Soo-Hyun Kim, Kyuseok Lee and Hyoung-Goo Kang
There have been the concerns that leveraged and inverse ETFs contribute to the financial crisis of 2007~2008. Several researchers have investigated this important issue. However…
Abstract
There have been the concerns that leveraged and inverse ETFs contribute to the financial crisis of 2007~2008. Several researchers have investigated this important issue. However, there is no consensus yet whether leveraged and inverse ETFs destabilize a financial market. Financial stability is an important subject for policy makers, practitioners and academia. ETFs are one of the most important financial innovations. In particular, leveraged and inverse become more and more influential. Therefore, such lack of academic and practical consensus is a significant challenge. In this paper, we analyze whether leveraged and inverse ETFs affect the price and volatility of Korean market. Thus, our research contributes to the body of literature and to the design of public policies and trading strategies. Our research can also advance the development of ETF industry, one of the fastest growing and promising sector in the Korean financial market.
Details
Keywords
In a recent paper, Yoon and Lee (2019) (YL hereafter) propose a weighted Fama and MacBeth (FMB hereafter) two-step panel regression procedure and provide evidence that their…
Abstract
Purpose
In a recent paper, Yoon and Lee (2019) (YL hereafter) propose a weighted Fama and MacBeth (FMB hereafter) two-step panel regression procedure and provide evidence that their weighted FMB procedure produces more efficient coefficient estimators than the usual unweighted FMB procedure. The purpose of this study is to supplement and improve their weighted FMB procedure, as they provide neither asymptotic results (i.e. consistency and asymptotic distribution) nor evidence on how close their standard error estimator is to the true standard error.
Design/methodology/approach
First, asymptotic results for the weighted FMB coefficient estimator are provided. Second, a finite-sample-adjusted standard error estimator is provided. Finally, the performance of the adjusted standard error estimator compared to the true standard error is assessed.
Findings
It is found that the standard error estimator proposed by Yoon and Lee (2019) is asymptotically consistent, although the finite-sample-adjusted standard error estimator proposed in this study works better and helps to reduce bias. The findings of Yoon and Lee (2019) are confirmed even when the average R2 over time is very small with about 1% or 0.1%.
Originality/value
The findings of this study strongly suggest that the weighted FMB regression procedure, in particular the finite-sample-adjusted procedure proposed here, is a computationally simple but more powerful alternative to the usual unweighted FMB procedure. In addition, to the best of the authors’ knowledge, this is the first study that presents a formal proof of the asymptotic distribution for the FMB coefficient estimator.
Details
Keywords
Jun Sik Kim and Sol Kim
This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications…
Abstract
This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications, citations, impact factors, and centrality indices grew up in early 2010s, and diminished in 2020. Keyword network analysis reveals the JDQS's main keywords including behavioral finance, implied volatility, information asymmetry, price discovery, KOSPI200 futures, volatility, and KOSPI200 options. Citations of JDQS articles are mainly driven by article age, demeaned age squared, conference, nonacademic authors and language. In comparison between number of views and downloads for JDQS articles, we find that recent changes in publisher and editorial and publishing policies have increased visibility of JDQS.
Details
Keywords
In the world of big data, data integration technology is crucial for maximising the capability of data-driven decision-making. Integrating data from multiple sources drastically…
Abstract
Purpose
In the world of big data, data integration technology is crucial for maximising the capability of data-driven decision-making. Integrating data from multiple sources drastically expands the power of information and allows us to address questions that are impossible to answer using a single data source. Record Linkage (RL) is a task of identifying and linking records from multiple sources that describe the same real world object (e.g. person), and it plays a crucial role in the data integration process. RL is challenging, as it is uncommon for different data sources to share a unique identifier. Hence, the records must be matched based on the comparison of their corresponding values. Most of the existing RL techniques assume that records across different data sources are structured and represented by the same scheme (i.e. set of attributes). Given the increasing amount of heterogeneous data sources, those assumptions are rather unrealistic. The purpose of this paper is to propose a novel RL model for unstructured data.
Design/methodology/approach
In the previous work (Jurek-Loughrey, 2020), the authors proposed a novel approach to linking unstructured data based on the application of the Siamese Multilayer Perceptron model. It was demonstrated that the method performed on par with other approaches that make constraining assumptions regarding the data. This paper expands the previous work originally presented at iiWAS2020 [16] by exploring new architectures of the Siamese Neural Network, which improves the generalisation of the RL model and makes it less sensitive to parameter selection.
Findings
The experimental results confirm that the new Autoencoder-based architecture of the Siamese Neural Network obtains better results in comparison to the Siamese Multilayer Perceptron model proposed in (Jurek et al., 2020). Better results have been achieved in three out of four data sets. Furthermore, it has been demonstrated that the second proposed (hybrid) architecture based on integrating the Siamese Autoencoder with a Multilayer Perceptron model, makes the model more stable in terms of the parameter selection.
Originality/value
To address the problem of unstructured RL, this paper presents a new deep learning based approach to improve the generalisation of the Siamese Multilayer Preceptron model and make is less sensitive to parameter selection.