| Project/Area Number |
11630091
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Public finance/Monetary economics
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| Research Institution | Otaru University of Commerce |
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Principal Investigator |
WADA Ryosuke Otaru University of Commerce, Faculty of Commerce, Associate Professor, 商学部, 助教授 (00241414)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2000: ¥100,000 (Direct Cost: ¥100,000)
Fiscal Year 1999: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | Microstructure / price volatility / trading volume / heterogeneous expectation / foreign exchange / continuous auctions / bid / ask spread / intra-day transaction / 売買商 / 取引高 / 外国為替市場 / 連続時間 / 一様分布 / 逆関数 |
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| Research Abstract |
Price formation processes in financial markets, including foreign exchange market, have theoretical difficulties as follows. Auctions are in continuous time and hence there is not a specific length of time to define demand and supply. So it is difficult to apply equilibrium analysis. Also market participants have heterogeneous expectations and they try to guess this heterogeneity. We have to formulate this heterogeneity while the definition of the equalibrium for the case of the continuous auction still is not clear. We introduced useful approaches to analyze continuous auctions.
Our approaches are as follows. First, we use number of arrivals of buyers and sellers instead of demand and supply. Second, we introduce a distribution function for the market participants' heterogeneous reservation prices. Third, we approximate transaction price by a median of the reservation prices.
We constructed two models for the foreign exchange market. One is for volatility/trading volume relationships and the second is for a market maker's determination of bid/ask spread. By regression analysis of intra-day USD/JPY rate, we obtained a statistically significant regression coefficient of volume on volatility. Our model can explain intra-day variations of the regression coefficient.
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