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In this chapter we consider the “Regularization of Derivative Expectation Operator” (Rodeo) of Lafferty and Wasserman (2008) and propose a modified Rodeo algorithm for semiparametric single index models (SIMs) in big data environment with many regressors. The method assumes sparsity that many of the regressors are irrelevant. It uses a greedy algorithm, in that, to estimate the semiparametric SIM of Ichimura (1993), all coefficients of the regressors are initially set to start from near zero, then we test iteratively if the derivative of the regression function estimator with respect to each coefficient is significantly different from zero. The basic idea of the modified Rodeo algorithm for SIM (to be called SIM-Rodeo) is to view the local bandwidth selection as a variable selection scheme which amplifies the coefficients for relevant variables while keeping the coefficients of irrelevant variables relatively small or at the initial starting values near zero. For sparse semiparametric SIM, the SIM-Rodeo algorithm is shown to attain consistency in variable selection. In addition, the algorithm is fast to finish the greedy steps. We compare SIM-Rodeo with SIM-Lasso method in Zeng et al. (2012). Our simulation results demonstrate that the proposed SIM-Rodeo method is consistent for variable selection and show that it has smaller integrated mean squared errors (IMSE) than SIM-Lasso.

This study provides a comprehensive analysis of the potential contagion of Bitcoin on financial markets and sheds light on the complex interplay between technological advancements, accounting regulatory and financial market stability.

The study employs a multi-faceted approach to analyze the impact of BTC systemic risk, technological factors and regulatory variables on Asia–Pacific financial markets. Initially, a single-index model is used to estimate the systematic risk of BTC to financial markets. The study then uses ordinary least squares (OLS) to assess the potential impact of systemic risk, technological factors and regulatory variables on financial markets. To further control for time-varying factors common to all countries, a fixed effect (FE) panel data analysis is implemented. Additionally, a multinomial logistic regression model is utilized to evaluate the presence of contagion.

Results indicate that Bitcoin's systemic risk to the Asia–Pacific financial markets is relatively weak. Furthermore, technological advancements and international accounting standard adoption appear to indirectly stabilize these markets. The degree of contagion is also found to be stronger in foreign currencies (FX) than in stock index (INDEX) markets.

This study has several limitations that should be considered when interpreting the study findings. First, the definition of financial contagion is not universally accepted, and the study results are based on the specific definition and methodology. Second, the matching of daily financial market and BTC data with annual technological and regulatory variable data may have limited the strength of the study findings. However, the authors’ use of both parametric and nonparametric methods provides insights that may inspire further research into cryptocurrency markets and financial contagions.

Based on the authors analysis, they suggest that financial market regulators prioritize the development and adoption of new technologies and international accounting standard practices, rather than focusing solely on the potential risks associated with cryptocurrencies. While a cryptocurrency crash could harm individual investors, it is unlikely to pose a significant threat to the overall financial system.

To the best of the authors knowledge, they have not found an asset pricing approach to assess a possible contagion. The authors have developed a new method to evaluate whether there is a contagion from BTC to financial markets. A simple but intuitive asset pricing method to evaluate a systematic risk from a factor is a single index model. The single index model has been extensively used in stock markets but has not been used to evaluate the systemic risk potentials of cryptocurrencies. The authors followed Morck et al. (2000) and Durnev et al. (2004) to assess whether there is a systemic risk from BTC to financial markets. If the BTC possesses a systematic risk, the explanatory power of the BTC index model should be high. Therefore, the first implied contribution is to re-evaluate the findings from Aslanidis et al. (2019), Dahir et al. (2019) and Handika et al. (2019), using a different method.

The R environment for statistical computing and graphics (R Development Core Team, 2008) offers practitioners a rich set of statistical methods ranging from random number generation and optimization methods through regression, panel data, and time series methods, by way of illustration. The standard R distribution (base R) comes preloaded with a rich variety of functionality useful for applied econometricians. This functionality is enhanced by user-supplied packages made available via R servers that are mirrored around the world. Of interest in this chapter are methods for estimating nonparametric and semiparametric models. We summarize many of the facilities in R and consider some tools that might be of interest to those wishing to work with nonparametric methods who want to avoid resorting to programming in C or Fortran but need the speed of compiled code as opposed to interpreted code such as Gauss or Matlab by way of example. We encourage those working in the field to strongly consider implementing their methods in the R environment thereby making their work accessible to the widest possible audience via an open collaborative forum.

There is a great deal of research that has been done to investigate the investment characteristics of conventional socially responsible investment portfolios compared to their broader conventional counterparts. However, the impact of incorporating sustainability criteria into the traditional Sharia screening process has not so far been investigated. Therefore, the study aims to give empirical evidence as to whether or not incorporating sustainability socially responsible criteria in the traditional Sharia screening process has a significant impact on the investment characteristics of the Islamic investment portfolio.

The paper examines the investment characteristics of four groups of investment portfolios mainly, Dow Jones Global Index, Dow Jones Sustainability World Index, Dow Jones Islamic Market World Index and Dow Jones Islamic Market Sustainability Index. To improve the robustness of the study, the analysis was carried out at different levels. First, absolute mean return and t‐test were used to examine whether the difference between the different groups of investments is statistically significant or not. Second, risk adjusted equilibrium models, both single‐index and Fama and French multi‐index, were employed. This is to control for different risk exposure and investment style bias associated with different investment portfolios examined.

The paper finds that neither the Sharia nor the sustainability screening process seems to have an adverse impact on the performance and systematic risk of the investment portfolios compared to their unrestricted conventional counterparts. Therefore, Muslim as well as socially responsible investors can choose investments that are consistent with their value systems and beliefs without being forced to sacrifice performance or expose to higher systematic risk.

