Forecasting crude oil futures market returns: A principal component analysis combination approach

To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.     

Downside/upside price spillovers between precious metals: A vine copula approach

We studied downside and upside price spillovers between four precious metals (gold, silver, platinum and palladium), characterizing the multivariate dependence structure using a vine copula model and computing downside and upside value-at-risk and conditional value-at-risk. We found that the dependence structure differed across precious metals, all of which displayed different average and tail dependence features. Gold and silver prices were highly dependent except at the upper tail, whereas silver prices were integrated with those for platinum and palladium except at the upper tail. The gold market was very little integrated with the platinum and palladium markets. We document asymmetric downside and upside price spillover effects that differed in magnitude across precious metals; silver, in particular, had a greater downside and upside price impact on gold. Our results, indicating that precious metals do not behave as a single asset class, have implications for risk management, trading and hedging strategies for portfolios that include precious metals.     

Risk management of precious metals

This paper examines volatility and correlation dynamics in price returns of gold, silver, platinum and palladium, and explores the corresponding risk management implications for market risk and hedging. Value-at-Risk (VaR) is used to analyze the downside market risk associated with investments in precious metals, and to design optimal risk management strategies. We compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi-parametric Filtered Historical Simulation approach. The best approach for estimating VaR based on conditional and unconditional statistical tests is documented. The economic importance of the results is highlighted by assessing the daily capital charges from the estimated VaRs.     

Downside risk management and VaR-based optimal portfolios for precious metals,oil and stocks

Value-at-Risk (VaR) is used to analyze the market downside risk associated with investments in six key individual assets including four precious metals, oil and the S&P 500 index, and three diversified portfolios. Using combinations of these assets, three optimal portfolios and their efficient frontiers within a VaR framework are constructed and the returns and downside risks for these portfolios are also analyzed. One-day-ahead VaR forecasts are computed with nine risk models including calibrated RiskMetrics, asymmetric GARCH type models, the filtered Historical Simulation approach, methodologies from statistics of extremes and a risk management strategy involving combinations of models. These risk models are evaluated and compared based on the unconditional coverage, independence and conditional coverage criteria. The economic importance of the results is also highlighted by assessing the daily capital charges under the Basel Accord rule. The best approaches for estimating the VaR for the individual assets under study and for the three VaR-based optimal portfolios and efficient frontiers are discussed. The VaR-based performance measure ranks the most diversified optimal portfolio (Portfolio #2) as the most efficient and the pure precious metals (Portfolio #1) as the least efficient.     

Non-linear volatility dynamics and risk management of precious metals

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with non-linear long memory volatility models, namely FIGARCH, FIAPARCH and HYGARCH, under normal and Student-t innovations’ distributions. For these analyses, we consider both long and short trading positions. Overall, our results reveal that long memory volatility models under Student-t distribution perform well in forecasting a one-day-ahead VaR for both long and short positions. In addition, we find that FIAPARCH model with Student-t distribution, which jointly captures long memory and asymmetry, as well as fat-tails, outperforms other models in VaR forecasting. Our results have potential implications for portfolio managers, producers, and policy makers.     

OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach

This paper provides a novel perspective to the predictive ability of OPEC meeting dates and production announcements for (Brent Crude and West Texas Intermediate) oil futures market returns and GARCH-based volatility using a nonparametric quantile-based methodology. We show a nonlinear relationship between oil futures returns and OPEC-based predictors; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. When the quantile-causality test is implemented, we observe that the impact of OPEC variables is restricted to Brent Crude futures only (with no effect observed for the WTI market). Specifically, OPEC production announcements, and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market returns. While, predictability of volatility covers the majority of the quantile distribution, barring extreme ends.     

