Special Issue "Financial Statistics and Data Analytics"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematical Finance".

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editors

Dr. Shuangzhe Liu
Website
Guest Editor
Department of Mathematics and Statistics, University of Canberra, Canberra, Australia
Interests: econometrics; statistics; time series; data analytics
Prof. Dr. Milind Sathye
Website
Guest Editor
Department of Accounting, Banking and Finance, University of Canberra, Canberra, ACT 2617, Australia
Interests: efficiency and productivity; E-commerce; Anti-Money laundering and microfinance

Special Issue Information

Dear Colleagues,

Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical techniques can help. Accordingly, finance, statistics and data analytics go hand in hand.

The purpose of this Special Issue is to bring to the fore the state-of-art research in these three areas and especially that which juxtaposes these three. Cross-country contributions are welcome.

The papers can be in any area of finance, statistics or data analytics.

The editorial office provides several Feature Paper quotas for this Special Issue. When accepted after review, these papers will be published free of charge. Feature paper refers to high-quality paper. It is up to Guest Editors to decide whether to grant full waiver to potential authors.

Should you have any question related to Feature Papers, please feel free to contact the Guest Editors or  JRFM’ editorial office ([email protected]).

Dr. Shuangzhe Liu
Prof. Dr. Milind Sathye
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big data analytics
  • Advanced statistics
  • Time series
  • Panel data analysis
  • Financial engineering
  • Risk management in finance
  • Financial institutions

Published Papers (10 papers)

