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Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy

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Abstract

Economic theory predicts that, in a small open economy, the dynamics of the real price of a commodity should be linked to a large-country real interest rate and fluctuations of the real exchange rate. Using data for Australia, we test this prediction using an out-of-sample forecasting experiment. We evaluate the economic value-added of out-of-sample forecasts by means of a behavioral approach that takes into account that a forecaster may have an asymmetric loss function.

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Notes

  1. For earlier research on the real interest rate as a determinant of the price of gold, see Diba and Grossman (1984), Barsky and Summers (1988), and Pindyck and Rotemberg (1990).

  2. Other recent applications of the forecasting approach include Alcock and Gray (2005), Hartmann et al. (2008), and Sarno and Valente (2009). Although we use the forecasting approach also used by Pierdzioch et al. (2014a, b), it should be emphasized that their results cannot be directly compared with our results. First, they study nominal gold-price fluctuations from a US-based perspective. We, in contrast, study fluctuations of the real gold price using data for Australia. Second, sample periods differ across studies. For example, Pierdzioch et al. (2014a) study monthly data for the sample period 1987\(-\)2012. We study quarterly data for the sample period 1977\(-\)2014.

  3. An increase in the foreign real interest rate should result in a higher expected rate of change of the gold price. Hence, as in the model studied by Frankel (2008, 2014), a higher foreign real interest rate should result in a lower level of the real gold price.

  4. The forecasting models always include a constant.

  5. If more than one model maximizes the proportion of correct in-sample forecasts, we use the ACD model-selection criterion to select a single forecasting model.

  6. When computing the ACD criterion, we use a logistic function to transform the ACD model-selection criterion so as to decrease (increase) the weight attached to forecasts implied by forecasting models with a low (high) ACD model-selection criterion.

  7. The behavioral approach could easily be extended to study some other widely studied loss function like the linex loss function. We specify the criterion in terms of the loss function studied by Elliott et al. (2005, 2008) because, in recent research on gold-price fluctuations, Pierdzioch et al. (2014b) use such a specification.

  8. See http://research.stlouisfed.org/fred2/. In case data are available at a higher than quarterly frequency, we use quarterly averages. We use revised macro data because the purpose of our forecasting experiment is to test the model of gold-price determination outlined in Sect. 2. Lettau and Ludvigson (2009, pp. 647–648) argue that revised macro data should be used to study equilibrium asset-pricing models, while real-time macro data should be used to track a practitioner’s real-time information set.

  9. All graphs and computations were coded up using the free R programming environment (R Core Team 2015).

  10. Results for other training periods (not reported in Table 2, but available from the authors upon request) are similar. Because the choice of the training period is somewhat arbitrary, we shall present in Sect. 4.3 results for alternative training periods.

  11. We use the length of the training period to fix the length of the rolling-estimation window. Because a rolling-estimation window implies that the number of data used for estimation is relatively small given that we study quarterly data, we present in Fig. 5 only results for a forecast-averaging criterion and window lengths of 15 and 20 years.

  12. For an extension that accounts for potential nonlinearities triggered by financial crises, see Hartmann et al. (2008). See Minford and Srinivasan (2008) for an analysis of the observational equivalence of nonlinearities in the data-generating process and an asymmetric loss function in the context of monetary policy.

References

  • Aiolfi M, Favero CA (2005) Model uncertainty, thick modelling and the predictability of stock returns. J Forecast 24:233–254

    Article  Google Scholar 

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov B, Csake F (eds) Second International Symposium on information theory. Akademia Kiado, Budapest

    Google Scholar 

  • Alcock J, Gray P (2005) Forecasting stock returns using model-selection criteria. Econ Rec 81:135–151

    Article  Google Scholar 

  • Barberis N, Thaler R (2003) A survey of behavioral finance. In: Constantinides GM, Harris M, Stulz R (eds) Handbook of the economics of finance. North Holland, Amsterdam, pp 1053–1128

    Google Scholar 

  • Barsky RB, Summers LH (1988) Gibson’s paradox and the gold standard. J Polit Econ 96:528–550

