Asset Price Co-movements and the Dollar Carry Trade

After the Lehman Brothers’ collapse in September 2008, volatility spilled over into the foreign currency markets with the carry trades starting to rapidly unwind, whereby this breakdown was reflected by the implied volatilities of major emerging market (EM) currencies. High-yielding and previous investment currencies saw large depreciations against the US dollar, while funding currencies such as the Japanese yen benefited by a repatriation of funds into Japan. There was a scramble for US dollars, which was reflected in the higher volatility of the euro-US dollar swap rates. In a related note, during the crisis there has been increasing divergence from the assumption of covered interest rate parity (CIRP).1

With risk appetite rebounding in recent months, the US dollar has depreciated as safe-haven flows have unwound. With low US interest rates, investors have increasingly borrowed in US dollars and invested these proceeds in higher-yielding assets especially in EM economies. The capital inflows have put pressures on some currencies, and authorities have responded by slowing the pace of appreciation by accumulating reserves, and in some cases by capital controls. For instance, Figure 1 illustrates the strong appreciation of currencies against the US dollar in Asia and especially Latin America after the low point in March 2009.

The current situation bears a resemblance to the Japanese experience from 2005 onwards. With declining risk premiums around the world and Japanese interest rates near zero under the quantitative easing policy of the Bank of Japan, Japanese domestic investors were increasingly investing abroad (as evidenced by soaring Japanese holdings of foreign assets) to seek higher returns, and at the same time, foreign investors started to borrow in Japanese yen to fund higher-yielding currencies.

This article uses a generalised autoregressive conditional heteroskedasticity (GARCH) framework to examine the co-movement of the US dollar with a number of key financial asset prices in recent years. In particular, the dynamic conditional correlation (DCC) GARCH model by Engle (2002) is adopted, since standard correlations are potentially biased when examining co-movements and spillover between asset prices, especially in the presence of systemic risks and high volatilities.2

Figure 1: Emerging Market Currency Markets

Source: IMF

 

The results (Figure 2) indicate that an index for the US dollar has seen an increased negative co-movement with major asset price classes in recent months (here the MSCI Emerging Market index, the EMBI+ bond spread, S&P 500 as well as oil prices). For example, the negative co-movement between the US dollar and oil prices is almost at its highest since the beginning of 2006 with -0.5. Jen (2009) recently provided a number of reasons why the correlation between the dollar and crude oil prices has been so negative.3

While the increased co-movement of the US dollar with a range of risky assets does not provide any evidence for the dollar carry trade per se, the fact that the correlations have almost reached the highest magnitude since the beginning of the sample period in 2006 for all the asset classes in Figure 2 does suggest that a dollar depreciation has gone hand in hand with a sharp appreciation of higher-yielding emerging market asset classes. This is consistent with a story whereby the unwinding of safe-haven flows has significantly led to the rebound of risky asset classes, and the US dollar, bolstered by US quantitative easing and low interest rates, could have increasingly served as a funding currency. In practice, it is very difficult to document the extent and strength of the dollar carry trade given data limitations – more research is surely needed in order to obtain a better understanding of these recent developments.

Figure 2: Implied DCC GARCH Correlations

Source: IMF Estimates

 

The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management.

References

  • Engle, R. 2002, “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models,” Journal of Business & Economic Statistics, Vol. 20, pp. 339–50.
  • Forbes, K., and R. Rigobon, 2002, “No Contagion, Only Interdependence: Measuring Stock Market Co-movements,” Journal of Finance, Vol. 57, No. 5, pp. 2223–61.
  • Frank, Nathaniel, Brenda González-Hermosillo, and Heiko Hesse, 2008, “Transmission of Liquidity Shocks: Evidence from the 2007 Subprime Crisis,” IMF Working Paper 08/200 (Washington: International Monetary Fund).
  • International Monetary Fund, 2006, Global Financial Stability Report. World Economic and Financial Surveys (Washington, September).
  • Jen, Stephen L., 2009, “On the Link between the Dollar and Crude Oil,” BlueGold Capital Management LLP (November 20, 2009).
  • Roubini, Nouriel, 2009, “Mother of all carry trades faces an inevitable bust,” Comment in Financial Times, November 2, 2009.

1This relationship postulates that the currency forward premium equals the interest rate differentials of the home and foreign interest rate, such that a violation would imply possible arbitrage opportunities. The daily deviations from the CIRP jumped at the time of the Bear Stearns rescue, and then completely broke down for various EM currencies after Lehman’s bankruptcy.

2 The DCC model allows for heteroskedasticity of the data and a time-varying correlation in the conditional variance.

3 On oil prices driving the dollar, Jen (2009) argues that petrodollar recycling could have become less dollar friendly, there are different central bank responses to oil price shocks and high oil prices hurt the U.S. current account deficit. In contrast for possible reasons for the dollar driving oil prices, a weak dollar could be increasing demand for energy products especially in Asia, and institutional investors could be using commodities as a separate asset class (as anti-dollars). The increased negative correlation could also be caused by a common factor.

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