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on Financial Markets |
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Issue of 2015‒08‒19
four papers chosen by |
| By: | Paresh K Narayan (Deakin University); Sagarika Mishra (Deakin University); Seema Narayan (Royal Melbourne Institute of Technology) |
| Abstract: | In this paper, we model the determinants of spread for 734 firms listed on the NYSE over the period 1 January 1998 to 31 December 2008. We propose a panel data model of the determinants of spread. There are four main messages emerging from our work. We find a statistically significant effect of volume on spread inconsistent with the work of Johnson (2000). On price, we find mixed results, consistent with the literature. On the effect of price volatility on spread, our results are completely the opposite of the cross-sectional literature but sides with the relatively recent work of Chordia et al. (2001). We allow for persistence of spread as a determinant of spread and find significant evidence of spread persistence across all 16 sectors. Finally, we examine size effects and find statistically strong evidence of size effects based on the relationship between price and spread, persistence and spread, and volatility and spread. |
| Keywords: | Bid-Ask; Spread; NYSE; Panel Data. |
| URL: | http://d.repec.org/n?u=RePEc:dkn:ecomet:fe_2015_06&r=fmk |
| By: | Joseph P. Byrne; Shuo Cao; Dimitris Korobilis |
| Abstract: | This paper investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in bond yields of seven advanced economies is due to global co-movement, which is mainly attributed to shocks to non-fundamentals. Global fundamentals, especially global inflation, affect yields through a ‘policy channel’ and a ‘risk compensation channel’, but the effects through two channels are offset. This evidence explains the unsatisfactory performance of fundamentals-driven term structure models. Our approach delineates asymmetric spillovers in global bond markets connected to diverging monetary policies. The proposed model is robust as identified factors has significant explanatory power of excess returns. The finding that global inflation uncertainty is useful in explaining realized excess returns does not rule out regime changing as a source of non-fundamental fluctuations. |
| Keywords: | Global Bond Markets, Term Structure of Interest Rates, Shocks to Fundamentals and Non-Fundamentals, Co-Movement, Contagion, Excess Return. |
| JEL: | C11 C32 E43 F3 G12 G15 |
| Date: | 2015–06 |
| URL: | http://d.repec.org/n?u=RePEc:gla:glaewp:2015_12&r=fmk |
| By: | Maarten van Oordt; Chen Zhou |
| Abstract: | Rules and regulations may have different impacts on risk-taking by individual banks and on banks' systemic risk levels. That is why implementing prudential rules and policies requires careful consideration of their impact on bank risk and systemic risk. This chapter assesses whether market-based measures of systemic risk and recent regulatory indicators provide similar rankings on the systemically importance of large European banks. We find evidence that regulatory indicators of systemic importance are positively related to systemic risk. In particular, banks with higher scores on regulatory indicators have a stronger link to the system in the event of financial stress, rather than having a higher level of bank risk. |
| Keywords: | G-SIBs; financial stability; macroprudential regulation; systemic importance |
| JEL: | G01 G21 G28 |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:478&r=fmk |
| By: | Jitendra Aswani (Indira Gandhi Institute of Development Research) |
| Abstract: | As importance of Asian Stock Markets (ASM) has increased after the globalization, it is become significant to know how this network of ASM behaves on the onset of financial crises. For this study, the Global Financial Crisis is considered whose origin was in the developed country, US, unlike the Asian crisis of 1997. To evaluate the impact of financial crisis on the ASM, network theory is used as a tool here. Network modeling of stock markets is useful as it can help to avert the spillover of crises by preventing the stock markets which are highly connected in the network. In this empirical work, weekly indices data from 2000-2013 for fifteen stock markets is used, which is further partitioned into three periods: pre, during and post crisis. This study shows how 13 important stock markets in Asia namely, India, Bangladesh, Philippines, China, Japan, Indonesia, Malaysia, Singapore, Hong Kong, Pakistan, South Korea and Thailand are connected to each other and how India, Japan, Hong Kong and Korea stock market appeared as the systemically important stock markets from them. Introduction of the US stock market into this network gives insight how the US stock market might had connected to systemically important markets which resulted into spread of crisis in the Asian region. Furthermore, using Kruskal algorithm spread of contagion is explained like how it first hit the Hong Kong stock market and from there it proceeds to the other systemic important stock markets like a virus. Addition to that, we quantified the network behavior in the form of metrics such as adjacency matrix, clustering coefficient, degree of nodes and Minimum Spanning Tree (MST), and on the basis of these some of the important questions like which stock markets are highly connected in Asia which if affected can induce the crises in the other stock markets of region are answered. This study can be used for the portfolio optimization as well as for policy making for which network analysis should be conducted on a regular basis. |
| Keywords: | Financial Crisis, Stock Markets, Networks, Minimum Spanning Tree |
| JEL: | C45 G1 G11 G15 P34 |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:ind:igiwpp:2015-020&r=fmk |