The implied volatility smirk of commodity options

This paper studies the implied volatility (IV) smirks in four commodity markets by adopting Zhang and Xiang's methodology. First, we document the term structure and dynamics of IV smirks. Overall, the commodity IV curves are negatively skewed with a positive curvature. Then we analyze the commodity and S&P 500 returns' predictability based on in‐sample and out‐of‐sample tests and find that the information embedded in IV smirks can significantly predict monthly commodity and S&P 500 returns. For example, the risk‐neutral fourth cumulant (FC) from the crude oil market outperforms all of the standard predictors in predicting the S&P 500 returns.     

Cross‐commodity analysis and applications to risk management

The understanding of joint asset return distributions is an important ingredient for managing risks of portfolios. Although this is a well‐discussed issue in fixed income and equity markets, it is a challenge for energy commodities. In this study we are concerned with describing the joint return distribution of energy‐related commodities futures, namely power, oil, gas, coal, and carbon. The objective of the study is threefold. First, we conduct a careful analysis of empirical returns and show how the class of multivariate generalized hyperbolic distributions performs in this context. Second, we present how risk measures can be computed for commodity portfolios based on generalized hyperbolic assumptions. And finally, we discuss the implications of our findings for risk management analyzing the exposure of power plants, which represent typical energy portfolios. Our main findings are that risk estimates based on a normal distribution in the context of energy commodities can be statistically improved using generalized hyperbolic distributions. Those distributions are flexible enough to incorporate many characteristics of commodity returns and yield more accurate risk estimates. Our analysis of the market suggests that carbon allowances can be a helpful tool for controlling the risk exposure of a typical energy portfolio representing a power plant. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:197–217, 2009     

Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility

Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time‐series models based on historical daily returns. In this study both the measurement and the forecasting of financial volatility is improved using high‐frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with and even outperform implied volatility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1005–1028, 2004     

Predictive abilities of speculators in energy markets

Using nonparametric methodology, I find that speculators are successful in taking profitable positions in energy futures markets, although the magnitude of this effect is lower than that found previously for agricultural markets. A plausible explanation for this difference is that price forecasting is more difficult for energy commodities. Moreover, I find that the energy speculators’ returns are due to the existence of the risk premiums rather than to speculators’ forecasting abilities. Futures risk premium is highly time-variant; notably, energy investors’ profits have been very limited in the GFC and post-GFC period, which coincided with the financialization of commodity markets.     

Economic uncertainty,trading activity,and commodity futures volatility

This paper investigates the dynamics of commodity futures volatility. I derive the variance decomposition for the futures basis and show unexpected excess returns result from new information about expected future interest rates, convenience yields, and risk premia. Measures of uncertainty in economic conditions have significant predictive power for realized volatility of commodity futures returns, after controlling for lagged volatility, returns, commodity index trading, hedging pressure, and other trading activity, even during the so-called “index financialization” period. During this period, hedge fund performance predicts volatility in grain commodities, which are affected by the US ethanol mandate.     

Long memory models for daily and high frequency commodity futures returns

The volatility of daily futures returns for six important commodities are found to be well described as FIGARCH, fractionally integrated processes, whereas the mean returns exhibit very small departures from the martingale difference property. Several years of high frequency intraday commodity futures returns are also found to have very similar long memory in volatility features as the daily returns. Semiparametric local Whittle estimation of the long memory parameter in absolute returns also finds very significant long memory features. Estimating the long memory parameter across many different data sampling frequencies provides consistent estimates of the long memory parameter, suggesting that the series are self‐similar. The results have important implications for empirical work using commodity futures price data. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:643–668, 2007     

An asymmetric analysis of the J‐curve effect in the commodity trade between China and the US

This paper follows a non‐linear ARDL error‐correction approach to examine the presence of the J‐curve in the commodity‐level trade between the United States and China. The analysis disaggregates the US–China trade flows by commodities and separately examines the trade balance responses of 97 commodities to the changes in the real yuan–US$ exchange rates. The analysis at the commodity level alleviates potential aggregation bias that is present in earlier studies offering little evidence for long‐run asymmetric effects of exchange rate on the China–US trade balance. We find strong support for short‐run asymmetric effects in the case of two‐third of the commodities, whereas significant long‐run asymmetric effects are present in the case of one‐third of the commodities including those commodities which command large shares in the China–US trade.     