The study contributes to the existing literature by giving new evidence on the impact of incorporating sustainability criteria into the traditional Sharia screening process that has not so far been investigated.

This paper aims to analyze the portfolio characteristics and the performance measures of sustainability-themed mutual funds, compared to ethical mutual funds that implement different sustainable and responsible investment strategies.

The study refers to a European sample of 106 ethical funds and 51 sustainability-themed funds. The monthly performance of each fund is downloaded from Bloomberg for the period from January 1996 to December 2015. By applying a Fama and French (1993) three-factor model, the authors overcome the limits of a capital asset pricing model (CAPM) based-single index model, to compare the performance of the two categories of funds.

Sustainability-themed funds do not differ significantly from ethical funds in terms of portfolio attributes, except for market capitalization, age and net asset value. Regarding performance measures, the results shows that sustainability-themed funds have a lower underperformance than ethical funds (as measured by Jensen’s alpha), whereas the samples do not differ in terms of market risk (as measured by Beta coefficient). The idiosyncratic risk of sustainability-themed funds is positively influenced by the specific portfolio strategies. The sustainability-themed funds show a higher concentration in the industrial sector and a lower exposure to financial sector than ethical funds; in terms of geographical strategy, they are more global and international oriented; they mainly focus on small caps and value stocks.

The different sustainable and responsible investment strategies can be applied simultaneously and in a growing number of possible combinations. Mutual fund managers can consider thematic approach as an efficient opportunity for reconciling financial performance and economic sustainability. It is demonstrated that sustainability-themed funds adopt a portfolio strategy significantly different from ethical funds and from the environmental, social and governance benchmarks. Mutual fund managers implement a thematic specialization without any negative impact on the funds returns compared to ethical funds; actually, with a proper diversified portfolio, they are able to reduce idiosyncratic risk.

The analysis is extremely innovative, especially for the thematic sample. During the past 15 years, literature about sustainable and responsible investment has been focused especially on the differences in terms of risk and performance between socially responsible and conventional funds. This paper, starting from the methodology applied in these studies, wants to compare two different types of socially responsible strategies, with a specific focus on sustainability-themed mutual funds, given their exponential growth in the past few years.

The beta coefficient used for the cost of equity calculation is at the heart of the valuation process. This study conducts comparative analyses of the classical capital asset pricing model (CAPM) and downside CAPM risk parameters to gain further insight into which risk parameter leads to better performing risk measures at explaining stock returns.

The study conducts a comparative analysis of 16 risk measures at explaining the stock returns of 4531 companies of 20 developed and 25 emerging market index for 2000–2018. The analyses are conducted using both the global and local indices and both USD and local currency returns. Calculated risk measures are analyzed in a panel data setup using a univariate model. Results are investigated in country-specific and model-specific subsets.

The results show that (1) downside betas are better than CAPM betas at explaining the stock returns, (2) both risk measure groups perform better for emerging markets, (3) global downside beta model performs better than global beta model, implying the existence of the contagion effect, (4) high significance levels of total risk and unsystematic risk measures further support the shortfall of CAPM betas and (5) higher correlation of markets after negative shocks such as pandemics puts global CAPM based downside beta to a more reliable position.

The data are limited to the index securities as beta could be time varying.

Results overall provide insight into the cost of equity calculation and emerging market assets valuation.

The framework and methodology enable us to compare and contrast CAPM and downside-CAPM risk measures at the firm level, at the global/local level and in terms of the level of market development.

This study aims to utilize the mean–variance optimization framework of Markowitz (1952) and the generalized reduced gradient (GRG) nonlinear algorithm to find the optimal portfolio that maximizes return while keeping risk at minimum.

This study applies the portfolio optimization concept of Markowitz (1952) and the GRG nonlinear algorithm to a portfolio consisting of the 30 leading stocks from the three different sectors in Amman Stock Exchange over the period from 2009 to 2013.

The selected portfolios achieve a monthly return of 5 per cent whilst keeping risk at minimum. However, if the short-selling constraint is relaxed, the monthly return will be 9 per cent. Moreover, the GRG nonlinear algorithm enables to construct a portfolio with a Sharpe ratio of 7.4.

The results of this study are vital to both academics and practitioners, specifically the Arab and Jordanian investors.

To the best of the author’s knowledge, this is the first study in Jordan and in the Arab world that constructs optimum portfolios based on the mean–variance optimization framework of Markowitz (1952) and the GRG nonlinear algorithm.

The purpose of this paper is to empirically analyze the “market-neutral” characteristics of the market-neutral strategy hedge funds in Chinese A-share market.

The analyses in the paper are conducted to study the market-neutral characteristics by means of index analysis, correlation analysis, β-neutral analysis and the three-factor model analysis.

The results show that the performance advantage of the market-neutral strategy hedge funds is obvious. Most market-neutral strategy funds are exposed to market risks and the α strategy funds also have obvious style factor exposure; strictly speaking, all of the market-neutral strategies have not reached the “market-neutral” requirements. This paper also finds that Chinese trading restrictions on stock index futures in September 2015 have a significant impact on Chinese market-neutral strategy hedge funds.

The conclusion of this paper has a certain reference value for understanding the risk characteristics and possible problems of hedge funds in emerging markets, and also has important reference value for investors.

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals N tends to ∞ and the time period T is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a non-linear positive effect on the economic growth rate.

Observations in a dataset are rarely missing at random. One can control for this non-random selection of the data by introducing fixed effects or other nuisance parameters. This chapter deals with consistent estimation the presence of many nuisance parameters. It derives a new orthogonality concept that gives sufficient conditions for consistent estimation of the parameters of interest. It also shows how this orthogonality concept can be used to derive and compare estimators. The chapter then shows how to use the orthogonality concept to derive estimators for unbalanced panels and incomplete data sets (missing data).