Contemporary structural models of the global market for crude oil jointly specify precautionary and speculative demand shocks as a composite shock, named a storage demand shock. We resolve this identification problem and examine the effects of these distinct shocks, along with conventional demand and supply shocks, on the global price of crude oil. We find that uncertainty driven precautionary demand for crude oil is, on average, the primary driver of real price of oil fluctuations that have previously been associated with storage demand shocks. Historically, these shocks have had distinct effects on the real oil price dynamics since the 1970s.
Aastveit, Knut Are; Cross, Jamie & van Dijk, Herman K. (2022)
Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil
We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time- varying forecast uncertainty and risk for the real price of oil over the period 1974–2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.
Cross, Jamie; Hou, Chenghan & Trinh, Kelly (2021)
Returns, Volatility and the Cryptocurrency Bubble of 2017-18
Research on cryptocurrencies has focused on price and volatility formation in isolation, however knowledge about their interdependence is important for risk management and asset allocation. We investigate the existence and nature of such a relationship in four commonly traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple, during the cryptocurrency bubble of 2017–18. Using a generalized asset pricing model, we find evidence of a risk premium effect in Litecoin and Ripple during the boom of 2017, and that adverse news effects were an important driver of the cryptocurrency crash of 2018 in all four cryptocurrencies. In an out-of-sample forecasting exercise, we find that allowing for stochastic volatility and a heavy tailed distribution provides more accurate return and volatility forecasts compared to a random walk benchmark. This suggests that cryptocurrency markets were not weak-form efficient during this period.
Guo, Na; Zhang, Bo & Cross, Jamie (2021)
Time-varying trend models for forecasting inflation in Australia
We investigate whether a class of trend models, which decompose a time series into an underlying trend and transitory component, with various error term structures can improve upon the forecast performance of commonly used time series models when forecasting consumer price index (CPI) inflation in Australia. The main result is that trend models tend to provide more accurate point and density forecasts at medium to long forecasting horizons compared with conventional autoregressive and Phillips curve models. The best medium‐term point forecasts come from a trend model with stochastic volatility in the transitory component and that with a moving average component, whereas long‐run point forecasts are better made by trend models with stochastic volatilities and a moving average component. In a full sample study, we also find that trend models can capture various dynamics in periods of significance to the Australian economy which conventional models cannot. This includes the dramatic reduction in inflation when the RBA adopted inflation targeting, a one‐off 10% Goods and Services Tax inflationary episode in 2000, and then gradually decline in inflation since 2014
Cross, Jamie; Hou, Chenghan & Nguyen, Bao (2021)
On the China factor in the world oil market: A regime switching approach
We investigate the relationship between China's macroeconomic performance and the world oil market over the past two decades. Unlike existing studies, we allow for possible regime changes by utilizing a class of Markov-switching vector autoregression (MS-VAR) models. The model identifies key regime changes in the structural shocks when the oil market experiences low and high volatility. We find that demand shocks from China and the rest of the world have a larger impact on the real price of crude oil during periods of high volatility. Supply shocks, in contrast, have a large effect on the price in the low volatility regime. A similar state-dependent phenomenon is observed for the impact of oil price shocks on China economic activity, however the size of these responses is relatively small. Thus, despite China being a major player in international oil markets, we conclude that oil market shocks tend to have little impact on China's real GDP growth.
Zhang, Bo; Chan, Joshua & Cross, Jamie (2020)
Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts
We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence, which results in standard Kalman filter techniques not being directly applicable. To overcome this hurdle, we develop an efficient posterior simulator that builds on recently developed precision-based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy, and the U.S.
Cross, Jamie; Hou, Chenghan & Poon, Aubrey (2020)
Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity
A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector autoregressions (BVARs) has recently been proposed. We question whether three such priors: Dirichlet-Laplace, Horseshoe, and Normal-Gamma, can systematically improve the forecast accuracy of two commonly used benchmarks (the hierarchical Minnesota prior and the stochastic search variable selection (SSVS) prior), when predicting key macroeconomic variables. Using small and large data sets, both point and density forecasts suggest that the answer is no. Instead, our results indicate that a hierarchical Minnesota prior remains a solid practical choice when forecasting macroeconomic variables. In light of existing optimality results, a possible explanation for our finding is that macroeconomic data is not sparse, but instead dense.
Cross, Jamie & Poon, Aubrey (2019)
On the Contribution of International Shocks in Australian Business Cycle Fluctuations
What proportion of Australian business cycle fluctuations are caused by international shocks? We address this question by estimating a panel VAR model that has time-varying parameters and a common stochastic volatility factor. The time-varying parameters capture the inter-temporal nature of Australia’s various bilateral trade relationships, while the common stochastic volatility factor captures various episodes of volatility clustering among macroeconomic shocks, e.g., the 1997/98 Asian Financial Crisis and the 2007/08 Global Financial Crisis. Our main result is that international shocks from Australia’s five largest trading partners: China, Japan, the EU, the USA and the Republic of Korea, have caused around half of all Australian business cycle fluctuations over the past two decades. We also find important changes in the relative importance of each country’s economic impact. For instance, China’s positive contribution increased throughout the mining boom of the 2000s, while the overall US influence has almost halved since the 1990s