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on Market Microstructure |
| By: | Philippe De Peretti (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne); Oren Tapiero (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne) |
| Abstract: | The ability to trade in dark-pools without publicly announcing trading orders, concerns regulators and market participants alike. This paper analyzes the information contribution of dark trades to the intraday volatility process. The analysis is conducted by performing a GARCH estimation framework where errors follow the generalized error distribution (GED) and two different proxies for dark trading activity are separately included in the volatility equation. Results indicate that dark trades convey important information on the intraday volatility process. Furthermore, the results highlight the superiority of the proportion of dark trades relative to the proportion of dark volume in affecting the one-step-ahead density forecast |
| Keywords: | Dark Pools; Density Forecast; Dark Volume; Dark trade |
| Date: | 2014–02–20 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:hal-00984834&r=mst |
| By: | Zhang, Guangfeng; Zhang, Qiong; Majeed, Muhammad Tariq |
| Abstract: | Using two measures of private information and high-frequency transaction data from the leading interdealer electronic broking system Reuters D2000-2, we examine the association between exchange rate return and contemporaneous order flow and the predictability power of lagged order flow on the future exchange rate return. Our empirical analysis demonstrates that at high frequency (5, 10, 15, 20, 25, and 30 min) there exists strong positive association between exchange rate returns and contemporaneous order flow. However, the results indicate weak predictability of order flow on the future exchange rate return. |
| Keywords: | Exchange Rate, Forecasting, Microstructure Approach |
| JEL: | F31 G17 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:57673&r=mst |
| By: | Zilu Shang (ICMA Centre, Henley Business School, University of Reading); Chris Brooks (ICMA Centre, Henley Business School, University of Reading); Rachel McCloy (University of Reading) |
| Abstract: | Investors are now able to analyse more noise-free news to inform their trading decisions than ever before. Their expectation that more information means better performance is not supported by previous psychological experiments which argue that too much information actually impairs performance. To test whether more information always means better performance in the stock markets, an experiment is conducted based on a trading simulation manipulated from a real market-shock. The results indicate that the explicitness of information neither improves nor impairs participants’ performance effectiveness from the perspectives of returns, share and cash positions, and trading volumes. However, participants’ performance efficiency is significantly affected by information explicitness. Although they need less time to implement their decisions when placing an order, explicitly informed investors are punished by making more mistakes. |
| Keywords: | explicitness of information, performance effectiveness, performance efficiency, individual investors, experimental finance |
| JEL: | C91 D82 G02 |
| Date: | 2013–06 |
| URL: | http://d.repec.org/n?u=RePEc:rdg:icmadp:icma-dp2013-05&r=mst |
| By: | Belén Nieto (Departamento de Economía Financiera y Contabilizad, University of Alicante, San Vicente del Raspeig, 03690 Alicante, Spain); Alfonso Novales Cinca (Departamento de Economía Cuantitativa (Department of Quantitative Economics), Facultad de Ciencias Económicas y Empresariales (Faculty of Economics and Business), Universidad Complutense de Madrid); Gonzalo Rubio (University CEU Cardenal Herrera, Elche, 03204 Alicante, Spain) |
| Abstract: | This paper analyzes the relationship between the volatility of corporate bond returns and standard financial and macroeconomic indicators reflecting the state of the economy. We employ the GARCHMIDAS multiplicative two-component model of volatility that distinguishes the short-term dynamics from the long-run component of volatility. Both the in-sample and out-of-sample analysis show that recognizing the existence of a stochastic low-frequency component captured by macroeconomic and financial indicators may improve the fit of the model to actual bond return data, relative to the constant long-run component embedded in a typical GARCH model. |
| Keywords: | Corporate bonds, Volatility, Low-frequency component, High-frequency component, Macroeconomic indicators, Financial indicators. |
| JEL: | G12 C22 E44 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1425&r=mst |