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Open AccessArticle
Ridge Type Shrinkage Estimation of Seemingly Unrelated Regressions And Analytics of Economic and Financial Data from “Fragile Five” Countries
J. Risk Financial Manag. 2020, 13(6), 131; https://doi.org/10.3390/jrfm13060131 - 18 Jun 2020
Abstract
In this paper, we suggest improved estimation strategies based on preliminarily test and shrinkage principles in a seemingly unrelated regression model when explanatory variables are affected by multicollinearity. To that end, we split the vector regression coefficient of each equation into two parts: [...] Read more.
In this paper, we suggest improved estimation strategies based on preliminarily test and shrinkage principles in a seemingly unrelated regression model when explanatory variables are affected by multicollinearity. To that end, we split the vector regression coefficient of each equation into two parts: one includes the coefficient vector for the main effects, and the other is a vector for nuisance effects, which could be close to zero. Therefore, two competing models per equation of the system regression model are obtained: one includes all the regression of coefficients (full model); the other (sub model) includes only the coefficients of the main effects based on the auxiliary information. The preliminarily test estimation improves the estimation procedure if there is evidence that the vector of nuisance parameters does not provide a useful contribution to the model. The shrinkage estimation method shrinks the full model estimator in the direction of the sub-model estimator. We conduct a Monte Carlo simulation study in order to examine the relative performance of the suggested estimation strategies. More importantly, we apply our methodology based on the preliminarily test and the shrinkage estimations to analyse economic data by investigating the relationship between foreign direct investment and several economic variables in the “Fragile Five” countries between 1983 and 2018. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
Robust Inference in the Capital Asset Pricing Model Using the Multivariate t-distribution
J. Risk Financial Manag. 2020, 13(6), 123; https://doi.org/10.3390/jrfm13060123 - 13 Jun 2020
Abstract
In this paper, we consider asset pricing models under the multivariate t-distribution with finite second moment. Such a distribution, which contains the normal distribution, offers a more flexible framework for modeling asset returns. The main objective of this work is to develop [...] Read more.
In this paper, we consider asset pricing models under the multivariate t-distribution with finite second moment. Such a distribution, which contains the normal distribution, offers a more flexible framework for modeling asset returns. The main objective of this work is to develop statistical inference tools, such as parameter estimation and linear hypothesis tests in asset pricing models, with an emphasis on the Capital Asset Pricing Model (CAPM). An extension of the CAPM, the Multifactor Asset Pricing Model (MAPM), is also discussed. A simple algorithm to estimate the model parameters, including the kurtosis parameter, is implemented. Analytical expressions for the Score function and Fisher information matrix are provided. For linear hypothesis tests, the four most widely used tests (likelihood-ratio, Wald, score, and gradient statistics) are considered. In order to test the mean-variance efficiency, explicit expressions for these four statistical tests are also presented. The results are illustrated using two real data sets: the Chilean Stock Market data set and another from the New York Stock Exchange. The asset pricing model under the multivariate t-distribution presents a good fit, clearly better than the asset pricing model under the assumption of normality, in both data sets. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
A Hypothesis Test Method for Detecting Multifractal Scaling, Applied to Bitcoin Prices
J. Risk Financial Manag. 2020, 13(5), 104; https://doi.org/10.3390/jrfm13050104 - 20 May 2020
Abstract
Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly [...] Read more.
Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
Forecasting the Term Structure of Interest Rates with Dynamic Constrained Smoothing B-Splines
J. Risk Financial Manag. 2020, 13(4), 65; https://doi.org/10.3390/jrfm13040065 - 03 Apr 2020
Abstract
The Nelson–Siegel framework published by Diebold and Li created an important benchmark and originated several works in the literature of forecasting the term structure of interest rates. However, these frameworks were built on the top of a parametric curve model that may lead [...] Read more.
The Nelson–Siegel framework published by Diebold and Li created an important benchmark and originated several works in the literature of forecasting the term structure of interest rates. However, these frameworks were built on the top of a parametric curve model that may lead to poor fitting for sensible term structure shapes affecting forecast results. We propose DCOBS with no-arbitrage restrictions, a dynamic constrained smoothing B-splines yield curve model. Even though DCOBS may provide more volatile forward curves than parametric models, they are still more accurate than those from Nelson–Siegel frameworks. DCOBS has been evaluated for ten years of US Daily Treasury Yield Curve Rates, and it is consistent with stylized facts of yield curves. DCOBS has great predictability power, especially in short and middle-term forecast, and has shown greater stability and lower root mean square errors than an Arbitrage-Free Nelson–Siegel model. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
Bank Competition and Credit Risk in Euro Area Banking: Fragmentation and Convergence Dynamics
J. Risk Financial Manag. 2020, 13(3), 57; https://doi.org/10.3390/jrfm13030057 - 16 Mar 2020
Cited by 2
Abstract
Consolidation in euro area banking has been the major trend post-crisis. Has it been accompanied by more or less competition? Has it led to more or less credit risk? In all or some countries? In this study, we examine the evolution of competition [...] Read more.
