The Tokyo experiment: results

Now, what happened to lunch-hour volatility after the trading restriction was lifted? Lunch return volatility doubled. This increase is statistically significant and is illustrated in Figure1, which is based on three 90-minute periods. Even though this result is similar to that of French and Roll, it is perhaps more striking since most people believe that in the FX market the flow of public information is even more important for understanding volatility than in the equity market.

So what's going on? Because we have controlled for public information, the volatility increase must be coming from one of the other two explanations cited above -- non-public information and pricing errors. To discriminate between them, we can look to relevant theory for testable implications. Specifically, we can look to work on volatility patterns. It is a stylized fact that within a trading day, volatility in most financial markets is relatively high in the morning, lower midday, and then high again in the afternoon. This is typically referred to as the intraday volatility "U-shape." From Figure 1 it is clear that this holds for the foreign exchange market as well. Theory explains the volatility U-shape using non-public information. We can use this theory to provide testable implications of the presence of non-public information, for example, how the U-shape should change when lunch-hour trading is introduced. Specifically, theory predicts the U-shape will flatten: more information can be revealed through trading over the lunch period, leaving a smaller share for the morning and afternoon. Clearly, this implication of non-public information is borne out in Figure 1 as well.

There is another testable implication provided by models with non-public information. This implication relates to trading over the morning period: if non-public information is indeed driving the U-shape, then in the period with closure over lunch we should find a morning U-shape. Moreover, after the restriction is lifted we should find that the morning U-shape disappears as a full-day U-shape emerges. Figure 2 verifies that this is the case, using higher resolution hourly intervals from 9:00AM-1:00PM. The data behave just as the models based on non-public information predict.

Now the question must be confronted: What is this information that is not publicly available but clearly moves exchange rates? Of the many examples, I describe just two. First, actual FX traders, for example, often cite the fact that not all market participants have the same information about real-time trading activity. Traders at big commercial banks have a lot more customers placing large trades directly through them. This non-public information about demands allows them to forecast how the market will react when it learns about these big trades (that is, it is a motive for speculative trading). Another example, cited above, is advance knowledge of the trades of central banks during intervention operations: a dealer who first receives a central bank's order has superior information for forecasting exchange rate movements.