Baillie R., Bollerslev T., 1989, “The Message in Daily Exchange Rates: A Conditional Variance Tale,” Journal of Business and Economic Statistics, Vol. 7, pp. 297 - 305.
Bessembinder H., Seguin P., 1992, “Futures Trading Activity and Stock Price Volatility,” Journal of Finance, Vol. 47, pp. 2015 - 2034.
Bollerslev T., Chou R., Kroner K., 1992, “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence,” Journal of Econometrics, Vol. 52, pp. 5 - 59.
Chatrath A., Ramchander S., Song F., 1996, “The Role of Futures Trading in Exchange Rate Volatility,” Journal of Futures Markets, Vol. 16, pp. 561 - 584.
Clifton E., 1985, “The Currency Futures Market and Interbank Foreign Exchange Trading,” Journal of Futures Markets, Vol. 5, pp. 375 - 384.
Crain S., Lee H. J., 1995, “Intraday Volatility in Interest Rate and Foreign Exchange Spot and Futures Markets,” Journal of Futures Markets, Vol. 15, pp. 395 - 421.
Edwards F., 1988, “Futures Trading and Cash Market Volatility: Stock Index and Interest Rate Futures,” Journal of Futures Markets, Vol. 8, pp. 421 - 439.
Engle R., 1982, “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of the UK Inflation,” Econometrica, Vol. 50, pp. 987 - 1008.
Finucane T., 1991, “Put - Call Parity and Expected Returns,” Journal of Financial and Quantitative Analysis, Vol. 26, pp. 445 - 457.
Friedman B., Laibson D., 1989, “Economic Implications of Extraordinary Movements in Stock Prices,” Brookings Papers on Economic Activity, Vol. 2, pp. 137 - 189.
Folkerts-Landau D., Ito T., 1995, International Capital Markets; Developments, Prospects, and Policy Issues, (Washington D.C., International Monetary Fund).
Geweke J., 1982, “Measurement of Linear Dependence and Feedback Between Time Series,” Journal of the American Statistical Association, Vol. 79, pp. 304 - 324.
Glosten L., Jagannathan R., Runkle D., 1993, “On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return of Stocks,” Journal of Finance, Vol. 48, pp. 1779 - 1801.
Hamilton J., 1989, “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, Vol. 57, pp. 357 - 384.
Hamilton J., Susmel R., 1994, “Autoregressive Conditional Heteroscedasticity and Changes in Regime,” Journal of Econometrics, Vol. 64, pp. 307 - 333.
Jabbour G., 1994, “Prediction of Future Currency Exchange Rates from Current Currency Futures Prices: The Case of DM and JY,” Journal of Futures Markets, Vol. 14, pp. 25 - 36.
Karpoff J., 1987, “The Relation between Price Changes and Trading Volume: A Survey,” Journal of Financial and Quantitative Analysis, Vol. 22, pp. 109 - 126.
Kawaller I., Koch P., Koch T., 1993, “Intraday Market Behavior and the Extent of Feedback Between S&P 500 Futures Prices and the S&P 500 Index,” Journal of Financial Research, Vol. 16, pp. 107 - 121.
Klemkosky R., Maness T., 1980, “The Impact of Options on the Underlying Security,” Journal of Portfolio Management, Vol. 7, pp. 12 - 18.
MacKinlay A., Ramaswamy k., 1988, “Index-Futures Arbitrage and the Behavior of Stock Index Futures Prices,” The Review of Financial Studies, Vol. 1, pp. 137 - 158.
McCarthy J., Najand M., 1993, “State Space Modeling of Price and Volume Dependence:Evidence from Currency Futures,” Journal of Futures Markets, Vol. 13, pp. 335 - 344.
Oellerman C., Farris P., 1985, “Futures or Cash: Which Market Leads Live Beef Cattle Prices?,” Journal of Futures Markets, Vol. 5, pp. 529 - 538.
Poon P., 1994, “An Empirical Examination or the Return Volatility-Volume Relation in Related Markets: The Case of Stock and Options,” The Financial Review Vol. 29, pp. 473-496.
Schwarz T., Laatsch F., 1991, “Dynamic Efficiency and Price Leadership in Stock Index and Futures Markets,” Journal of Futures Markets, Vol. 11, pp. 669 - 683.
Shastri K., Sultan J., Tandon K., 1996, “The Impact of the Listing of Options in the Foreign Exchange Market,” Journal of International Money and Finance, Vol. 15, pp. 37 - 64.
Stephan J., Whaley R., 1990, “Intraday Price Change and Trading Volume Relations in the Stock and Stock Options Market,” Journal of Finance, Vol. 45, pp. 191 - 220.
