Generalized R-estimators under conditional heteroscedasticity

In this paper, we extend the classical idea of Rank estimation of parameters from homoscedastic problems to heteroscedastic problems. In particular, we define a class of rank estimators of the parameters associated with the conditional mean function of an autoregressive model through a three-steps procedure and then derive their asymptotic distributions. The class of models considered includes Engel's ARCH model and the threshold heteroscedastic model. The class of estimators includes an extension of Wilcoxon-type rank estimator. The derivation of the asymptotic distributions depends on the uniform approximation of a randomly weighted empirical process by a perturbed empirical process through a very general weight-dependent partitioning argument.     

Density estimation for nonlinear parametric models with conditional heteroscedasticity

This article studies density and parameter estimation problems for nonlinear parametric models with conditional heteroscedasticity. We propose a simple density estimate that is particularly useful for studying the stationary density of nonlinear time series models. Under a general dependence structure, we establish the root n$n$ consistency of the proposed density estimate. For parameter estimation, a Bahadur type representation is obtained for the conditional maximum likelihood estimate. The parameter estimate is shown to be asymptotically efficient in the sense that its limiting variance attains the Cramér–Rao lower bound. The performance of our density estimate is studied by simulations.     

The excess co-movement of commodity prices reconsidered

This paper provides an empirical reconsideration of evidence for excess co-movement of commodity prices within the framework of univariate and multivariate GARCH(1, 1) models. Alternative formulations of zero excess co-movement are provided, and corresponding score and likelihood ratio tests are developed. Monthly time series data for two sample periods, 1960–85 and 1974–92, on up to nine commodities are used. In contrast to earlier work, only weak evidence of excess co-movement is found.     

The effects of economic news on commodity prices

We assess how commodity prices respond to macroeconomic news and show that commodities have been relatively insensitive to such news over daily frequencies between 1997 and 2009 compared to other financial assets and major exchange rates. Where commodity prices are influenced by news, there is a pro-cyclical bias and these sensitivities have risen as commodities have become increasingly financialized. However, models based on news still do a relatively poor job of forecasting commodity prices at daily frequencies. We also find some asymmetries in how commodity prices respond to news, most notably for gold, which alone among commodities acts as a safe-haven when “bad” economic news emerges.     

The conditional heteroscedasticity of the yen–dollar exchange rate

This paper examines the conditional heteroscedasticity of the yen–dollar exchange rate. A model is constructed by extending the asymmetric power autoregressive conditional heteroscedasticity model to a process that is fractionally integrated. It is found that, unlike the equity markets, the appreciation and depreciation shocks of the yen against the dollar have similar effects on future volatilities. Although the results reject both the stable and the integrated models, our analysis of the response coefficients of the past shocks and the application of the models to the estimation of the capital requirements for trading the currencies show that there are no substantial differences between the fractionally integrated models and the stable models. © 1998 John Wiley & Sons, Ltd.     

Trends in international commodity prices: Panel unit root analysis

The purpose of this paper is to examine the behavior of international commodity prices within the context of the Prebisch–Singer hypothesis. To this end, I utilize a panel unit root approach which is able to account for multiple structural breaks and cross-section dependency. The unit root analysis for 24 international commodity prices during the period 1900–2003 shows evidence in favor of the trend stationary process in the commodity prices. The results thereby imply that shocks to commodity prices are temporary in nature and tend to be corrected over time. The estimation of the trend stationary models indicates that the Prebisch–Singer hypothesis is not a universal phenomenon.     

On the behavior of commodity prices when speculative storage is bounded

This paper investigates the implications of bounded speculative storage, storage bounded from below at zero and above at a capacity, on commodity prices. Binding capacity mirrors the non-negativity constraint on storage and leads to negative price spiking and higher volatility when the market is in deep contango, i.e. low current prices at high stock levels. With bounded storage there is no need to restrict storage to be costly to ensure a rational expectations equilibrium. This allows the model to cover a wide range of storage technologies, including free and productive storage. We also provide an alternative expression for speculative prices that highlights the key role of the storage boundaries. The competitive equilibrium price is the sum of discounted future probability weighted boundary prices. The boundary prices can be viewed as dividends on commodities in storage reflecting the realization of economic profits from storage.     

