Are you Jaehun Chung?

Claim your profile

Publications (2)1.76 Total impact

  • Source
    Jaehun Chung, Yongmiao Hong
    [Show abstract] [Hide abstract]
    ABSTRACT: We examine directional predictability in foreign exchange markets using a model-free statistical evaluation procedure. Based on a sample of foreign exchange spot rates and futures prices in six major currencies, we document strong evidence that the directions of foreign exchange returns are predictable not only by the past history of foreign exchange returns, but also the past history of interest rate differentials, suggesting that the latter can be a useful predictor of the directions of future foreign exchange rates. This evidence becomes stronger when the direction of larger changes is considered. We further document that despite the weak conditional mean dynamics of foreign exchange returns, directional predictability can be explained by strong dependence derived from higher-order conditional moments such as the volatility, skewness and kurtosis of past foreign exchange returns. Moreover, the conditional mean dynamics of interest rate differentials contributes significantly to directional predictability. We also examine the co-movements between two foreign exchange rates, particularly the co-movements of joint large changes. There exists strong evidence that the directions of joint changes are predictable using past foreign exchange returns and interest rate differentials. Furthermore, both individual currency returns and interest rate differentials are also useful in predicting the directions of joint changes. Several sources can explain this directional predictability of joint changes, including the level and volatility of underlying currency returns. Copyright © 2007 John Wiley & Sons, Ltd.
    Journal of Applied Econometrics 01/2007; 22(5):855-889. · 1.76 Impact Factor
  • Jaehun Chung, Yongmiao Hong
    [Show abstract] [Hide abstract]
    ABSTRACT: Using a generalized cross-spectral approach, we propose a model-free omnibus statistical procedure to check whether the direction of changes in an economic variable is predictable using the history of its past changes. A class of separate inference procedures are also given to gauge possible sources of directional predictability. They can reveal information about whether the direction of future changes is predictable using the direction, level, volatility, skewness, and kurtosis of past changes. An important feature of the proposed procedures is that they check many lags simultaneously, which is particularly suitable for detecting the alternatives whose directional dependence is small at each lag but it carries over a long distributional lag. At the same time, the tests naturally discount higher order lags, which is consistent with the conventional wisdom that financial markets are more influenced by the recent past events than by the remote past events. We apply the proposed procedures to four daily U.S. stock price indices. We find overwhelming evidence that the directions of excess stock returns are predictable using past excess stock returns, and the evidence is stronger for the directional predictability of large excess stock returns. In particular, the direction and level of past excess stock returns can be used to predict the direction of future excess stock returns with any threshold, and the volatility, skewness and kurtosis of past excess stock returns can be used to predict the direction of future excess stock returns with nonzero thresholds (i.e., large returns). The well-known strong volatility clustering together with weak serial dependence in mean cannot completely explain all documented directional predictability for stock returns. To exploit the economic significance of the documented directional predictability for stock returns, we consider a class of autologit models for directional forecasts and find that they have significant out-of-sample dire
    Econometric Society, Econometric Society 2004 North American Winter Meetings. 01/2004;