Given the competition for top journal space, there is an incentive to produce "significant" results. With the combination of unreported tests, lack of adjustment for multiple tests, and direct and ...indirect p-hacking, many of the results being published will fail to hold up in the future. In addition, there are basic issues with the interpretation of statistical significance. Increasing thresholds may be necessary, but still may not be sufficient: if the effect being studied is rare, even t > 3 will produce a large number of false positives. Here I explore the meaning and limitations of a p-value. I offer a simple alternative (the minimum Bayes factor). I present guidelines for a robust, transparent research culture in financial economics. Finally, I offer some thoughts on the importance of risk-taking (from the perspective of authors and editors) to advance our field.
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BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
•A novel, hand-collected and publicly available dataset on informal collaboration is sourced from 5631 acknowledgement sections.•Females are acknowledged by fewer papers and by fewer authors than ...comparable males.•The network of informal collaboration contains valuable information on author productivity and paper success that is currently not used.•Centrality in the network of informal collaboration constitutes a novel ranking of researchers.
We present and discuss a novel dataset on informal collaboration in financial economics, manually collected from more than 5,000 acknowledgement sections of published papers. We find that informal collaboration is the norm in financial economics, while generational differences in informal collaboration exist and reciprocity among collaborators prevails. Female researchers appear less often in acknowledgements than comparable male researchers. Information derived from networks of informal collaboration allows us to predict academic impact of both researchers and papers even better than information from co-author networks. Finally, we study the characteristics of the networks using various measures from network theory and characterize what determines a researcher’s position in it. The data presented here may help other researchers to shed light on an under-explored topic.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The “conventional wisdom” in academic and policy circles argues that, while large and foreign banks are generally not interested in serving SMEs, small and niche banks have an advantage because they ...can overcome SME opaqueness through relationship lending. This paper shows that there is a gap between this view and what banks actually do. Banks perceive SMEs as a core and strategic business and seem well-positioned to expand their links with SMEs. The intensification of bank involvement with SMEs in various emerging markets is neither led by small or niche banks nor highly dependent on relationship lending. Moreover, it has not been derailed by the 2007–2009 crisis. Rather, all types of banks are catering to SMEs and large, multiple-service banks have a comparative advantage in offering a wide range of products and services on a large scale, through the use of new technologies, business models, and risk management systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
This paper presents a novel term structure of interest rate (TSIR) model with stochastic volatility and jumps (SVJ) that combines the market framework proposed by Brace et al. (1997) with the ...string-shock framework of Santa-Clara and Sornette (2001). In this model, the factors’ variance is estimated through the eigendecomposition of a variance–covariance matrix obtained with a measure of market volatility derived from out-of-the-money option prices and historical correlations of interest rates traded in the futures market. The stochastic evolution of the factors’ variance is governed by the 4/2 model developed by Grasselli (2017), including jumps. A novel method is employed to estimate the parameters of the SVJ model that minimizes the distance between the sample moments and the moments of a gamma distribution. The empirical application of the model in the Brazilian derivatives market demonstrates its effectiveness in accurately capturing the volatility smile of interest rate options.
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•Introduces a new TSIR model with stochastic volatility and jumps.•Proposes a novel methodology to estimate the time-varying variance–covariance matrix.•Proposes a novel methodology to estimate parameters of the stochastic volatility model.•The model capture the volatility smile of IDI options in the Brazilian financial market.•Analyzes the predictive power of interest rate futures and the options market.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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