1 gives the serial correlation coefficients for monthly returns with lags from one to six months. Ex- cept for sugar, which displays coefficients significantly different from zero across all lags, the results support the random walk assumption. During the period of the study sugar prices rose dramatically and consistently for almost a year because of a supposed shortage, then prices generally fell.2 This may explain the unusual results.

2. Sugar prices rose more than 400% between January and November 1974, after which they declined to original levels by late 1975. There was some question whether this rise was due to a shortage or not. See the Wall Street Journal, December 23, 1974, p. 25, and August 13, 1975, p. 18, for example.

Exhibit 1. Estimated Serial Correlation Coefficients for Monthly Returns on 19 Commodities

Commodity Order of Correlation Coefficients

1 2 3 4 5 6

1. Broilers -.141 -.150 -.162 -.086 .290* .035 2. Cattle .157 -.185 -.257 -.300 .113 .355* 3. Cocoa .106 -.005 .064 -.126 -.277* .031 4. Coffee .056 .057 .053 .084 -.107 -.084 5. Copper** .180 -.022 -.030 -.201 .054 -.047 6. Corn -.114 .109 -.269* .046 -.013 .209 7. Cotton .217 -.124 .240 .390* -.066 -.117 8. Eggs -.153 .001 -.131 .082 -.107 -.015 9. Flaxseed .322* .015 -.113 .202 .265* .182

10. Hogs -.032 -.125 -.128 -.059 .226 .152 11. Oats -.145 -.044 -.168 .049 .082 .061 12. Porkbellies .008 -.011 .085 -.039 -.036 .202 13. Rye .315* -.199 -.174 .088 .173 -.040 14. Silver -.026 -.001 .058 -.080 -.033 -.098 15. Soy meal -.033 .171 -.209 -.008 -.025 .107 16. Soy oil -.227 .135 -.216 -.115 .243 -.017 17. Soybeans -.096 .115 .052 -.215 -.135 .017 18. Sugar .317* .277* .583* .355* .432* .544* 19. Wheat .160 -.062 -.093 .010 -.040 -.033

*Significant at the 5% level. **Based on London Metals Exchange spot prices.

(The domestic spot market for copper is dominated by a few large producers who set the copper spot price. See McNicol [5] for a good description of this market. We have therefore used the LME spot price for copper, since that is a free market price.)

39




Exhibit 2 presents the results of chi-square goodness-of-fit tests for normality of commodity return distributions. For each commodity, the sample mean and standard deviation were used to find the points which would divide a theoretical normal distribution with that mean and standard deviation into ten parts with equal frequencies in each decile. The actual frequencies in each theoretical decile are given in Exhibit 2 for each commodity; these can be compared visually to the equal frequency expectation. In seven cases, the hypothesis of normal return distributions can be rejected at the 5% level. Even the 12 distributions not significantly different from normal show a very consistent non-normal pattern. Almost all the distributions are peaked, skewed to the left, and thin-tailed in comparison with a normal distribution.

There are two other possible distributions that have been explored in the commodities literature which are consistent with the capital asset pricing model; these are the stable Paretian and subordinated normal distributions. (See Dusak [3] for the stable Paretian and Clark [2] for the subordinated normal distribution.) Although both are characterized by peakedness, neither is skewed or thin-tailed. Hence, the observations reported here do not fit the assumption of nor-

mality of return distributions very well, but neither do they strongly support the principal alternative hypotheses of stable Paretian and subordinated normal distributions.

Panels A, B, and C of Exhibit 3 give the estimated parameters of the multiple regressions using the three alternative indices for the commodity market factor and the Standard & Poor's Composite 500 Index for the security market factor. Although t-statistics are reported, they must be interpreted carefully in light of the non-normality of some of the distributions. None- theless, the t-statistics do at least serve as a rough test of the significance of the estimated coefficients.

Perhaps the most striking result in Exhibit 3 is the importance of the commodity index relative to the security index. Out of 19 commodities, 14 to 15 have large and seemingly significant coefficients for the commodity market index, while only 4 at most have significant coefficients for the security market index. (Again, the results for sugar are anomalous, with only the intercept term significant and the explained variance being the lowest for any commodity.) One cannot attribute these results to the presence of each commodity in the commodity index because the same relative importance of the commodity factor is found

Exhibit 2. Chi Square Tests of Decile Frequencies of Actual Returns on 19 Commodities

Commodity Decile Frequencies t x2

.1 .2 .3 .4 .5 .6 .7 .8 .9 1.0

1. Broilers 5 5 5 8 10 7 7 5 1 6 8.6271 2. Cattle 3 8 4 7 11 4 10 2 4 6 14.0508 3. Cocoa 6 4 3 10 9 5 5 3 9 5 9.9831 4. Coffee 2 3 4 5 21 8 9 0 5 2 54.3898* 5. Copper 5 3 7 8 9 9 4 3 6 5 7.9492 6. Corn 5 6 2 11 7 7 9 1 5 6 13.3729 7. Cotton 6 3 3 5 10 16 4 4 3 5 25.9153* 8. Eggs 5 7 11 2 6 3 6 6 8 5 9.6441 9. Flaxseed 4 1 6 12 11 9 4 4 4 4 19.4746*