Consolidation in euro area banking has been the major trend post-crisis. Has it been accompanied by more or less competition? Has it led to more or less credit risk? In all or some countries? In this study, we examine the evolution of competition (through market power and concentration) and credit risk (through non-performing loans) in 2005–2017 across all euro area countries (EA-19), as well as core (EA-Co) and periphery (EA-Pe) countries separately. Using Theil inequality and convergence analysis, our results support the continued existence of fragmentation as well as of divergence within and/or between core and periphery with respect to competition and credit risk, especially post-crisis, in spite of some partial reintegration trends. Policy measures supporting faster convergence of our variables would be helpful in establishing a real banking union. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
A General Family of Autoregressive Conditional Duration Models Applied to High-Frequency Financial Data
J. Risk Financial Manag. 2020, 13(3), 45; https://doi.org/10.3390/jrfm13030045 - 03 Mar 2020
Abstract
In this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter λ to the conditional [...] Read more.
In this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter λ to the conditional median dynamics and an asymmetric response to shocks; this is denoted by GBS-AACD. We then carry out a Monte Carlo simulation study to evaluate the performance of the GBS-ACD models. Finally, an illustration of the proposed models is made by using New York stock exchange (NYSE) transaction data. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
Safe-Haven Assets, Financial Crises, and Macroeconomic Variables: Evidence from the Last Two Decades (2000–2018)
J. Risk Financial Manag. 2020, 13(3), 40; https://doi.org/10.3390/jrfm13030040 - 28 Feb 2020
Abstract
This paper focuses on three “safe haven” assets (gold, oil, and the Swiss Franc) and examines the impact of recent financial crises and some macroeconomic variables on their return co-movements during the last two decades. All financial crises produced significant increases in conditional [...] Read more.
This paper focuses on three “safe haven” assets (gold, oil, and the Swiss Franc) and examines the impact of recent financial crises and some macroeconomic variables on their return co-movements during the last two decades. All financial crises produced significant increases in conditional correlations between these asset returns, thus revealing consistent portfolio shifts from more traditional towards safer financial instruments during turbulent periods. The world equity risk premium stands out as the most relevant macroeconomic variable affecting return co-movements, while economic policy uncertainty indicators also exerted significant effects. Overall, this evidence points out that gold, oil, and the Swiss currency played an important role in global investors’ portfolio allocation choices, and that these assets preserved their essential “safe haven” properties during the period examined. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
Parsimonious Heterogeneous ARCH Models for High Frequency Modeling
J. Risk Financial Manag. 2020, 13(2), 38; https://doi.org/10.3390/jrfm13020038 - 20 Feb 2020
Abstract
In this work we study a variant of the GARCH model when we consider the arrival of heterogeneous information in high-frequency data. This model is known as HARCH(n). We modify the HARCH(n) model when taking into consideration some market [...] Read more.
In this work we study a variant of the GARCH model when we consider the arrival of heterogeneous information in high-frequency data. This model is known as HARCH(n). We modify the HARCH(n) model when taking into consideration some market components that we consider important to the modeling process. This model, called parsimonious HARCH(m,p), takes into account the heterogeneous information present in the financial market and the long memory of volatility. Some theoretical properties of this model are studied. We used maximum likelihood and Griddy-Gibbs sampling to estimate the parameters of the proposed model and apply it to model the Euro-Dollar exchange rate series. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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Open AccessArticle
An Universal, Simple, Circular Statistics-Based Estimator of α for Symmetric Stable Family
J. Risk Financial Manag. 2019, 12(4), 171; https://doi.org/10.3390/jrfm12040171 - 23 Nov 2019
Abstract
The aim of this article is to obtain a simple and efficient estimator of the index parameter of symmetric stable distribution that holds universally, i.e., over the entire range of the parameter. We appeal to directional statistics on the classical result on wrapping [...] Read more.
The aim of this article is to obtain a simple and efficient estimator of the index parameter of symmetric stable distribution that holds universally, i.e., over the entire range of the parameter. We appeal to directional statistics on the classical result on wrapping of a distribution in obtaining the wrapped stable family of distributions. The performance of the estimator obtained is better than the existing estimators in the literature in terms of both consistency and efficiency. The estimator is applied to model some real life financial datasets. A mixture of normal and Cauchy distributions is compared with the stable family of distributions when the estimate of the parameter α lies between 1 and 2. A similar approach can be adopted when α (or its estimate) belongs to (0.5,1). In this case, one may compare with a mixture of Laplace and Cauchy distributions. A new measure of goodness of fit is proposed for the above family of distributions. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)

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Open AccessReply
Reply to “Remarks on Bank Competition and Convergence Dynamics”
J. Risk Financial Manag. 2020, 13(6), 127; https://doi.org/10.3390/jrfm13060127 - 15 Jun 2020
Abstract
In this reply, we provide detailed answers to the remarks made by Tsionas on the use of stochastic frontier-based measures of market power in a part of our empirical study, which examines the fragmentation and convergence dynamics of market power, concentration and credit [...] Read more.
In this reply, we provide detailed answers to the remarks made by Tsionas on the use of stochastic frontier-based measures of market power in a part of our empirical study, which examines the fragmentation and convergence dynamics of market power, concentration and credit risk in the euro area banking sector during 2005–2017. Our answers clarify all the issues raised by Tsionas and show that the only challenging, in our opinion, point of the criticism has been based on a hypothesis that does not hold in the case of our study. Full article
(This article belongs to the Special Issue Financial Statistics and Data Analytics)
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