Editorial

3 pages, 172 KiB

Open AccessEditorial

Nonparametric Econometric Methods and Applications

by Thanasis Stengos

J. Risk Financial Manag. 2019, 12(4), 180; https://doi.org/10.3390/jrfm12040180 - 30 Nov 2019

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An area of very active research in econometrics over the last 30 years has been that of non- and semi-parametric methods. These methods have provided ways to complement more-traditional parametric approaches in terms of robust alternatives, as well as preliminary data analysis. The [...] Read more.

An area of very active research in econometrics over the last 30 years has been that of non- and semi-parametric methods. These methods have provided ways to complement more-traditional parametric approaches in terms of robust alternatives, as well as preliminary data analysis. The present Special Issue collects a number of new contributions, both theoretical and empirical that cover a wide spectrum of areas such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth as well as statistical theory and methodology. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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34 pages, 1170 KiB

Open AccessArticle

Nonparametric Approach to Evaluation of Economic and Social Development in the EU28 Member States by DEA Efficiency

by Lukáš Melecký, Michaela Staníčková and Jana Hančlová

J. Risk Financial Manag. 2019, 12(2), 72; https://doi.org/10.3390/jrfm12020072 - 24 Apr 2019

Cited by 4 | Viewed by 3121

Abstract

Data envelopment analysis (DEA) methodology is used in this study for a comparison of the dynamic efficiency of European countries over the last decade. Moreover, efficiency analysis is used to determine where resources are distributed efficiently and/or were used efficiently/inefficiently under factors of [...] Read more.

Data envelopment analysis (DEA) methodology is used in this study for a comparison of the dynamic efficiency of European countries over the last decade. Moreover, efficiency analysis is used to determine where resources are distributed efficiently and/or were used efficiently/inefficiently under factors of competitiveness extracted from factor analysis. DEA measures numerical grades of the efficiency of economic processes within evaluated countries and, therefore, it becomes a suitable tool for setting an efficient/inefficient position of each country. Most importantly, the DEA technique is applied to all (28) European Union (EU) countries to evaluate their technical and technological efficiency within the selected factors of competitiveness based on country competitiveness index in the 2000–2017 reference period. The main aim of the paper is to measure efficiency changes over the reference period and to analyze the level of productivity in individual countries based on the Malmquist productivity index (MPI). Empirical results confirm significant disparities among European countries and selected periods 2000–2007, 2008–2011, and 2012–2017. Finally, the study offers a comprehensive comparison and discussion of results obtained by MPI that indicate the EU countries in which policy-making authorities should aim to stimulate national development and provide more quality of life to the EU citizens. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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16 pages, 1407 KiB

Open AccessArticle

Smoothed Maximum Score Estimation of Discrete Duration Models

by Sadat Reza and Paul Rilstone

J. Risk Financial Manag. 2019, 12(2), 64; https://doi.org/10.3390/jrfm12020064 - 15 Apr 2019

Cited by 1 | Viewed by 2378

Abstract

This paper extends Horowitz’s smoothed maximum score estimator to discrete-time duration models. The estimator’s consistency and asymptotic distribution are derived. Monte Carlo simulations using various data generating processes with varying error distributions and shapes of the hazard rate are conducted to examine the [...] Read more.

This paper extends Horowitz’s smoothed maximum score estimator to discrete-time duration models. The estimator’s consistency and asymptotic distribution are derived. Monte Carlo simulations using various data generating processes with varying error distributions and shapes of the hazard rate are conducted to examine the finite sample properties of the estimator. The bias-corrected estimator performs reasonably well for the models considered with moderately-sized samples. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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22 pages, 814 KiB

Open AccessArticle

Growth and Debt: An Endogenous Smooth Coefficient Approach

by Mustafa Koroglu

J. Risk Financial Manag. 2019, 12(1), 23; https://doi.org/10.3390/jrfm12010023 - 01 Feb 2019

Cited by 3 | Viewed by 3945

Abstract

The new growth theories with an emphasis on fundamental determinants such as institutions suggest a non-linear cross-country growth process. In this paper, we investigate the public debt and economic growth relationship using the semi-parametric smooth coefficient approach that allows democracy to influence this [...] Read more.

