Financial stability in response to climate change in a northern temperate economy

Climate change will have considerable impact on the global economy. Estimates of the economic damages due to climate change have focused on the effect of average temperature, but not the effect of other important climate variables. Related research has not explored the sub-annual economic cycles which may be impacted by climate volatility. To address these deficits, we propose a flexible, non-linear framework which includes a wide range of climate variables to estimate changes in GDP and project sub-annual economic cycle adjustments (period, amplitude, trough depth). We find that the inclusion of a more robust set of climate variables improves model performance by over 20%. Importantly, the improved model predicts an increase in GDP rather than a decrease when only temperature is considered. We also find that climate influences the sub-annual economics of all but one province in Canada. Highest stressed were the Prairie and Atlantic regions. Least stressed was the Southeastern region. Our study advances understanding of the nuances in the relationship between climate change and economic output in Canada. It also provides a method that can be applied to related economies globally to target adaptation and resilience management.

Purchasing power parity in GIIPS countries: evidence from unit root tests with breaks and non-linearity

Purpose This paper aims to test purchasing power parity (PPP) hypothesis for Greece, Italy, Ireland, Portugal and Spain, which are known as the GIIPS countries. Design/methodology/approach The authors conduct a comprehensive analysis by using unit root approaches without and with structural breaks and non-linearity. Findings The PPP is valid for the GIIPS countries. Considering structural breaks in non-linear framework plays a crucial role. Originality/value There is no empirical study testing PPP hypothesis by focusing on the GIIPS countries. This study further takes into account for structural breaks and non-linearity in the real exchange rates of these countries.

Extended Fully Fuzzy Linear Regression to Analyze a Solid Cantilever Beam Moment

There are several procedures such as possibilistic and least-square methods to estimate regression models. In this study, first, a fully fuzzy regression equation is converted into a fully fuzzy linear framework. By considering a least-square approach, a model is suggested based on matrix equations for solving fully fuzzy regression models. The main advantage of this method over existing ones is that this method considered values based on their specification, and all linear problems can be easily solved. Moreover, a case study for solid mechanics about the quantity of beam momentum is considered. In this example, the inner data are force values, and the output is momentum values.

Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting

The relationships between crude oil prices and exchange rates have always been of interest to academics and policy analysts. There are theoretical transmission channels that justify such links; however, the empirical evidence is not clear. Most of the studies on causal relationships in this area have been restricted to a linear framework, which can omit important properties of the investigated dependencies that could be exploited for forecasting purposes. Based on the nonlinear Granger causality tests, we found strong bidirectional causal relations between crude oil prices and two currency pairs: EUR/USD, GBP/USD, and weaker between crude oil prices and JPY/USD. We showed that the significance of these relations has changed in recent years. We also made an attempt to find an effective strategy to forecast crude oil prices using the investigated exchange rates as regressors and vice versa. To this aim, we applied Support Vector Regression (SVR)—the machine learning method of time series modeling and forecasting.

Temporally delayed linear modelling (TDLM) measures replay in both animals and humans

There are rich structures in off-task neural activity which are hypothesised to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit – Temporal Delayed Linear Modelling (TDLM) for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, e.g., its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience.

A volatility-match approach to measure performance: the case of socially responsible exchange traded funds (ETFs)

PurposeThis study examines the risk-adjusted performance of socially responsible exchange traded funds (SR ETFs) in comparison to conventional ETFs.Design/methodology/approachThe main empirical result is based on a risk-adjusted performance metric that does not rely on a linear framework. It measures the difference between the returns of an ETF and the returns of a volatility-match and efficient portfolio. In addition, performance is measured using alpha based on single and multifactor formulations.FindingsResults show that the performance of SRI ETFs is not different from the performance of conventional ETFs.Originality/valueGiven the results of the study, socially aware investors can choose to invest in SRI ETFs without sacrificing performance.

Non-linear relationship between foreign currency derivatives and firm value: evidence on Sharīʿah compliant firms

PurposeThis study examines the impact of hedging on firm value of Sharīʿah compliant firms (SCFs) in a non-linear framework.Design/methodology/approachThis study employs the system-GMM for dynamic panel data to examine the influence of derivatives usage on firm value (Tobin's Q, ROA and ROE). The sample comprised of 59 non-financial SCFs engaged in derivatives from 2000 to 2017 (18 years). The Sasabuchi-Lind-Mehlum (SLM) test for U-shaped is performed to confirm the existence of the non-linear relationship.FindingsThis study concludes that hedging significantly contributes to firm value of SCFs based on the non-linear framework. This study suggests that, first, the non-linear relationship occurs due to the different degree of derivatives usage and risk. Second, firms practice selective hedging to maintain the upside potential of firm value.Research limitations/implicationsThis study has important implications. First, the importance of risk management via derivatives to increase firm value, second, the evidence of selective hedging from the non-linear relationship between derivatives and firm value and third, the need for quality reporting on derivatives engagement by firms in line with the required accounting standard on derivatives.Originality/valueThis study fills the gap in the literature in relation to the risk management strategies of SCFs in three aspects. First, re-examines the relationship using recent data. Second, examines the relationship in the non-linear framework as the limited studies found in the literature on Malaysian firms are only based on linear relationship. Third, determines whether hedging undertaken by firms is optimal as this can only be addressed using the non-linear framework. This study is robust to the various definitions of firm value (Tobin's Q, ROA and ROE) and non-linear methodologies.