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on Market Microstructure |
| By: | Laser, Falk; Hellwig, Michael |
| JEL: | D22 G21 G34 L11 L25 L40 L41 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc19:203536&r=all |
| By: | Eckbo, B. Espen (Tuck School of Business at Darthmouth College); Ødegaard, Bernt Arne (University of Stavanger) |
| Abstract: | We test for systematic gender-differences in trading propensity and performance using the population of primary insiders on the Oslo Stock Exchange (OSE), 1986--2016. We use Norway's 2005 board gender quota law, which nearly tripled the population of female directors, as an exogenous shock to female directors' access to information through the expanded director network. Moreover, we use differences in trading activity following the exogenous increase in trading risk caused by the 2008 financial crisis to identify gender-based differences in risk aversion. We find no significant gender-based difference in insider trading performance, whether before or after the mandatory board gender-balancing. However, we find that female insiders are significantly more likely to buy shares during the financial crisis, which is consistent with female directors and executives exhibiting less (not more) risk aversion than their male counterparts. |
| Keywords: | Insider trading; gender; risk aversion; portfolio performance; director network; board gender- balancing |
| JEL: | G14 M14 |
| Date: | 2019–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:stavef:2019_002&r=all |
| By: | Mawuli Segnon (Department of Economics, Institute for Econometric and Economic Statistics, University of Münster, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Keagile Lesame (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Mark E. Wohar (Department of Economics, University of NE-Omaha, USA and School of Business and Economics, Loughborough University, UK) |
| Abstract: | We propose a logistic smooth transition autoregressive fractionally integrated [STARFI(p,d)] process for modeling and forecasting US housing price volatility. We discuss the statistical properties of the model and investigate its forecasting performance by assuming various specifications for the dynamics underlying the variance process in the model. Using a unique database of daily data on price indices from ten major US cities, and the corresponding daily Composite 10 Housing Price Index, and also a housing futures price index, we find that using the Markov-switching multifractal (MSM) and FIGARCH frameworks for modeling the variance process helps improving the gains in forecast accuracy. |
| Keywords: | US housing prices, GARCH processes, MSM processes, Model confidence set |
| JEL: | C22 C53 C58 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201977&r=all |
| By: | Halbleib, Roxana; Dimitriadis, Timo |
| JEL: | C1 C4 C5 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc19:203669&r=all |