Volatility contagion: A range-based volatility approach

This article proposes a new approach to evaluate volatility contagion in financial markets. A time-varying logarithmic conditional autoregressive range model with the lognormal distribution (TVLCARR) is proposed to capture the possible smooth transition in the range process. Additionally, a smooth transition copula function is employed to detect the volatility contagion between financial markets. The approach proposed is applied to the stock markets of the G7 countries to investigate the volatility contagion due to the subprime mortgage crisis. Empirical evidence shows that volatility is contagious from the US market to several markets examined.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

The customer consequences of returns in online retailing: An empirical analysis

Pressure continues to build on the operations management function to facilitate system and firm level benefits. In the online marketplace, one area of growing interest is that of product returns. Though commonly viewed as a cost center from an operations perspective, operations’ actions have the potential to strongly influence future customer buying behavior in several ways. Using an archival database of actual purchase and returns history provided by a moderately sized online retailer, this study examines the relationship between a customer's experience of product returns, and subsequent shopping behavior. Employing transaction cost, consumer risk, and procedural justice theories, we demonstrate that the returns management process, rather than being regarded as an afterthought to the production and deployment of goods, can significantly and positively influence repurchase behavior. Additionally, we provide evidence that certain customers should be considered for prioritization in the returns process. We suggest ways through which operations managers can take care in discharging their responsibilities in this area – to make returns processing more than simply a “necessary cost of doing business” rather, using it to their advantage in engendering repeat and increased purchase behavior.     

SPACs: An overview and assessment of returns

Special purpose acquisition companies (SPACs) originated in the 1980s. A SPAC is a shell company formed by a sponsor to raise capital through a SPAC IPO to acquire or merge with an existing (target) private company. In a SPAC IPO, the units issued, consisting of (1) shares of common stock and (2) warrants, are typically priced at a nominal $10. Until 2022, SPAC IPOs were an increasingly popular alternative to traditional IPOs. We provide an overview of SPACs and an assessment of two measures of returns to SPACs, one is market-adjusted buy and holds abnormal returns, and the other is risk-adjusted abnormal returns by estimating a three-factor regression model. The return calculations are based on 299 SPAC completed mergers between January 2013 and December 2021. Our results indicate that the main driver in a series of regressions, including various explanatory variables in explaining deSPAC returns, is the extent of warrants issued in a SPAC IPO, and robustness checks confirm these results.     

Dynamic relations between oil and stock market returns: A multi-country study

We study the relation between the BRENT and seventeen stock market indexes of important oil-dependent economies. We focus on connectedness between these markets and characterize the dynamics of transmission and reception. We use LASSO methods to shrink, select, and estimate the high dimensional network linking these markets between August, 1999 and March, 2018. This methodological innovation allows the inclusion of a significantly larger number of markets in the network, providing finer results regarding connectedness in the oil-stock market nexus. We show that transmission runs mainly from stock markets to the BRENT. Connectedness varies considerably over time, reaching peaks during times of financial distress. Dynamic predictive causality tests show evidence of time-varying bidirectional causality. Causality from stock markets to the BRENT is detected mostly for the last part of the sample period. This finding indicates that the impact of stock market developments on oil markets is growing over time.     

Buyout candidates and their investment banking relationships: An empirical analysis of returns adjoining buyout offers

Previous studies document a significant positive price reaction at the proposal of a buyout offer. In this study the author examines the influence of investment banking relationships on target firms associated with buyout offers. The results support the hypothesis that knowledge of an impending buyout manifests itself before a buyout’s public announcement. The evidence supports the hypothesis that investment bankers use different strategies before a buyout.     

Volatility estimation for stock index options: A GARCH approach

The objective of this paper is to compare the mispricing of option valuation models when alternate techniques are applied to the volatility estimation. Akgiray (1989) shows that out-of-sample forecasts of return variances of stock indices based on a GARCH model are superior predictors of the actual ex-post variances in comparison to forecasts generated using standard rolling regression methods. A second objective of this study is to examine if Akgiray's results carry over to option valuation. Although we find that the implied volatility technique results in the least mispricing, within the class of forecasts using only historic returns data, the use of GARCH models will also significantly reduce model mispricing.     

Long memory and structural breaks in modeling the return and volatility dynamics of precious metals

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.     

