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on Financial Markets |
| By: | Geir Høidal Bjønnes (Norwegian School of Management); Dagfinn Rime (Norges Bank); Haakon O. Aa. Solheim (Statistics Norway (SSB)) |
| Abstract: | We presents evidence that non-financial customers are the main liquidity providers in the overnight foreign exchange market using a unique daily data set covering almost all transactions in the SEK/EUR market over almost ten years. Two main findings support this: (i) The net position of non-financial customers is negatively correlated with the exchange rate, opposed to the positive correlation found for financial customers; (ii) Changes in net position of non-financial customers are forecasted by changes in net position of financial customers, indicating that non-financial customers take a passive role consistent with liquidity provision. |
| Keywords: | Microstructure, International finance, Liquidity |
| JEL: | F31 F41 G15 |
| Date: | 2004–11–05 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2004_13&r=fmk |
| By: | Torben G. Andersen (Kellogg School of Management, Northwestern University); Tim Bollerslev (Department of Economics, Duke University); Peter F. Christoffersen (Faculty of Management, McGill University); Francis X. Diebold (Department of Economics, University of Pennsylvania) |
| Abstract: | Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. |
| JEL: | C10 C53 G1 |
| Date: | 2005–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:05-011&r=fmk |