The aim of this study is threefold; first, the study investigates the symmetric impact of trade openness, financial development, and institutional quality on environmental degradation and environmental sustainability. Second, the study examines the asymmetric relationship between financial development, institutional quality, and environmental degradation. Third, the study examines the asymmetric relationship between financial development, institutional quality, and environmental sustainability. For this purpose, the study utilized the data of Pakistan from 1996 to 2018. The study applied Augmented Dickey–Fuller (ADF), Phillips Parron (PP) and Zivote, and Andrews unit root test to check the properties of stationarity of the data. This study applied the Auto Regressive Distributive Lags (ARDL) model to investigate symmetric relationships while the Non-Linear Auto Regressive Distributive Lag Model (NARDL) approach is utilized to investigate the asymmetric relationship among variables. ARDL bounds testing approach utilized to investigate long-run co-integration while short-run dynamics have been investigated by applying the error correction method (ECM). This study found the significant long-run symmetric and asymmetric association of institutional quality (IQ) and financial development (FD) with environmental degradation (ED) and environmental sustainability. However, IQ- has an insignificant association with environmental sustainability. Moreover, dynamic multiplier analysis indicates that positive shock to FD and IQ has a stronger impact on environmental degradation while a positive or negative shock to FD; both have a stronger impact on environmental sustainability. However, a positive or negative shock to IQ has a smaller impact on environmental sustainability. Moreover, the study also found a significant long-run symmetric association of trade openness with environmental degradation and environmental sustainability. This study suggests that the quality of institutions, financial development, and trade openness is necessary to enhance the quality of the environment.

The environmental degradation and the concern for sustainable development have garnered extensive attention from researchers to evaluate the prospects of green bonds over other traditional assets. Against this backdrop, the current study measures the asymmetric relationship between green bonds, U.S. economic policy uncertainty (EPU), and bitcoins by employing the Nonlinear Autoregressive Distribution Lag (NARDL) estimation technique recently developed by Shin et al. The outcome of the empirical analysis confirms an asymmetric cointegration between EPU, bitcoins, the clean energy index, oil prices, and green bonds. The NARDL estimation substantiates that positive shock in EPU exerts a negative impact on green bonds, whereas a negative shock in EPU increases the performance of green bonds. It implies, in the long run, a 1 percent increase (decrease) in EPU decreases (increases) the performance of green bonds by 0.22 percent and 0.11 percent, respectively. Likewise, the study also confirms a bidirectional relationship between bitcoins and green bonds. A positive shock in bitcoin increases the performance of green bonds and vice versa. In addition, our study also reveals a direct co-movement between clean energy, oil prices, and green bonds. This outcome implies that green bonds are not a different asset class, and they mirror the performance of other asset classes, such as clean energy, oil prices, and bitcoins. The findings offer several implications to understand the hedging and diversification properties of bitcoins, and assist in understanding the role of U.S. economic policy uncertainty on green bonds.

The field of econophysics is still very young and is in constant evolution. One of the great innovations in finance coming from econophysics is the fractal market hypothesis, which contradicts the traditional efficient market hypothesis. From fractal market hypothesis new studies/models have emerged. The aim of this work is to review the bibliography on some of these new models, specifically those based on the Hurst exponent, explaining how they work, outline different forms of calculation and, finally, highlighting some of the empirical applications they have within the study of the financial market.

The main goal of the paper is to introduce different models to calculate the amount of money that must be allocated to each stock in a statistical arbitrage technique known as pairs trading. The traditional allocation strategy is based on an equal weight methodology. However, we will show how, with an optimal allocation, the performance of pairs trading increases significantly. Four methodologies are proposed to set up the optimal allocation. These methodologies are based on distance, correlation, cointegration and Hurst exponent (mean reversion). It is showed that the new methodologies provide an improvement in the obtained results with respect to an equal weighted strategy.