An alternative approach to estimate the wage returns to private‐sector training

This paper follows an alternative approach to identify the wage effects of private‐sector training. The idea is to narrow down the comparison group by only taking into consideration the workers who wanted to participate in training but did not do so because of some random event. This makes the comparison group increasingly similar to the group of participants in terms of observed individual characteristics and the characteristics of (planned) training events. At the same time, the point estimate of the average return to training consistently drops from a large and significant return to a point estimate close to zero. Copyright © 2008 John Wiley & Sons, Ltd.     

Dynamic risk spillovers and portfolio risk management between precious metals and global foreign exchange markets

This study examines portfolio management and risk spillovers between four major precious metals (gold, silver, palladium and platinum) and 20 important U.S. exchange markets. To this end, we employ the multivariate DECO-GARCH model and the spillover index developed by Diebold and Yilmaz (2014, 2016) to examine the spillovers between those metal prices and the exchange rates and design portfolios and hedging strategies using different risk measures. The results show evidence of weak average conditional equicorrelations among the considered markets over time, excluding the turbulent 2008–2010 period. Furthermore, the precious metals (excluding platinum) and the currencies (with the exception of the Australian, Brazilian, Denmark, Euro, Mexican, Norwegian, New Zealand and Swedish currencies) are net receivers of shocks. Finally, the four precious metals provide strong risk and downside risk reductions, underscoring the usefulness of including precious metals in a traditional foreign exchange-dominated portfolio.     

Core and competitive equilibria: An approach from discrete convex analysis

We extend the assignment market (Shapley and Shubik, 1972; Kaneko, 1976, 1982) by utilizing discrete convex analysis. We consider the market in which buyers and sellers trade indivisible commodities for money. Each buyer demands at most one unit of commodity. Each seller produces multiple units of several types of commodities. We make the quasi-linearity assumption on the sellers, but not on the buyers. We assume that the cost function of each seller is M^♮-convex, which is a concept in discrete convex analysis. We prove that the core and the competitive equilibria exist and coincide in our market model.     

An international study of causality-in-variance: Interest rate and financial sector returns

We demonstrate that causality-in-variance test could be employed to model the direction and lags in information flow between two variables and to avoid misspecifications. We apply this methodology to test the causality between the financial sector returns and interest rates of the G7 countries and show that the direction and the lead/lag structure of causality in the mean and the variance are more complex and dynamic than that have previously been reported. In most cases, we found two-way information flow both at the mean and the volatility level. Causality results give us insights into (i) how and when information is impacted on different market segments, and (ii) design more objective bi-variate models with the appropriate lag structure.     

Spectral analysis of public utility returns

This study tests the practicality of using the capital asset pricing model to evaluate public utility returns. To this end, utilities are grouped into portfolios, in accordance with their industry classification and their prime regulatory authority. Then their relationship to a market index of unregulated securities is analyzed vis-à-vis the relationship between this index and several control securities believed, a priori, to be of commensurate risk. These relationships are analyzed in two steps: the first step investigates the real trend movements over time; the second, the association of the wide-sense stationary components of these return processes. Bivariate spectral analysis is used in the second phase of this investigation. This enables us to analyze the time lags between return processes, and to break up our estimates by duration of cyclical movement and compare the long-term and short-term estimates.We conclude that ordinary least squares estimates of utility β's lack value to either investors or regulators. This is due to the highly unstable relationship between utility returns and the market index and to the existence of a lag between these processes. One explanation for this finding is regulatory lag and the uncertainty (as well as the actual content) surrounding regulatory authority decisions.     

Seasonality in firm-size portfolio returns: A nonparametric analysis

In this study the author uses stochastic dominance, a nonparametric method of portfolio performance analysis, to test for seasonality in firm-size portfolio return behavior. Stochastic dominance confirms the January effect, found in previous parametric studies, only for the smallest firm-size portfolio. It statistically eliminates the size effect for the larger firm-size portfolios in January and for all firm-size portfolios in the other months of the year. It is demonstrated that a market proxy problem and normality assumption violation may bias the parametric results. Nonparametric analysis, therefore, suggests that markets may be more efficient than parametric methods imply when model violations exist.