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NONLINEARITY IN FOREIGN EXCHANGE INTERVENTION EVIDENCE FROM DM

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NONLINEARITY IN FOREIGN EXCHANGE INTERVENTION EVIDENCE FROM DM

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    1. NONLINEARITY IN FOREIGN EXCHANGE INTERVENTION (EVIDENCE FROM DM/USD MARKET) Jongbyung Jun

    2. Intervention under floating exchange rate system Purpose of intervention (Federal Reserve) To slow rapid exchange rate moves. To signal that the exchange rate does not reflect fundamental economic conditions.

    3. Nonlinearity in Intervention Nonlinearity in Reaction Function Infrequency of intervention Nonlinear models better than linear models? Nonlinearity in Effectiveness of Intervention Ineffective on average but effective under certain conditions. Characterize the conditions with threshold models?

    4. Table of Contents Chapter 1 Friction Model and Intervention Chapter 2 Threshold Nonlinearity in Intervention Chapter 3 Conditions for Effective Intervention

    5. Chapter 1 Friction Model and Intervention Central bank reaction function y: amount of intervention x: measures of disorderly market conditions Infrequency of intervention For most daily observations, y = 0 when x ? 0. Infrequency implies nonlinearity. y is not proportional to x. Probit or logit. Tobit vs. Friction model.

    6. Friction Model of Rosett (1959, Econometrica) Friction y is insensitive to x over some range of x. Example y = change in a certain type of asset holdings x = change in yield y = 0 if x is smaller than transaction cost.

    7. Friction hypothesis for intervention Friction hypothesis A central bank does not intervene when degree of disorderly market conditions is relatively low, but intervenes when degree of disorderly market conditions is high. Friction model is better than a linear model? Almekinders & Eijffinger (1996, JBF) Kim & Sheen (2002, JIMF) Neely (2002, JIE)

    8. Reaction Function

    9. Explanatory variables dev7t-1: percentage deviation of St-1 from target level. Volt-1: volatility of St-1.

    11. Estimation of Friction Model Maximum Likelihood

    12. Goodness of fit R2 squared correlation coefficient of y and the fitted value.

    15. Testing for Relative Explanatory Power H0: R2linear = R2friction H1: R2linear < R2friction P-value obtained by bootstrapping. 1000 replications

    16. The Data Official daily intervention by Federal Reserve and Bundesbank. Net amount of US dollars (in millions) purchased. Amounts sold are recorded as negative numbers. DM/USD rate Recorded at 9:30 in Paris Source: Olsen and Associates of Zurich, Switzerland Data period: 1/5/87 1/22/93 Sample period: 2/23/87 10/31/89 (post-Louvre Accord)

    18. Federal Reserve Reaction Function

    22. Bundesbank Reaction Function

    23. With additional explanatory variables

    24. With extended sample (1/5/87 -1/22/93)

    25. Conclusion (Chapter 1) Infrequency of intervention implies nonlinear relationship between y and x. Friction model cannot explain the observed intervention better than a linear model. At least for daily observations. The result is robust to selective changes in explanatory variables and sample period.

    26. Chapter 2 Threshold Nonlinearity Friction Model Intervene when St is highly unstable. Do not intervene when St is relatively stable. Threshold Model Reaction to rapid movements in St is different from reaction to mild movements.

    27. Three-regime threshold model

    28. Measures of disorderly market conditions Almekinders & Eijffinger (1996) dev7, volatility (GARCH) Frenkel & Stadtmann (2001) dev25, dev(PPP), volatility lags of y, foreign banks intervention Humpage (1999) dev10, daily return, 10day moving std relative importance of exchange market in FOMC agenda Kim & Sheen (2002) dev150, volatility interest rate differentials, ratio of foreign reserves to imports.

    29. Test statistics and P-values Thresholds not identified under the null. Test statistic has a non-standard distribution. Compute p-values by the bootstrap procedure of Hansen (2000).

    30. Federal Reserve Reaction Function

    31. Federal Reserve Intervention (tau=0.15)

    32. Bundesbank Reaction Function

    33. Bundesbank Intervention (tau=0.15)

    34. Conclusion (Chapter 2) Multi-regime threshold models (2-regime models) tend to have higher R2 than a linear model. Friction hypothesis is not supported. However, central banks tend to intervene more frequently and in large amounts when the deviation is large.

    35. Size is too small. ? What if the size is large? Lean against too strong winds (short-term trends). ? What if the wind is weak? Inappropriate timing. ? What if the timing is right? Chapter 3 Conditions for effective intervention Most empirical studies find intervention to be ineffective on average. But sometimes intervention is effective. Conditions for effective intervention?

    36. Related Literature No systematic and persistent effect. Rosenberg (1996, Ch.11). Intervention is effective. Dominguez and Frankel (1993). Depends on market conditions & strategy. Baillie, Humpage and Osterberg (2000). Sarno and Taylor (2001).

    37. Linear effect model

    38. Average (Linear) effect of intervention

    39. Nonlinear effect by size of intervention (Loess fit: degree = 1, span = 0.3)

    40. Test by size of intervention (qt = xt = amount of intervention)

    41. 3-regime threshold model Estimation: c1, c2, c3, ?1, ?2, ?3 and g1, g2 Null Hypothesis: Effectiveness of intervention is the same in the 3 regimes. Or, 3-regime model is no better than a 1-regime model. H0: c1=c2=c3, ?1= ?2= ?3.

    42. Threshold effect by size of intervention

    43. Test by strength of wind (qt=deviation from 20-day average)

    44. Threshold effect by strength of wind

    45. 3. Timing of intervention (qt=% deviation from 50-day average) Noise-traders dominate short-run market. Buy rising currency and sell falling currency. Enhance trend with self-fulfilling effects. Responsible for short-run overshooting. Noise-trading channel (Hung, 1997). When the market runs, do not attempt to block. Wait until they slow down. Right timing When traders are in a heavy oversold or overbought position.

    46. Measure of right timing

    47. Test by right timing (qt=deviation from 50-day moving average)

    48. Threshold effect by timing

    49. Conclusion (Chapter 3) Large buying USD intervention is effective. Leaning against the weak wind is effective. Intervention at a right timing is effective. Noise-trading channel exists. Right timing is more important than size of intervention.

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