The motifs of risk transmission in multivariate time series: Application to commodity prices

In this article we propose to exploit topological information embedded in forecast error variance decomposition derived from large Bayesian vector autoregressive models (VAR) to study network connectedness and risk transmission of multivariate time series observations. Firstly, we design a robust link classification procedure based on shortest paths, so to identify salient directional spillovers in a high-dimensional framework. Secondly, we study recurrent and statistically significant sub-graphs, i.e. network motifs, on the induced network backbone by means of null models which account for local node heterogeneity. The methodology is applied to analyze spillover networks of a set of global commodity prices. We demonstrate that spillovers become key drivers of the system variance during commodity price bubbles and bursts, giving raise to complex triadic structures which do not manifest during normal business periods. By accounting for local node connectivity, we observe a departure from the null models due to the high participation of Crude Oil, Food and Beverages and Raw Materials in complex recurrent sub-graphs.     

Modelling the conditional volatility of commodity index futures as a regime switching process

Commodity index futures offer a versatile tool for gaining different forms of exposure to commodity markets. Volatility is a critical input in many of these applications. This paper examines issues in modelling the conditional variance of futures returns based on the Goldman Sachs Commodity Index (GSCI). Given that commodity markets tend to be ‘choppy’ (Webb, 1987 ), a general econometric model is proposed that allows for abrupt changes or regime shifts in volatility, transition probabilities which vary explicitly with observable fundamentals such as the basis, GARCH dynamics, seasonal variations and conditional leptokurtosis. The model is applied to daily futures returns on the GSCI over 1992–1997. The results show clear evidence of regime shifts in conditional mean and volatility. Once regime shifts are accounted for, GARCH effects are minimal. Consistent with the theory of storage, returns are more likely to switch to the high‐variance state when the basis is negative than when the basis is positive. The regime switching model also performs well in forecasting the daily volatility compared to standard GARCH models without regime switches. The model should be of interest to sophisticated traders who base their trading strategies on short‐term volatility movements, managed commodity funds interested in hedging an underlying diversified portfolio of commodities and investors of options and other derivatives tied to GSCI futures contracts. Copyright © 2001 John Wiley & Sons, Ltd.     

Forecasting stock prices with commodity prices: New evidence from Feasible Quasi Generalized Least Squares (FQGLS) with non-linearities

The complexities in modern stock markets make it imperative to unravel the possible predictors of their future values. This paper thus provides insights into the predictability of stock prices of the BRICS countries with large dependence on commodities either for foreign exchange earnings or industrial while accounting for the role of asymmetries. Essentially, empirical evidence abound for the high volatility in world commodity markets, thus making us to determine if positive and negative changes in commodity prices predict stock prices differently. In addition, unlike the traditional forecast models, our choice of forecast models additionally addresses certain statistical features, including conditional heteroskedasticity, serial dependence, persistence and endogeneity, inherent in the predictors, which have the potential of causing estimation bias. In all, we find evidence in favour of the ability of commodity prices to predict stock prices of Brazil, Russia and South Africa. Also, both the in-sample and out-of-sample forecast performances of the predicted models support asymmetries in a number of commodity prices in each of these three countries. Our results are robust to different data samples and forecast horizons.     

Hypotheses testing concerning relationships between spot prices of various types of coffee

In this paper hypotheses are tested concerning long-run relationships between the four indicator prices of coffee. These relationships are assumed to exist based on a previous study of the coffee market by the same author. The four coffee prices are investigated in more detail in this paper. After a brief introduction to the price formation on the coffee market the univariate properties of the coffee prices are checked first. Then the tests for co-integration, as developed by Johansen (1988) and Johansen and Juselius (1989) are performed. These tests appear to be very informative with respect to the way the prices may be linked in the long run, concerning the number and the form of the relationships. Specifications of three equilibrium relationships among the coffee prices are detected and commented.     

