Nonparametric estimators are widely used to estimate the productive efficiency of firms and other organizations, but often without any attempt to make statistical inference. Recent work has provided statistical properties of these estimators as well as methods for making statistical inference, and a link between frontier estimation and extreme value theory has been established. New estimators that avoid many of the problems inherent with traditional efficiency estimators have also been developed; these new estimators are robust with respect to outliers and avoid the well-known curse of dimensionality. Statistical properties, including asymptotic distributions, of the new estimators have been uncovered. Finally, several approaches exist for introducing environmental variables into production models; both two-stage approaches, in which estimated efficiencies are regressed on environmental variables, and conditional efficiency measures, as well as the underlying assumptions required for either approach, are examined. (This abstract was borrowed from another version of this item.)

Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives

COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin

Hedging oil price risk with gold during COVID-19 pandemic

Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm

Do the emerging stock markets react to international economic policy uncertainty, geopolitical risk and financial stress alike?

Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach

The objective of this study is to present a systematic literature review in the context of bank efficiency and productivity. It focuses on the recent developments related to empirical methodological advances and new dimensions added to the ever-growing field of bank performance analysis. Selected research papers were coded in terms of their key objectives and were segregated into 11 themes—Branch, Comparison, Consolidation and Expansion, Deregulation and Regulation, Environment, Input–output, Methodological advances, Non-traditional activities, Risk, Stock performance and Others. The 103 selected studies were further analysed based on efficiency measures, input–output approaches and methodology. While summarising the extant literature on bank efficiency and productivity, the ongoing debate regarding the optimal input output approaches and ideal frontier techniques for bank performance analysis has also been dealt with. The current study also highlights the possible future research avenues in this area.

A review of bank efficiency and productivity

This study examines the dynamic relationship between precious metals and stock markets of major developed (G7) and emerging (BRICS) nations. We use a hybrid wavelet-based Dynamic Conditional Correlation (DCC) approach, which allows us to investigate the dynamic relationship between precious metals and stock markets in time and frequency domain. Our results suggest that DCC between the return series of precious metals and stock market varies with timescales in terms of dynamicity, persistence and strength of relationship. G7 and BRICS nations exhibit different dynamicity with precious metals over the study period (2000 to 2017). The dynamics between precious metals and G7 stock markets show similar patterns, which depicts a clustering behaviour, however, same is not true for BRICS nations. In contrast to existing literature, this study found that silver offers better hedging capability than other precious metals for both short and long-run. To construct a two-asset optimal portfolio of precious metal and stock index, palladium emerges as the most favourable option for both short and long-run.

Hedging effectiveness of precious metals across frequencies: Evidence from Wavelet based Dynamic Conditional Correlation analysis

The authors revisit the relationship between US economic growth and crude oil prices considering Industrial Production Index and West Texas Intermediate crude oil spot prices as respective proxies for a period spanning over January 1986 to June 2017. To capture the asymmetric and time-varying relationship, the authors employ maximum overlap discrete wavelet transform (MODWT)-based quantile regression (QR) analysis. Interestingly, MODWT-based QR analysis provides evidence of supply-driven link between crude oil prices and economic growth in the short run. However, in the medium to long run a demand-driven link is dominant. In addition, the QR results without MODWT also advocate a demand-driven link. Overall, the result of this study adds a new dimension to the literature on the relationship between crude oil prices and economic growth by focusing upon the time-frequency varying business cycle fluctuations.

The relationship between oil prices and US economy revisited

In this paper, we analyzed changing comovement relationship between prices of crude oil and food crops/food index using a detrended cross-correlation analysis (DCCA) approach proposed by Podobnik and Stanley (2008). In order to detect the changing nature of cross-correlation coefficient over time, we estimated the detrended cross-correlation coefficient on a rolling window and found that cross-correlations were negative before 2004 for most of the items but they increased considerably after the surge in crude oil price in 2007 that caused diversion of food grains for production of biofuels.

Vaneet Bhatia

Papers

Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach

A review of bank efficiency and productivity

Hedging effectiveness of precious metals across frequencies: Evidence from Wavelet based Dynamic Conditional Correlation analysis

The relationship between oil prices and US economy revisited

Changing value detrended cross correlation coefficient over time: Between crude oil and crop prices