Abstract
This paper examines the inflation-hedging ability of common stocks in the long run for emerging market countries using monthly data on stock and goods prices over the period 1982:01–20,016:01. Johansen’s (J Econ Dyn Control 12:231–254, 1988) method of cointegration is employed for 28 countries for which the order of integration of the underlying series is the same, and Pesaran et al.’s (J Appl Econom 16:289–326, 2001) autoregressive distributed lag (ARDL) bounds test is used for 18 countries for which the order of integration is not the same. In only 10 cases is the long-run relationship established between stock and goods prices when the former test is used, whereas such a relationship is established in 7 cases when the latter test is used. The results of error-correction representations normalized on stock prices indicate that stock prices take a long time to return to their long-run equilibrium relation with goods prices. Overall, common stocks provide a good hedge against inflation in the long run in more than one-third of the cases examined. One implication that emerges from these results is that in the majority of the countries, monetary authorities are not able to control inflation by reducing the nominal interest rate. Another implication is that the monetary growth is not the key factor determining the long-run inflation rate in these countries.
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Notes
An enormous work has also been carried out on the inflation hedging ability of other financial (e.g. bonds and Treasury bills) and real assets (e.g. gold and real estate). For a recent, detailed and comprehensive survey on this issue, see Arnold and Auer (2015).
It is argued that average expected returns and their volatility have historically been higher in equity markets of the emerging market countries. However, given the low correlation between equity returns of developed and emerging market countries, the inclusion of emerging market assets in a mean-variance efficient portfolio is likely to significantly reduce portfolio volatility and increase expected return.
The exceptions include such countries as Bahrain (2007:08–2016:01), Qatar (2009:01–2016:01), United Arab Emirates (2007:01–2016:01), Slovenia (2006:04–2016:01), Chile (2009:01–2016:01) and Venezuela (2007:12–2016:01) for which the sample starts from the period when the underlying crises had already erupted but not ended.
These inconsistent findings between the two studies may be attributed to differences in the specifications used to test the long-run Fisher relationship between stock prices and goods prices. Rather than a bivariate relationship between stock prices and goods prices, Ely and Robinson (1997, p. 148) examine a multivariate relationship that incorporates real output and money as additional variables due to their potential effect on the relationship between stock prices and goods prices.
Luintel and Paudyal (2006) use monthly data for the U.K. retail price index, the Financial Times All-Share Index and seven equally weighted industry portfolios (constructed for consumer goods, financial institutions, general manufacturing, investment trusts, mineral extraction, services and utilities) over the period 1955–2002. In five of the seven industry groups, the price elasticity is greater than 1.0; in one industry (mineral extraction) it is below 1.0; and in one (investment trusts) it is exactly 1.0.
The two-regime TVECM provides a framework for testing the long-run relationship between stock and goods prices, allowing for asymmetric adjustment of stock returns and expected inflation to the long-run equilibrium.
The MSCI Emerging Market Index lists 25 countries. Included in these are: Brazil, Chile, China, Colombia, Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Morocco, Qatar, Peru, the Philippines, Poland, Russia, South Africa, South Korea, Taiwan, Thailand, Turkey, and United Arab Emirates.
The sample period varies between 1957:02 and 1996:03 for such countries as Chile (1974:0–1996:03), Germany (1970:01–1996:03), Israel (1980:03–1996:03), Korea (1978:01–1996:03), Mexico (1984:01–1996:03), New Zealand (1961:01–1996:01), Peru (1989:01–1996:03), Portugal (1988:01–1996:03), Sweden (1957:02–1996:04) and Luxemburg (1980:01–1996:03).
While the sample period for most of the countries starts with January 2010, the starting point varies from country to country, between January 1970 and January 2005.
One reason for using monthly data is that the majority of studies utilized the monthly frequency of data on stock and goods prices. Another reason is to increase the number of sample observations for employing cointegration analysis and to make our results comparable with those of other studies in the underlying area.
Due to the unavailability of data on their stock markets, other countries from Central Asia (e.g., Kyrgyzstan, Tajikistan and Uzbekistan) are not included in the dataset.