The Asymmetric Commodity Inventory Effect on the Optimal Hedge Ratio

Hedging strategies for commodity prices largely rely on dynamic models to compute optimal hedge ratios. This study illustrates the importance of considering the commodity inventory effect (effect by which the commodity price volatility increases more after a positive shock than after a negative shock of the same magnitude) in modeling the variance–covariance dynamics. We show by in‐sample and out‐of‐sample forecasts that a commodity price index portfolio optimized by an asymmetric BEKK–GARCH model outperforms the symmetric BEKK, static (OLS), or naïve models. Robustness checks on a set of commodities and by an alternative mean‐variance optimization framework confirm the relevance of taking into account the inventory effect in commodity hedging strategies.     

Panel quantile regressions for estimating and predicting the value-at-risk of commodities

Using a flexible panel quantile regression framework, we show how the future conditional quantiles of commodities returns depend on both ex post and ex ante uncertainty. Empirical analysis of the most liquid commodities covering main sectors, including energy, food, agriculture, and precious and industrial metals, reveal several important stylized facts. We document common patterns of the dependence between future quantile returns and ex post as well as ex ante volatilities. We further show that the conditional returns distribution is platykurtic. The approach can serve as a useful risk management tool for investors interested in commodity futures contracts.     

Estimating the Cost‐of‐Equity Capital Using Empirical Asset Pricing Models

Empirical asset pricing models seek to capture characteristic‐based patterns in the cross‐section of average stock returns. I propose a new approach for constructing these models, and investigate its performance with respect to estimating the cost‐of‐equity capital. Using a model that accounts for the cross‐sectional relation between five characteristics and average stock returns, I obtain cost‐of‐equity estimates that outperform those produced by the Fama‐French five‐factor model in out‐of‐sample tests. Because the proposed approach builds directly on standard cross‐sectional regression techniques, it provides complete flexibility in choosing the firm characteristics used to formulate the cost‐of‐equity estimates.     

Volatility term structures in commodity markets

In this study, we comprehensively examine the volatility term structures in commodity markets. We model state-dependent spillovers in principal components (PCs) of the volatility term structures of different commodities, as well as that of the equity market. We detect strong economic links and a substantial interconnectedness of the volatility term structures of commodities. Accounting for intra-commodity-market spillovers significantly improves out-of-sample forecasts of the components of the volatility term structure. Spillovers following macroeconomic news announcements account for a large proportion of this forecast power. There thus seems to be substantial information transmission between different commodity markets.     

The information content of implied volatility: Evidence from Australia

This study develops an implied volatility index for the Australian stock market, termed as the AVX, and assesses its information content. The AVX is constructed using S&P/ASX 200 index options with a constant time‐to‐maturity of three months. It is observed that the AVX has a significant negative and asymmetric relationship with S&P/ASX 200 returns. When evaluating the forecasting power of the AVX for future stock market volatility, it is found that the AVX contains important information both in‐sample and out‐of‐sample. In‐sample, the AVX significantly improves the fit of a GJR‐GARCH(1, 1) model. Out‐of‐sample, the AVX significantly outperforms the RiskMetrics approach and the GJR‐GARCH(1, 1) model, with its highest forecasting power at the one‐month forecasting horizon. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:134–155, 2010     

Speculation and volatility—A time-varying approach applied on Chinese commodity futures markets

Experts have long discussed and empirically investigated whether speculative activity increases volatility on commodity futures markets. Little empirical research, however, analyzes the role of speculators on commodity futures markets in China. Using time-varying vector autoregression models with stochastic volatility, this paper investigates for four heavily traded metal and agricultural contracts, how the relationship between returns volatility and speculation evolves over time. Our findings indicate that speculative activity has little to no impact on volatility. On the contrary, for all commodities examined, returns volatility seems to amplify speculation.     

Forecasting exchange rates with commodity prices—a global country analysis

This paper investigates the predictive properties of import and export prices of commodities on the exchange rates. A period from 1993 to 2016 is considered. We find that forecasts of the exchange rate adding commodity export and import prices are superior to those neglecting these variables. This holds irrespective of whether the countries are net exporters or importers of commodities. However, the forecasting power was even better in the 1990s and seems to have decreased since that that time. Nevertheless, forecasts can even today be improved considerably by adding commodity prices.     