The new growth theories with an emphasis on fundamental determinants such as institutions suggest a non-linear cross-country growth process. In this paper, we investigate the public debt and economic growth relationship using the semi-parametric smooth coefficient approach that allows democracy to influence this relationship and parameter heterogeneity in the unknown functional form and addresses the endogeneity of variables. We find results consistent with the previous literature that identified a significant adverse effect of public debt on growth for the countries below a particular democracy level. However, we also find conclusive evidence that countries with high institutional quality have an adverse effect of public debt on growth for the period 1980–2009, as well as for the extended period including the years 2010–2014. A 10-percentage point increase in the debt-to-GDP ratio is associated with a 0.12% and 0.07% decrease in the subsequent 10-year period real GDP growth rate for the zero democracy countries and for the countries with a democracy score of 10, respectively. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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15 pages, 491 KiB

Open AccessFeature PaperArticle

Forecasting of Realised Volatility with the Random Forests Algorithm

by Chuong Luong and Nikolai Dokuchaev

J. Risk Financial Manag. 2018, 11(4), 61; https://doi.org/10.3390/jrfm11040061 - 11 Oct 2018

Cited by 22 | Viewed by 7062

Abstract

The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply [...] Read more.

The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply the random forests algorithm for the forecasting of the direction and the magnitude of the realised volatility. In experiments with historical high frequency data, we demonstrate improvements of forecast accuracy for the proposed model. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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29 pages, 798 KiB

Open AccessArticle

Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

by Mark J. Jensen and John M. Maheu

J. Risk Financial Manag. 2018, 11(3), 52; https://doi.org/10.3390/jrfm11030052 - 05 Sep 2018

Cited by 6 | Viewed by 3224

Abstract

In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation [...] Read more.

In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation of the observables’ joint distribution. Our nonparametric estimator allows for deviation from conditional Gaussianity through non-zero, higher ordered, moments, like asymmetric, fat-tailed behavior, along with smooth, nonlinear, risk–return relationships. We use the parsimonious and relatively uninformative Bayesian Dirichlet process prior to overcoming the problem of having too many unknowns and not enough observations. Applying our Bayesian nonparametric model to more than a century’s worth of monthly US stock market returns and realized variances, we find strong, robust evidence of volatility feedback. Once volatility feedback is accounted for, we find an unambiguous positive, nonlinear, relationship between expected excess returns and expected log-realized variance. In addition to the conditional mean, volatility feedback impacts the entire joint distribution. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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15 pages, 562 KiB

Open AccessArticle

Monte Carlo Comparison for Nonparametric Threshold Estimators

by Chaoyi Chen and Yiguo Sun

J. Risk Financial Manag. 2018, 11(3), 49; https://doi.org/10.3390/jrfm11030049 - 17 Aug 2018

Cited by 1 | Viewed by 3306

Abstract

This paper compares the finite sample performance of three non-parametric threshold estimators via the Monte Carlo method. Our results indicate that the finite sample performance of the three estimators is not robust to the position of the threshold level along the distribution of [...] Read more.

This paper compares the finite sample performance of three non-parametric threshold estimators via the Monte Carlo method. Our results indicate that the finite sample performance of the three estimators is not robust to the position of the threshold level along the distribution of the threshold variable, especially when a structural change occurs at the tail part of the distribution. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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22 pages, 425 KiB

Open AccessArticle

On the Performance of Wavelet Based Unit Root Tests

by Burak Alparslan Eroğlu and Barış Soybilgen

J. Risk Financial Manag. 2018, 11(3), 47; https://doi.org/10.3390/jrfm11030047 - 13 Aug 2018

Cited by 16 | Viewed by 3367

Abstract

In this paper, we apply the wavelet methods in the popular Augmented Dickey-Fuller and M types of unit root tests. Moreover, we provide an extensive comparison of the wavelet based unit root tests which also includes the recent contributions in the literature. Moreover, [...] Read more.