Arguably, Fisher (1930) was the first economist to develop the standard hypothesis relating the nominal interest rate to expected inflation and real interest rates. However, Humphrey (1983, p. 2-6) argues that the proposition that the nominal interest rate equals the real interest rate plus the expected inflation rate has a longer history that dates back more than 240 years to the writings of William Douglass, Henry Thornton, John Stuart Mill, Jacob de Hass, Alfred Marshall and J.B. Clark. Moreover, it has been argued that the claim is disproved by Fisher (1896) himself when he noted that he was by no means the first to present that analysis.
When applied to Treasury bills and deposit markets (see, inter alia, Fama and Schwert 1977; Rose 1988; MacDonald and Murphy 1989; Mishkin 1992; Balparda et al. 2016) the FH implies that the one-period nominal interest rate on a Treasury bill (a bank deposit) should fully reflect the inflation rate anticipated by market agents such that the ex ante real rate of return remains constant and independent of anticipated inflation over time. This may be defined as follows:
\( {i}_t={r}_{t+1}^e+\Delta {p}_{t+1}^e \)
The OLS estimate of the slope coefficient of the Fisher relation (β) may be greater than 1.0, as demonstrated by Darby (1975), or less than 1.0, as demonstrated by Mundell (1963) and Tobin (1965). Consequently, if β≥1.0 (β ≤1.0), then common stocks may provide a superior (partial) hedge against inflation. For the GFH to hold precisely, the expected (current) real stock returns should also be independent of the expected (current) inflation rates. The relationship between real stock returns (rsrt) and inflation rates (∆pt) can be obtained by substituting the Fisher equation (∆st = rsrt + ∆pt) in equation (7) and rearranging the resulting expression as follows: rsrt = α + b∆pt + εt, where b = (1 − β) should be equal to zero if GFH holds (see, for example, Rushdi et al. 2012).
Moosa and Bhatti (1997, p. 149) argue, “If normality, serial correlation or heteroscedasticity statistics are significant, the Phillips-Perron procedure should be adopted.”
The time trend can be included in the regression model to allow the alternative of trend stationarity. In all cases, the null of hypothesis of non-stationarity (unit root) is rejected when the test statistic is significantly negative and greater in absolute terms than the critical value at the appropriate significance level. Because the PP test statistics are asymptotically equivalent to the corresponding Dickey-Fuller statistics, the critical values to be used in conjunction with the Phillips-Perron test are identical to those tabulated in Fuller (1976) and MacKinnon (1996). Both the test statistics calculated from the regression model with a time trend is valid (in the sense that it accurately confirms or refutes stationarity) only if the coefficient of the time trend is zero, but if there is a significant time trend, then the underlying series will not be stationary even if the coefficient of the lagged value of the series is less than zero. See, for example, Moosa and Bhatti (1997, p. 144-151) for a detailed discussion of these unit root tests.
The optimal number of lags in the ADF test is determined based on the Schwartz Information Criterion (SIC).
Tests will be not applied to the first difference of a series if it turns out to be I(0) in level. This is why the outcomes for level and first difference are reported for those series which are I(1) in level.
An important step in the application of this technique is fixing the optimal lag level. The lag level was set to 4, which in almost all cases removes serial correlation in the residuals of the VAR model. Moreover, an unrestricted constant specification is used to account for the trending behavior of the underlying series (for more details on the specification of deterministic terms, see Johansen and Juselius (1990) and Johansen (1995)).
Barnes et al. (1999, p. 747) “found that an ARMA (2, 1) process for the error term provided ‘the best fit.’”
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We are indebted to an anonymous referee for her/his comments and suggestions that substantially improved both the Model and Method section and the discussion of our results. Any remaining shortcomings are solely our responsibility.
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Al-Nassar, N.S., Bhatti, R.H. Are common stocks a hedge against inflation in emerging markets?. J Econ Finan 43, 421–455 (2019). https://doi.org/10.1007/s12197-018-9447-9
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DOI: https://doi.org/10.1007/s12197-018-9447-9