Using extreme value theory to measure value-at-risk for daily electricity spot prices

The recent deregulation in electricity markets worldwide has heightened the importance of risk management in energy markets. Assessing Value-at-Risk (VaR) in electricity markets is arguably more difficult than in traditional financial markets because the distinctive features of the former result in a highly unusual distribution of returns—electricity returns are highly volatile, display seasonalities in both their mean and volatility, exhibit leverage effects and clustering in volatility, and feature extreme levels of skewness and kurtosis. With electricity applications in mind, this paper proposes a model that accommodates autoregression and weekly seasonals in both the conditional mean and conditional volatility of returns, as well as leverage effects via an EGARCH specification. In addition, extreme value theory (EVT) is adopted to explicitly model the tails of the return distribution. Compared to a number of other parametric models and simple historical simulation based approaches, the proposed EVT-based model performs well in forecasting out-of-sample VaR. In addition, statistical tests show that the proposed model provides appropriate interval coverage in both unconditional and, more importantly, conditional contexts. Overall, the results are encouraging in suggesting that the proposed EVT-based model is a useful technique in forecasting VaR in electricity markets.     

Regime switching in stochastic models of commodity prices: An application to an optimal tree harvesting problem

This paper investigates whether a regime switching model of stochastic lumber prices is better for the analysis of optimal harvesting problems in forestry than a more traditional single regime model. Prices of lumber derivatives are used to calibrate a regime switching model, with each of two regimes characterized by a different mean reverting process. A single regime, mean reverting process is also calibrated. The value of a representative stand of trees and optimal harvesting prices are determined by specifying a Hamilton-Jacobi-Bellman Variational Inequality, which is solved for both pricing models using a implicit finite difference approach. The regime switching model is found to more closely match the behavior of futures prices than the single regime model. In addition, analysis of a tree harvesting problem indicates significant differences in terms of land value and optimal harvest thresholds between the regime switching and single regime models.     

The macroeconomic response to real and financial factors,commodity prices,and monetary policy: International evidence

This study estimates a variety of small dynamic factor macro models where the factors are time-varying. The sample consists of 20 economies from around the world. Using quarterly data since the late 1990s, I find that the focus of some policymakers on the negative spillovers from monetary shocks is exaggerated. Four separate types of shocks are identified, and these can easily offset each other with a neutral to positive overall economic impact in some economies investigated here. However, a few economies, namely Brazil, Chile and China, experience a net economic loss from spillovers.     

Model specification of conditional jump intensity: Evidence from S&P 500 returns and option prices

This study investigates the model specification of the conditional jump intensity under option pricing models having a generalized autoregressive conditional heteroskedastic with jumps (GARCH-jump). We compare three GARCH-jump models of Chang, Chang, Cheng, Peng, and Tseng (2018) to examine whether specifying asymmetric jumps in conditional jump intensity can improve the empirical performance. The empirical results from S&P 500 returns and options show that specifying the asymmetric jumps into the conditional jump intensity does improve the in-sample pricing errors and implied volatility errors. However, the out-of-sample results depend on the error measurement.     

Price formation in commodity markets

This paper develops a theory-consistent market model for storable commodities and illustrates its characterization of the data-generating process for a set of major traded commodities. The dynamics of the system incorporate recent advances in modelling techniques. Cointegrated variables in the demand functions are represented by the error correction mechanism (ECM), and expected prices in the stock demand relationship are generated by a rational expectations process. The outside-sample performance of the model is tested against the pure time-series model used to formulate expected prices, and is shown to have a smaller mean square error than that of the time-series model. Thus the model provides comparatively efficient forecasts and, unlike models constructed in their reduced form, permits consideration of key behavioural relationships in commodity markets.     

Bayesian Variable Selection in Spatial Autoregressive Models

This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. It presents two alternative approaches that can be implemented using Gibbs sampling methods in a straightforward way and which allow one to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. A simulation study shows that the variable selection approaches tend to outperform existing Bayesian model averaging techniques in terms of both in-sample predictive performance and computational efficiency. The alternative approaches are compared in an empirical application using data on economic growth for European NUTS-2 regions.