In this paper, we apply the wavelet methods in the popular Augmented Dickey-Fuller and M types of unit root tests. Moreover, we provide an extensive comparison of the wavelet based unit root tests which also includes the recent contributions in the literature. Moreover, we derive the asymptotic properties of the wavelet based unit root tests under generalized least squares detrending mechanism. We demonstrate that the wavelet based M tests exhibit better size performance even in problematic cases such as the presence of negative moving average innovations. However, the power performances of the wavelet based unit root tests are quite similar to each other. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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13 pages, 1269 KiB

Open AccessFeature PaperArticle

Financial Development and Countries’ Production Efficiency: A Nonparametric Analysis

by Nickolaos G. Tzeremes

J. Risk Financial Manag. 2018, 11(3), 46; https://doi.org/10.3390/jrfm11030046 - 07 Aug 2018

Cited by 2 | Viewed by 3039

Abstract

This paper examines the effect of financial development on countries’ production efficiency levels. By applying a probabilistic framework it develops robust (Order-m) time-dependent conditional nonparametric frontier estimators in order to measure 87 countries’ production efficiency levels over the period 1970–2014. In order to [...] Read more.

This paper examines the effect of financial development on countries’ production efficiency levels. By applying a probabilistic framework it develops robust (Order-m) time-dependent conditional nonparametric frontier estimators in order to measure 87 countries’ production efficiency levels over the period 1970–2014. In order to examine the effect of time and domestic credit on countries’ production efficiency levels, a second-stage nonparametric econometric analysis is performed. Specifically, generalized additive models with tensor products and cubic spline penalties are applied in order to investigate the potential nonlinear behavior of financial development on countries’ production efficiency levels. The results reveal that the effect of financial development on production efficiency is nonlinear. Specifically, the effect is positive up to a certain credit level after which it becomes negative. Finally, the evidence suggests that the effect is influenced by a country’s financial system, institutional, and development characteristics. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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10 pages, 324 KiB

Open AccessFeature PaperArticle

Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model

by Karen X. Yan and Qi Li

J. Risk Financial Manag. 2018, 11(3), 44; https://doi.org/10.3390/jrfm11030044 - 03 Aug 2018

Cited by 2 | Viewed by 3628

Abstract

This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction. [...] Read more.

This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction. Monte Carlo simulations show that the proposed estimator performs well in finite samples. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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14 pages, 410 KiB

Open AccessFeature PaperArticle

Greenhouse Emissions and Productivity Growth

by Pantelis Kalaitzidakis, Theofanis P. Mamuneas and Thanasis Stengos

J. Risk Financial Manag. 2018, 11(3), 38; https://doi.org/10.3390/jrfm11030038 - 09 Jul 2018

Cited by 14 | Viewed by 4036

Abstract

In this paper, we examine the effect of emissions, as measured by carbon dioxide (CO

_{2}

), on economic growth among a set of OECD countries during the period 1981–1998. We examine the relationship between total factor productivity (TFP) growth and emissions using [...] Read more.

In this paper, we examine the effect of emissions, as measured by carbon dioxide (CO

_{2}

), on economic growth among a set of OECD countries during the period 1981–1998. We examine the relationship between total factor productivity (TFP) growth and emissions using a semiparametric smooth coefficient model that allow us to directly estimate the output elasticity of emissions. The results indicate that there exists a monotonically-increasing relationship between emissions and TFP growth. The output elasticity of CO

_{2}

emissions is small with an average sample value of 0.07. In addition, we find an average contribution of CO

_{2}

emissions to productivity growth of about 0.063 percent for the period 1981–1998. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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20 pages, 2459 KiB

Open AccessArticle

Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study

by Yiguo Sun and Ximing Wu

J. Risk Financial Manag. 2018, 11(2), 29; https://doi.org/10.3390/jrfm11020029 - 08 Jun 2018

Cited by 4 | Viewed by 4183

Abstract

This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different [...] Read more.

This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different segments of the market return distribution. We find that: (a) the two series exhibit strong, negative, extreme tail dependence; (b) the negative dependence is stronger in extreme bearish markets than in extreme bullish markets; (c) the dependence gradually weakens as the market return moves toward the center of its distribution, or in quiet markets. The unique dependence structure supports the VIX as a barometer of markets’ mood in general. Moreover, applying the proposed method to the S&P 500 returns and the implied variance (VIX²), we find that the nonparametric leverage effect is much stronger than the nonparametric volatility feedback effect, although, in general, both effects are weaker than the dependence relation between the market returns and the log-increments of the VIX. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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