10. Hogs 5 6 3 9 8 7 7 5 6 3 5.9153 11. Oats 6 6 6 6 6 6 7 5 3 8 2.5254 12. Porkbellies 4 6 7 5 9 10 4 6 3 5 7.6102 13. Rye 5 2 5 7 10 12 4 4 5 5 13.7119 14. Silver 5 6 6 4 12 5 7 7 2 5 10.3220 15. Soy meal 5 2 7 5 13 8 10 2 2 5 20.4915* 16. Soy oil 5 5 7 6 9 6 3 7 5 6 3.8814 17. Soybeans 5 2 3 10 11 10 8 4 1 5 19.8136* 18. Sugar 0 3 14 19 6 5 3 1 2 6 55.7458* 19. Wheat 2 4 5 12 13 7 2 6 3 5 22.5254*

*Significant at the 5% level. tThere would be 5.9 observations per cell if returns were normally distributed.

40




even in the GM index that deletes the commodity for which each regression is run. There are at least three interpretations of this result. For one, it may be taken as evidence that the security market index is not a good proxy for a global capital market index in the study of commodities.

Alternatively, one may regard the commodity index as something like an industry index in studies of securities (see King [4], for example). While an industry index has little predictive ability ex ante, it is highly correlated with the returns to each firm in the industry ex post. Hence, even though it appears as a significant variable in ex post regressions, it need not be important in the determination of ex ante equilibrium prices. We do not feel that this explana- tion applies very strongly in the case of the commodity index, however. While firms may be classified into roughly homogeneous industries, the commodity "industry" is quite heterogeneous. Factors affecting grains differ from those affecting livestock and metals, for example. Even among grains, differences in grow- ing seasons, regional weather patterns, and demand are sufficiently large to argue against a strong industry effect.

Finally, the commodity index may act as a proxy for some stochastic state variable that conditions the return generating process. A model of such a process has been reported by Merton [7]. In particular, the commodity index may be a partial proxy for stochastic inventory levels or some other variable associated with a fluctuating investment opportunity set.

We are not able to discriminate among these alternative interpretations, and in fact all may be partial explanations. What needs to be done is to construct a better global market index and to include inventory levels explicitly in a Merton-type model.

If the capital asset pricing model is the appropriate one to use in studying commodity returns, we would expect mean returns to be positively associated with nondiversifiable risk as measured by the betas of the various indices used. To test this, we ran cross- sectional regressions for all combinations of estimated beta coefficients and standard deviations against mean returns. Contrary to our expectations, the coefficients of the beta terms are never significant in explaining variations in mean returns (see Exhibit 4). Only the intercept terms are sometimes significant.

Generally, the highest remaining t-statistics relate to the coefficients of the standard deviation, which is also at odds with the prior theory. These results should be interpreted cautiously, however, due to the con-

siderable problem of errors in the variables. Without sufficient numbers of commodities to construct portfolios, this problem seems unavoidable.

Summary The results of this research suggest that the capital

asset pricing model may be less well suited to commodity markets than it is to security markets. The assumption of normality of returns is clearly violated

by many of the commodities studied. The usual

security market index is probably a poor proxy for a

global market index for commodities. Finally, com-

modity returns do not appear to be highly related to measures of nondiversifiable risk.

Subject to the limitations of this study, these results would imply that financial managers may find it desirable to include commodities in a portfolio of risky assets, since it appears that returns have not been fully adjusted for diversifiable risk. Also, since many commodities are traded in futures markets, and betas for futures contracts are related to commodity betas (see Black [1]), futures contracts might also be considered for inclusion in portfolios.

Further research in this area will be necessary to give more definitive answers to the questions raised here. One of the first priorities must be the construc- tion of a market index that correctly weights securities and commodities. In addition, storage costs, convenience yields, and fluctuating supplies should be considered. Such refinements may give results different from this study. At the present time it does not appear that the extant theory is either entirely appropriate or especially rich in explaining commodity price behavior.

References 1. Fischer Black, "The Pricing of Commodity Contracts,"

Journal of Financial Economics (January/March 1976), pp. 167-79.

2. Peter K. Clark, "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica (January 1973), pp. 135-55.

3. Katherine Dusak, "Futures Trading and Investor Returns: An Investigation of Commodity Market Risk Premiums," Journal of Political Economy (Novem- ber/December 1973), pp. 1387-1406.