Stucki T., Wasserfallen W., “Stock and Option Markets: the Swiss Evidence,” Journal of Banking and Finance, Vol. 18, pp. 881 - 893.
This paper was prepared while Christian Jochum was visiting the Fund. The authors are grateful to Carlo Cottarelli, Robert Flood, Graeme Justice, Charles Kramer, Ricky Lam, David Ng, Lorenzo Perez, Patricia Reynolds, S. Hossein Samiei, Garry Schinasi, Gabriel Sensenbrenner, and Philip Young for helpful comments and discussion during the preparation of the paper, to Darvas Zsolt, of the Hungarian National Bank for the provision of the Hungarian Data, and to the Malaysian authorities for the initial prompt to undertake the study. We are further grateful to James Hamilton.
“NYCE’s Plan to Launch Contracts Gets Mixed Response,” International Securities Regulation Report, June 19, 1997, p. 3.
Other related elements often identified as providing low transaction costs include lower feesand commissions, lower opportunity cost of initial margins, lower opportunity cost ofadditional liquid assets held to meet variation margin, smaller bid-ask spreads, and fewerregulatory constraints.
While several other emerging market currencies have recently begun trading (or are scheduled to), their price history is still to short to use in any empirical work.
For the US market conflicting evidence is presented on the question of whether the options market leads the stock market. Stephan and Whaley (1990) present results favoring this conclusion, while Finucane (1991) reaches the opposite conclusion.
McCarthy and Najand (1993) find a positive relation between futures trading volume and futures volatility.
Kawaller, Koch, Koch (1993) also report a strengthened relation between cash and futures market as the futures market volatility rises. They attribute this behavior to the speed of information processing.
Jabbour (1994) states that the implied spot rates derived from futures prices are good predictors of future spot rates. This result can be regarded as a cautious indicator that the derivative market is not excessively volatile.
The crash of the EMS 1992 is a much cited example for the destructive destabilization emanating from derivative markets. Nevertheless it could be argued that futures and options markets were anticipating and thus accelerating realignments, which had been postponed for too long.
The tendency of ARCH models to imply too much volatility persistence was demonstrated in the analysis of the October 1987 stock market crash, e.g. Engle and Mustafa (1992).
It may be possible to make these transition probabilities a function of macroeconomic variables associated with regime shifts, thereby linking this purely time series model of volatility to economic fundamentals. However, since our intention is simply to obtain a base case against which to measure the effect of a futures introduction, this extension is left as future research.
The response of an element i in zt to a shock in another element j in zt is described by the sequence ψij,1, ψij,2, ψij,3, …: the impulse response function.
Many emerging market countries, including those examined here, manage their currencies so as to remain within a band. The use of the SWARCH methodology would, if the band effectively limited ex post volatility to be within a constant range over the sample period, show that only one “regime” would be necessary to accommodate the time series pattern of volatility. In this case, the SWARCH model is superfluous and the use of an ARCH model would suffice. Thus, since the SWARCH model is purely a statistical model for volatility, the existence or nonexistence of a formal (or informal) exchange rate band does not affect its usefulness. The data determine whether multiple regimes are needed to provide a good statistical fit.
The data have been provided by Bloomberg, the Futures Industry Institute, and the Hungarian National Bank.
Daily open interest data are not available on the Hungarian forint/U.S. dollar contract.
Because the nearby series will have discrete jumps at transition points between contract months, dummy variables for these dates are introduced in the return equation. The results are not sensitive to this method for estimating the return equation.
Futures data on the Mexican peso are only available after April 1995, since the contract began trading at this time.
The sample lengths are relatively short for unit root tests to have high power. However, despite the added tendency to accept the null hypothesis of a unit root when it is not present, we strongly reject the presence of a unit root.
Only the futures return series for the HF does not show the regime switching characteristics initially assumed and thus the SWARCH model is not used in this case. A three regime model was attempted, but did not converge for any of the series.
Using a shorter lag structure results in very slow convergence and parameter estimates were not robust to alternative starting values. Lag lengths between 2 and 5 lags have been investigated.
Note that this feature of the spot and futures prices, that is, the spot prices remain unchanged for several days at a time while the futures prices are rarely unchanged, by itself suggests that futures markets incorporate information faster than spot markets.
Since the appropriate length of time over which agents measure and react to volatility may be different from the daily horizon assumed here, the results for the Mexican peso have been reestimated using returns measured over 5 trading days. The smaller number of observations typically lowers the significance of the estimated coefficients and the model utilizes a normal distribution, rather than the fatter-tailed t distribution, to obtain convergence. However, the results are qualitatively the same: ARCH effects are still present; there are significant regime shifts; and similar probabilities for the transition matrices are obtained. This result accords with other studies, Droste and Nijman (1993) and Diebold (1988), that show that ARCH effects are relatively stable at multiple sampling frequencies.