    Article  Google Scholar 

  • Baur DG, Lucey BM (2010) Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financ Rev 45:217–229

    Article  Google Scholar 

  • Baur DG, McDermott TK (2010) Is Gold a safe haven? International evidence. J Bank Finance 34:1886–1898

    Article  Google Scholar 

  • Beckmann J, Czudaj R (2013) Oil and gold price dynamics in a multivariate cointegration framework. Int Econ Econ Policy 10:453–468

    Article  Google Scholar 

  • Blose LE (2010) Gold prices, cost of carry, and expected inflation. J Econ Bus 62:35–47

    Article  Google Scholar 

  • Campbell JY, Thompson SB (2008) Predicting excess stock returns out of sample: Can anything beat the historical average? Rev Financ Stud 21:1509–1531

    Article  Google Scholar 

  • Capie F, Mills TC, Wood G (2005) Gold as a hedge against the dollar. J Int Financ Mark Inst Money 15:343–352

    Article  Google Scholar 

  • Christodoulakis GA, Mamatzakis EC (2008) An assessment of the EU growth forecasts under asymmetric preferences. J Forecast 27:483–492

    Article  Google Scholar 

  • Christodoulakis GA, Mamatzakis E (2013) Behavioural asymmetries in the G7 foreign exchange market. Int Rev Financ Anal 29:261–270

    Article  Google Scholar 

  • Christoffersen P, Diebold FX (1997) Optimal prediction under asymmetric loss. Econ Theory 13:808–817

    Article  Google Scholar 

  • Diba BT, Grossman HI (1984) Rational bubbles in the price of gold. NBER Working Paper, No. 1300, Cambridge, MA

  • Dornbusch R (1976) Expectations and exchange rate dynamics. J Polit Econ 84:1161–1176

    Article  Google Scholar 

  • Elliott G, Komunjer I, Timmermann A (2005) Estimation and testing of forecast rationality under flexible loss. Rev Econ Stud 72:1107–1125

    Article  Google Scholar 

  • Elliott G, Komunjer I, Timmermann A (2008) Biases in macroeconomic forecasts: Irrationality or asymmetric loss? J Eur Econ Assoc 6:122–157

    Article  Google Scholar 

  • Frankel JA (1986) Expectations and commodity price dynamics: the overshooting model. Am J Agric Econ 68:344–348

    Article  Google Scholar 

  • Frankel JA (2008) The effect of monetary policy on real commodity prices. In: Campbell JY (ed) Asset prices and monetary policy., NBER booksUniversity of Chicago Press, Chicago, pp 291–333

    Chapter  Google Scholar 

  • Frankel JA (2014) Effect of speculation and interest rates in a “carry trade” model of commodity prices. J Int Money Finance 42:88–112

    Article  Google Scholar 

  • Frankel JA, Rose AK (2010) Determinants of agricultural and mineral commodity prices. HKS Faculty Research Working Paper Series, Harvard University, Cambridge, MA, No. RWP10-038

  • Fritsche U, Pierdzioch C, Rülke JC, Stadtmann G (2015) Forecasting the Brazilian real and the Mexican peso: asymmetric loss, forecast rationality, and forecaster herding. Int J Forecast 31:130–139

    Article  Google Scholar 

  • Ghosh D, Levin EJ, Macmillan P, Wright RE (2004) Gold as an inflation hedge? Stud Econ Finance 22:1–25

    Article  Google Scholar 

  • Granger CW, Jeon Y (2004) Thick modeling. Econ Model 21:323–343

    Article  Google Scholar 

  • Hartmann D, Kempa B, Pierdzioch C (2008) Economic and financial crises and the predictability of U.S. stock returns. J Empir Finance 15:468–480

    Article  Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291

    Article  Google Scholar 

  • Koenker R, Machado JAF (1999) Goodness of fit and related processes for quantile regression. J Am Stat Assoc 94:1296–1310

    Article  Google Scholar 

  • Lettau M, Ludvigson SC (2009) Measuring and modeling variation in the risk-return tradeoff. In: Ait-Shalia Y, Hansen LP (eds) Handbook of financial econometrics, vol 1., Tools and techniquesNorth Holland, Amsterdam, pp 617–690