4. Benjamin F. King, "Market and Industry Factors in Stock Price Behavior," Journal of Business (January 1966 supplement), pp. 139-90.

5. D. L. McNicol, "The Two-Price System in the Copper Industry," The Bell Journal of Economics (Spring 1975), pp. 50-73.

41




6. Robert C. Merton, "A Dynamic General Equilibrium Model of the Asset Market and Its Application to the Pricing of the Capital Structure of the Firm," Massa- chusetts Institute of Technology, Sloan School Research Paper (December 1970).

7. Robert C. Merton, "An Intertemporal Capital Asset Pricing Model," Econometrica (September 1973), pp. 867-87.

Appendix

A return-generating process consistent with these

assumptions can be defined as follows:

dPi P a dt + ai dzi, Pi (A-l)

where dzi = a standard Weiner process. The normality of return distributions assumption implies a lognormal distribution for spot commodity prices. Therefore, integrating Equation (A-l) over the time interval h leads to:

P, (t+h) = Pi (t) exp [(ai - (1/2)a)h + aiz (t;h)], (A-2)

where zi (t;h) - t+h

dzi and thus is normal with t

mean zero and variance h. Now define commodity returns over the time interval h as

Ri (t+h) - log [Pi (t+h)/Pi (t)] (ai - (1/2)ua)h + aizi (t;h). (A-3)

Then, defining market returns, Rm (t+h), similarly to R, (t+h), solving for ai and am, and substituting in

Equation (1), the following ex post model can be derived:

R, (t+h) - rh = 3i [Rm (t+h) - rh] + yih + Ej (t;h), (A-4)

where yi _ (1/2) (ia2 - a2),

ei (t;h) a, / - p,2m yi (t;h), yi (t;h) a normal random variate with mean

zero and variance h, and Pim = the correlation coefficient between com-

modity and market returns.

Equation (A-4) is the basis for our ordinary least

squares (OLS) regressions.

Exhibit 3. Estimated Coefficients from Time Series Regressions on 19 Commodities with Various Commodity Indices

Security Commodity Coefficient Index Index of Multiple

Constant Coefficient Coefficient Correlation

Panel A y t $s&P t SJ&oc t R2

1. 2. 3. 4. 5. 6. 7. 8. 9.

10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

Broilers Cattle Cocoa Coffee Copper Corn Cotton Eggs Flaxseed Hogs Oats Porkbellies Rye Silver Soy meal Soy oil Soybeans Sugar Wheat

.0008 -.0035

.0118

.0005 -.0082

.0040 -.0038 -.0110

.0079 -.0049

.0093 -.0047

.0018

.0035 -.0129

.0036 -.0007

.0280

.0064

.051 -.469

.758

.072 -.553

.354 -.335 -.622 1.139 -.310

.917 -.273

.163

.320 -.647

.187 -.041 2.906*

.650

.0032

.2084

.2850

.0746

.1459 -.2902 -.0666 -.4968

-.2902 .0615 .0232

-.0353 .0387 .1182

-.7098 -.6264 -.6672

.0935

.2104

.010 1.446 .958 .601 .515

-1.354 -.303

-1.469 -2.178*

.203

.119 -.108

.186

.568 -1.860 -1.716* -2.179*

.507 1.119

.3554

.7197

.0392

.0715

.2638 1.0132 1.1223 .9107

1.3309 .9931 .7263 .8489

1.8582 1.2407 1.5378 1.4966 1.2729 .0473

1.8291

.756 3.224*

.085

.372

.601 3.052* 3.299* 1.738* 6.449* 2.121* 2.415* 1.672* 5.767* 3.854* 2.602* 2.648* 2.684*

.166 6.280*

.0104

.1972

.0170

.0099

.0124

.1597

.1652

.0792

.4446

.0782

.0977

.0484

.3807

.2226

.1457

.1428

.1658

.0055

.4356

42




t S&P t $CRB

1. Broilers 2. Cattle 3. Cocoa 4. Coffee 5. Copper 6. Corn 7. Cotton 8. Eggs 9. Flaxseed

10. Hogs 11. Oats 12. Porkbellies 13. Rye 14. Silver 15. Soy meal 16. Soy oil 17. Soybeans 18. Sugar 19. Wheat

-.0040 -.275 .0064 .023 .9178 3.044* .1443 -.0028 -.385 .2422 1.712* .5317 3.501* .2194

.0066 .446 .2732 .965 .6932 2.280* .1018

.0000 .003 .0766 .624 .1185 .899 .0217 -.0101 -.705 .1524 .551 .4778 1.610 .0507

.0021 .222 -.2499 -1.356 1.1044 5.581* .3726

.0023 .189 -.0005 -.002 .1811 .708 .0090 -.0126 -.752 -.4607 -1.428 .9935 2.869* .1551

.0118 1.459 -.2209 -1.419 .6515 3.901* .2361 -.0079 -.561 .0977 .360 1.2414 4.260* .2502