Macroeconomic factors and emerging market equity returns: a Bayesian model selection approach

Macroeconomics figures prominently in analyses of emerging markets, both as an asset class and for allocations within emerging markets. However, the literature on the drivers of emerging markets equity returns generally pays little attention to macroeconomic factors. This paper investigates the predictive power of several candidate macroeconomic factors for emerging market equity returns using the Bayesian model selection approach developed in Cremers [Cremers, K.J.M., 2002. Stock return predictability: a Bayesian model selection perspective. The Review of Financial Studies 15, 1223–1249]. The results provide strong evidence against all of the macro factors considered with the exception of exchange rate changes and, consistent with the existing literature, provide strong support for several financial factors, but not beta, as significant predictors of excess returns.     

Can skewness of the futures-spot basis predict currency spot returns?

This paper examines the relationship between skewness of the futures-spot basis and expected currency spot returns. The empirical results show that the expected spot returns are negatively correlated with the basis skewness. We find that the basis skewness exhibits statistically significant in-sample and out-of-sample forecasting power. Furthermore, the basis skewness beat the random walk (without drift) in economic measures. The impacts of the basis skewness on spot returns barely vary with time and have no structural breaks. We also find that the basis skewness can really improve the predictability of spot returns, even when the futures-spot basis is considered.     

Underwriter Reputation in Brazilian IPOs

This study updates previous analyses of banks' activities in equity offerings in the Brazilian market. Using data for the 2004–2007 period, the study tests the hypotheses that banks with the highest reputations coordinate offerings with lower initial returns, lower levels of risk and charge more for their services. Three measures of reputation were constructed in line with measures used for the US market. None of the measures showed a statistically significant relationship with returns, risk, or remuneration. The difference between the mid-point of the preliminary price range and the offering price constituted the most significant element in the forecasting of returns.     

Stock return dynamics,option volume,and the information content of implied volatility

This article reports new empirical results on the information content of implied volatility, with respect to modeling and forecasting the volatility of individual firm returns. The 50 firms with the highest option volume on the Chicago Board Options Exchange between 1988 and 1995 are examined. First, the results indicate that the ability of implied volatility to subsume all relevant information about conditional variance depends on option trading volume. For the most active options in the sample, implied volatility reliably outperforms GARCH and subsumes all information in return shocks beyond the first lag. For these active options, implied volatility performs substantially better than indicated by the prior results of Lamoureux and Lastrapes ( 1993 ), despite significant methodological improvements in the time‐series volatility models in this study including the use of high‐frequency intraday return shocks. For the lower option‐volume firms in the sample, the performance of implied volatility deteriorates relative to time‐series volatility models. Finally, compared to a time‐series approach, the implied volatility of equity index options provides reliable incremental information about future firm‐level volatility. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:615–646, 2003     

Assessing credit quality from the equity market: can a structural approach forecast credit ratings?

We investigate the empirical performance of default probability prediction based on Merton's (1974) structural credit risk model. More specifically, we study if distance‐to‐default is a sufficient statistic for the equity market information concerning the credit quality of the debt‐issuing firm. We show that a simple reduced form model outperforms the Merton (1974) model for both in‐sample fitting and out‐of‐sample predictability for credit ratings, and that both can be greatly improved by including the firm's equity value as an additional variable. Moreover, the empirical performance of this hybrid model is very similar to that of the simple reduced form model. As a result, we conclude that distant‐to‐default alone does not adequately capture the firm's credit quality information from the equity market. Copyright © 2007 ASAC. Published by John Wiley & Sons, Ltd.     

Valuing real options using implied binomial trees and commodity futures options

A real option on a commodity is valued using an implied binomial tree (IBT) calibrated using commodity futures options prices. Estimating an IBT in the absence of spot options (the norm for commodities) allows real option models to be calibrated for the first time to market‐implied probability distributions for commodity prices. In addition, the existence of long‐dated futures options means that good volatility estimates may now be incorporated into capital budgeting evaluations of real options projects with long planning horizons. An example is given using gold futures options and a real option to extract gold from a mine. A detailed out‐of‐sample test is included that shows how IBT option pricing errors evolve on subtrees emanating from future levels of the underlying asset. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:203–226, 2007