    Google Scholar 

  • Meese RA, Rogoff K (1983) Empirical exchange rate models of the seventies: Do they fit out of sample? J Int Econ 14:3–24

    Article  Google Scholar 

  • Minford P, Srinivasan V (2008) Are central bank preferences asymmetric? A comment. Econ Notes 37:119–126

    Article  Google Scholar 

  • Ntim, CG, English, J, Nwachukwu, J, Wang, Y (2015) On the efficiency of the global gold markets. Int Rev Financ Anal 41:218–236

  • Pesaran MH, Timmermann A (1995) Predictability of stock returns: robustness and economic significance. J Finance 50:1201–1228

    Article  Google Scholar 

  • Pesaran MH, Timmermann A (2000) A recursive modelling approach to predicting UK stock returns. Econ J 110:159–191

    Article  Google Scholar 

  • Pierdzioch C, Risse M, Rohloff S (2014a) On the efficiency of the gold market: results of a real-time forecasting approach. Int Rev Financ Anal 32:95–108

    Article  Google Scholar 

  • Pierdzioch C, Risse M, Rohloff S (2014b) The international business cycle and gold-price fluctuations. Q Rev Econ Finance 54:292–305

    Article  Google Scholar 

  • Pierdzioch C, Risse M, Rohloff S (2015) A real-time quantile-regression approach to forecasting gold returns under asymmetric loss. Resour Policy 45:299–306

    Article  Google Scholar 

  • Pindyck RS, Rotemberg JJ (1990) The excess co-movement of commodity prices. Econ J 100:1173–1189

    Article  Google Scholar 

  • Politis DN, Romano JP (1994) The stationary bootstrap. J Am Stat Assoc 89:1303–1313

    Article  Google Scholar 

  • Pukthuanthong K, Roll R (2011) Gold and the dollar (and the euro, pound, and yen). J Bank Finance 35:2070–2083

    Article  Google Scholar 

  • R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

  • Rapach DE, Strauss JK, Zhou G (2010) Out-of-sample equity premium prediction: combination forecasts and links to the real economy. Rev Financ Stud 23:821–862

    Article  Google Scholar 

  • Reboredo JC (2013a) Is gold a safe haven or a hedge for the U.S. dollar? Implications for risk management. J Bank Finance 37:2665–2676

    Article  Google Scholar 

  • Reboredo JC (2013b) Is gold a hedge or safe haven against oil price movements? Resour Policy 38:130–137

    Article  Google Scholar 

  • Sarno L, Valente G (2009) Exchange rates and fundamentals: Footloose or evolving relationship? J Eur Econ Assoc 7:786–830

    Article  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:416–464

    Article  Google Scholar 

  • Shiller RJ (1999) Human behavior and the efficiency of the financial system. In: Taylor JB, Woodford M (eds) Handbook of macroeconomics, vol 1. North-Holland, Amsterdam, pp 1305–1340

    Google Scholar 

  • Sjaastad LA (2008) The price of gold and the exchange rates: once again. Resour Policy 33:118–124

    Article  Google Scholar 

  • Theil H (1966) Applied economic forecasting., Studies in mathematical and managerial economicNorth-Holland, Amsterdam

    Google Scholar 

Download references

Acknowledgments

We thank an anonymous reviewer for helpful comments. We thank Marcos Sanso-Navarro and participants of the 19th Spring Meeting of Young Economists 2014 in Vienna, Austria, and the participants of the annual conference of the Verein für Socialpolitik 2014 in Hamburg, Germany, for helpful comments. The usual disclaimer applies.

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Authors and Affiliations

  1. Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008, Hamburg, Germany

    Christian Pierdzioch, Marian Risse & Sebastian Rohloff

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  1. Christian Pierdzioch
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  2. Marian Risse
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  3. Sebastian Rohloff
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Correspondence to Christian Pierdzioch.

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Pierdzioch, C., Risse, M. & Rohloff, S. Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy. Empir Econ 51, 1481–1499 (2016). https://doi.org/10.1007/s00181-015-1053-5

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