.0085 .898 .0534 .294 .7270 3.728* .2034 -.0075 -.472 -.0050 -.016 1.0912 3.331* .1679

.0063 .531 .1332 .582 1.0219 4.161* .2441

.0038 .375 .1741 .897 1.0274 4.928* .3150 -.0177 -1.058 -.6540 -2.037* 1.9342 5.613* .3900 -.0024 .162 -.5755 -2.002* 2.0486 6.641* .4637 -.0056 -.448 -.6236 -2.588* 1.7258 6.672* .4786

.0275 2.885* .0943 .515 .1007 .512 .0097

.0108 .992 .3033 1.444 1.0097 4.480* .2899

Panel C t iS&P t GM t R2

1. Broilers -.0021 -.145 .1263 .440 .8970 2.733* .1197 2. Cattle -.0025 -.349 .3313 2.393* .6326 4.073* .2667 3. Cocoa .0095 .634 .3434 1.167 .4470 1.359 .0488 4. Coffee .0004 .067 .0854 .628 .0889 .716 .0144 5. Copper -.0079 -.545 .1913 .674 .2434 .774 .0167 6. Corn .0044 .454 -.1127 -.592 1.1380 5.186* .3400 7. Cotton .0015 .123 .0459 .194 .3821 1.449 .0368 8. Eggs -.0107 -.625 -.3567 -1.079 .9233 2.431* .1229 9. Flaxseed .0135 1.652 -.1416 -.890 .6486 3.621* .2125

10. Hogs -.0069 -.498 .2928 1.090 1.4450 4.605* .2803 11. Oats .0105 1.090 .1461 .776 .6778 3.194* .1582 12. Porkbellies -.0071 -.465 .1697 .568 1.3556 3.888* .2156 13. Rye .0090 .738 .2714 1.141 .9865 3.626* .1980 14. Silver .0076 .687 .2875 1.324 .7635 3.109* .1602 15. Soy meal -.0143 -.809 -.4574 -1.332 1.9070 4.618* .3086 16. Soy oil .0022 .136 -.3518 -1.140 2.1093 5.648* .3884 17. Soybeans -.0021 -.162 -.4409 -1.714* 1.7627 5.756* .4112 18. Sugar .0287 3.027* .0876 .473 -.0762 -.378 .0076 19. Wheat .0141 1.231 .4376 1.954* .8947 3.513* .2084

*Significant at the 5% level.

Panel B y

43




Exhibit 4. Estimated Coefficients from Cross-sectional Regressions of Mean on Various Combinations of Betas and Standard Deviation

Estimated Coefficient of Regressor

Constant t OS&P t J3oc t 1CRB t fGM t a t R2

1. .0174 1.999* -.0080 -.948 -.0950 -1.108 .0797 2. .0147 1.652 -.0058 -.680 .0043 1.062 -.1103 -1.288 .1597 3. .0193 2.219* -.0029 -.317 .0083 1.217 -.1887 -1.667 .1655 4. .0184 2.048* -.0050 -.548 .0044 .771 -.1390 -1.258 .1057 5. .0132 1.768* -.0497 -.701 .0281 6. .0044 1.113 .0041 1.096 .0660 7. .0068 1.626 .0015 .388 .0088 8. .0076 2.047* .0006 .169 .0017 9. .0045 1.085 -.0020 -.032 .0039 .946 .0667

10. .0073 1.419 -.0017 -.178 .0008 .154 .0108 11. .0087 1.849 -.0038 -.404 -.0004 -.097 .0113

*Significant at the 5% level.

American Journal of Edited by V. James Rhodes, University of Missouri-Columbia

Agricultural Economics Published by the American Agricultural Economics Association

May 1978

Articles: Alaouze, Watson, and Sturgess, "Oligopoly Pricing in the World Wheat Market"; Eckstein and Heien, "The 1973 Food Price Inflation"; King and Byerlee, "Factor Intensities and Locational Linkages of Rural Consumption Patterns in Sierra Leone"; Spitze, "The Food and Agriculture Act of 1977." Notes: Ladd and Gibson, "Microeconomics of Technical Change"; Mercer and Morgan, "Measurement of Economic Uncertainty in Public Water Resource Development." Proceedings of the AAEA 1977 winter meeting, three sessions: "Contemporary Issues in Natural Resource Economics," "Appropriate Technology for U.S. Agriculture-Are Small Farms the Coming Thing?" and "The Economics of Rural Household Behavior." Plus more Articles, Notes, Book Reviews, News.

Annual membership dues (including Journal) $25; Annual subscription rate $35; Individual copies $10. Contact John C. Redman, Agricultural Economics, University of Kentucky, Lexington, Ky, 40506. Published in February, May, August, November, and December.

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commodity returns and capital asset pricing

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