This paper investigates the return linkages and volatility transmission between oil and stock markets in the Gulf Cooperation Council (GCC) countries over the recent period 2005-2010. We employ a recent generalized VAR-GARCH approach which allows for transmissions in return and volatility. In addition, we analyze the optimal weights and hedge ratios for oil-stock portfolio holdings. On the whole, our results point to the existence of substantial return and volatility spillovers between world oil prices and GCC stock markets, and appear to be crucial for international portfolio management in the presence of oil price risk.

This paper aims to study the role of gold as a hedge against inflation based on local monthly gold prices in China, India, Japan, France, the United Kingdom and the United States of America in periods ranging from 1955 to 2015. We extend the literature by using a novel approach with the nonlinear autoregressive distributed lags (NARDL) model (Shin et al., 2014). The main advantage of this model relies on its ability to simultaneously capture the short-and long-run asymmetries through positive and negative partial sum decompositions of changes in the independent variable(s). Moreover, we rely on local gold prices instead of those from London converted into local currencies like in most of previous studies. The results show that gold is not a hedge against inflation in the long run in all cases. In the short run, gold is an inflation hedge only in the UK, USA, and India. Furthermore, there is no long-run equilibrium between gold prices and the CPI in China, India and France. This difference may be due to traditional aspects of gold and custom controls for gold trade in these countries. Our robustness check suggests that the data time-frequency does not change the specification of the NARDL model but can change conclusions regarding the role of gold as a hedge against inflation in certain countries.

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International audienceIn this article,we use the recently developed nonlinear autoregressive distributedlags (NARDL) model to examine the pass-through of crude oil prices into gasoline and natural gas prices. Our approach allowsus to simultaneously test the short-and long-run nonlinearities through positive and negative partial sum decompositions of the predetermined explanatory variables. It also offers the possibility to quantify the respective responses of gasoline and natural gas prices to positive and negative oil price shocks from the asymmetric dynamic multipliers. The obtained results indicate that oil prices affect gasoline prices and natural gas prices in an asymmetric and nonlinear manner, but the price transmission mechanism is not the same. Important policy implications can be learned from the empirical findings

Unlike prior studies, this study examines the nonlinear, asymmetric and quantile effects of aggregate commodity index and gold prices on the price of Bitcoin. Using daily data from July 17, 2010 to February 2, 2017, we employed several advanced autoregressive distributed lag (ARDL) models. The nonlinear ARDL approach was applied to uncover short-and longrun asymmetries, whereas the quantile ARDL was applied to account for a second type of asymmetry, known as the distributional asymmetry according to the position of a dependent variable within its own distribution. Moreover, we extended the nonlinear ARDL to a quantile framework, leading to a richer new model, which allows testing for distributional asymmetry while accounting for short-and long-run asymmetries. Overall, our results indicate the possibility to predict Bitcoin price movements based on price information from the aggregate commodity index and gold prices. Importantly, we report the nuanced result that most often the relations between bitcoin and aggregate commodity, on the one hand, and between bitcoin and gold, on the other, are asymmetric, nonlinear, and quantiles-dependent, suggesting the need to apply non-standard cointegration models to uncover the complexity and hidden relations between Bitcoin and asset classes.

This paper investigates whether structural breaks and long memory are relevant features in modeling and forecasting the conditional volatility of oil spot and futures prices using three GARCH-type models, i.e., linear GARCH, GARCH with structural breaks and FIGARCH. By relying on a modified version of Inclan and Tiao (1994)'s iterated cumulative sum of squares (ICSS) algorithm, our results can be summarized as follows. First, we provide evidence of parameter instability in five out of twelve GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that forecasting models accommodating for structural break characteristics of the data often outperform the commonly used short-memory linear volatility models. It is however worth noting that the long memory evidence found in the in-sample period is not strongly supported by the out-of-sample forecasting exercise.

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We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semiparametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA-FIGARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.