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on Financial Markets |
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Issue of 2017‒04‒30
four papers chosen by |
| By: | Raddant, Matthias; Kenett, Dror Y. |
| Abstract: | The global financial system is highly complex, with cross-border interconnections and interdependencies. In this highly interconnected environment, local financial shocks and events can be easily amplified and turned into global events. This paper analyzes the dependencies among nearly 4,000 stocks from 15 countries. The returns are normalized by the estimated volatility using a GARCH model and a robust regression process estimates pairwise statistical relationships between stocks from different markets. The estimation results are used as a measure of statistical interconnectedness, and to derive network representations, both by country and by sector. The results show that countries like the United States and Germany are in the core of the global stock market. The energy, materials, and financial sectors play an important role in connecting markets, and this role has increased over time for the energy and materials sectors. Our results confirm the role of global sectoral factors in stock market dependence. Moreover, our results show that the dependencies are rather volatile and that heterogeneity among stocks is a non-negligible aspect of this volatility. |
| Keywords: | Asset markets,Comovement,Financial networks,Interconnectedness |
| JEL: | G15 G11 C58 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwkwp:2076&r=fmk |
| By: | José Renato Haas Ornelas; Roberto Baltieri Mauad |
| Abstract: | This paper extends the empirical literature on volatility risk premium (VRP) and future returns by analyzing the predictive ability of commodity currency VRP and commodity VRP. The empirical evidence throughout this paper provides support for a positive relationship of commodity currency VRP and future commodity returns, but only for the period after the 2008 global financial crisis. This predictability survives the inclusion of control variables such as equity VRP and past currency returns. Furthermore, we find a negative relationship between gold VRP and future commodity and currency returns. This result corroborates the view of gold as a safe haven asset |
| Date: | 2017–04 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:455&r=fmk |
| By: | Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, Australia); Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa and IPAG Business School, Paris, France); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, Omaha, USA and School of Business and Economics, Loughborough University, Leicestershire,UK) |
| Abstract: | This paper examines volatility linkages and forecasting for stock and foreign exchange (FX) markets from a novel perspective by utilizing a bivariate Markov-switching multifractal model (MSM) that accounts for possible interactions between stock and FX markets. Examining daily data from the advanced G6 and emerging BRICS nations, we compare the out-of-sample volatility forecasts from GARCH, univariate MSM and bivariate MSM models. Our findings show that the GARCH model generally offers superior volatility forecasts for short horizons, particularly for FX returns in advanced markets. Multifractal models, on the other hand, offer significant improvements for longer forecast horizons, consistently across most markets. Finally, the bivariate MF model provides superior forecasts compared to the univariate alternative in most G6 countries and more consistently for FX returns, while its benefits are limited in the case of emerging markets. Overall, our findings suggest that multifractal models can indeed improve out-of-sample volatility forecasts, particularly for longer horizons, while the bivariate specification can potentially extend the superior forecast performance to shorter horizons as well. |
| Keywords: | Long memory, multifractal models, simulation based inference, volatility forecasting, BRICS |
| JEL: | C11 C13 G15 |
| Date: | 2017–04 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201728&r=fmk |
| By: | Wagner, Alexander F. (University of Zurich); Zeckhauser, Richard J. (Harvard University); Siegler, Alexandre (University of Zurich) |
| Abstract: | The election of Donald J. Trump as the 45th President of the United States of America on 11/8/2016 came as a surprise. Markets responded swiftly and decisively. This note investigates both the initial stock market reaction to the election, and the longer-term reaction through the end of 2016. We find that the individual stock price reactions to the election--that is, the market's vote--reflect investor expectations on economic growth, taxes, and trade policy. Heavy industry and banking were relative winners, whereas healthcare, medical equipment, pharmaceuticals, textiles, and apparel were among the relative losers. High-beta stocks and companies with a hitherto high tax burden benefited from the election. Although internationally-oriented companies may profit under some plans of the new administration, several other arguments suggest a more favorable climate for domestically-oriented companies. Investors have found the domestic-favoring arguments to be stronger. While investors incorporated the expected consequences of the election for US growth and tax policy into prices relatively quickly, it took them more time to digest the consequences of shifts in trade policy on firms' prospects. |
| JEL: | G12 G14 H25 O24 |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:harjfk:rwp17-005&r=fmk |