There were a number of large outliers in the data. To gauge their influence on the results, the model is reestimated for the MP spot rate after removing the four largest outliers (in absolute value) and replacing them with the average value of the previous and following observation. The values of PI and P2 are virtually unchanged and the correlation coefficient between the estimated volatility of the series with and without the outliers is 0.92. Only the second ARCH coefficient changes appreciably, from 0.203 to 0.112, while the other estimates remain more or less unchanged. From these results, we view the influence of outliers as limited.
The optimization procedure was run in GAUSS 3.0 employing the OPTMUM package. Some of the routines are derived from programmes generously provided by J. Hamilton.
Since exchange rates are the price of one currency in terms of the other and can be quoted in dollar terms, or the reciprocal, a leverage effect is thought to be less likely for exchange rate series. However, one could argue that for emerging market currencies, where the numeraire currency is the dollar, the distinction between a depreciation and an appreciation in local currency may be meaningful. The absence of a leverage term demonstrating this effect may be a reflection of the time period used—all three currencies were depreciating and were expected to maintain this trend.
The use of “generated” variables in subsequent econometric techniques can sometimes be problematic. We believe we have circumvented any biases due to the generated volatility estimates by not using any common variables from the original spot and futures SWARCH specifications in subsequent specifications.
While we attempt to use sound economic arguments to appropriately choose the ordering of the variables in the VAR it is useful to note that the covariance terms in the VAR error variance-covariance matrix are typically quite low, ranging from 0.009 to 0.47 with a mean of 0.12. Since these coefficients are generally low, the orthogonalization and, consequently, the exact methodology used are of somewhat reduced importance. This is borne out by reestimating the variance decomposition using the Bernanke (1986) approach in which the ordering of the variables is replaced by an ordering of the errors, thereby generating an impulse response function which is not influenced by the original ordering of the variables. The results are substantively the same. Further, reestimating the variance decomposition with the positions of spot volatility and futures volatility exchanged does not significantly alter the results in Table 6: for all three currencies and for any ordering chosen over the 10-day horizon the own-variance of the spot market variable is never lower than 60 percent. Moreover, the futures market variables continue to have strong self-explanatory power as well.
With the minor exception of spot on spot in the MP case, which most probably is due to the high starting level of 98.23 percent.
Originally, we were concerned that the inclusion of futures volume may detract from any relation between futures volatility and spot volatility, since futures volume and futures volatility have been found to be highly correlated in previous research. However, it appears the volume-volatility relation is fairly weak in these data. Moreover, we had hoped to examine the potential influence of the futures market on the underlying spot market after correcting for the influence of spot trading on the spot volatility. But since the foreign exchange market is highly decentralized, spot trading volume data are seldom collected and are not available for the three currencies examined here. Under these circumstances a significant relationship between spot volatility and the futures market volatility can falsely be established when the spot market volume is absent and the futures market trading activity acts as an instrument for the (missing) spot volume variable.
Three authors offer evidence against the hypothesis that futures volume is very closely related to spot volume, implying this latter issue is moot as well. Wei (1994) argues that the movement of the futures market and the spot market can diverge significantly and he concludes that “the omission of the spot volume variable does not seriously bias the parameter estimation for the market’s anticipated volatility.” Lyons (1995) reports that much of the currency spot market trading is uninformative as market makers pass through currency positions obtained from their corporate and retail customers. Finally Poon (1994) shows that regressing the spot volatility on spot volume and derivative volume yields two significant coefficients and that derivative volume tends to lead spot volume suggesting that both spot and futures volume have their own independent influence on spot volatility.
Our futures data run from January 1, 1995 through February 28, 1997, limiting the sample period for the previous results. However, our spot series is longer, allowing us to expand the sample for this purpose.
The use of dummy variables in (G)ARCH type volatility estimation was first outlined by Baillie and Bollerslev (1989), who use dummies to account for day of the week effects. The dummy dt takes the value of 1 with the introduction of futures contracts.
Replacing the simple step dummy with a measure of futures market volume decreases the coefficient in absolute size, but does not affect the level of significance or the sign of the coefficient.
Replacing the volume variable in models (5a) and (5b) with the SWARCH estimate for futures market volatility again yields negative coefficients on the futures variable, which lack statistical significance in 2 of the 3 cases. However these results were numerically difficult to obtain and appear to lack the robustness of our other results.