We use microdata underlying U.S. consumer, producer and import price indices to document how the distribution of price changes evolves over time. Two striking features characterize pricing across all ...three datasets: (1) Frequency of price adjustments is countercyclical. (2) Frequency of price adjustments is correlated with variance. Conversely, other statistics that have received recent attention, like kurtosis, do not exhibit uniform patterns across our data sets. What implications do our empirical results have for monetary policy? Using a flexible accounting framework that collapses the high-dimensional distribution of price changes into a single measure of aggregate price flexibility, we show that flexibility is highly variable and countercyclical.
This paper studies a monopolist firm selling a fixed capacity. The firm sets a price before demand uncertainty is resolved. Speculators may enter the market purely with the intention of resale, which ...can be profitable if demand turns out to be high. Consumers may strategically choose when to purchase, and they may also choose to purchase from the firm or from the speculators. We characterize equilibrium prices and profits and analyze the long-run capacity decisions of the firm. There are three major findings. First, the presence of speculators increases the firm's expected profits even though the resale market competes with the firm. Second, by facilitating resale, the firm can mimic dynamic pricing outcomes and enjoy the associated benefits while charging a fixed price. Third, speculative behavior may generate incentives for the seller to artificially restrict supply, and thus may lead to lower capacity investments. We also explore several model extensions that highlight the robustness of our results.
This study investigates the volatility dynamics of oil and gas prices in an environment characterized by post-coronavirus disease 2019 recovery, uncertainty, high inflation, and geopolitical ...tensions. Unlike previous studies, we examine a long-run series of high-frequency data on gas and oil prices from July 2007 to May 2022, which provides more than one million observations with which to analyze volatility. We compute realized volatility (RV) and decompose it into continuous volatility and jumps. We then investigate the relationship between uncertainty, investor sentiment, and RV, as well as its main components. Econometrically, we extend the heterogeneous autoregressive model of Corsi (2009) while considering not only disaggregate proxies for volatility (jumps and continuous volatility) and introducing uncertainty and heterogeneous investor sentiment, but also by allowing the model to include asymmetry, nonlinearity, and time variation according to the regime under consideration. Our results present three main findings. First, we find significant evidence of volatility decomposition, suggesting that both markets are characterized by significant jumps. Second, we show that trading volume, extra-financial news (uncertainty, investor sentiment), and jumps appear to drive commodity price volatility. Third, we find evidence of nonlinearity and threshold effects on energy price volatility. These findings are relevant for policymakers, regulators, investors, and portfolio managers, as they enable them to better characterize and forecast changes in commodity prices.
•We investigate the volatility dynamics of oil and gas under high uncertainty.•We identify the main fundamental and psychological volatility drivers.•The effects of each driver are time-varying and regime-dependent.•Fundamentals and public news drive volatility in the low-volatility regime.•Behavioral factors dominate energy price volatility in the high-volatility regime.
The food regime concept is a key to unlock not only structured moments and transitions in the history of capitalist food relations, but also the history of capitalism itself. It is not about food per ...se, but about the relations within which food is produced, and through which capitalism is produced and reproduced. It provides, then, a fruitful perspective on the so-called ‘world food crisis' of 2007-2008. This paper argues that the crisis stems from a long-term cycle of fossil-fuel dependence of industrial capitalism, combined with the inflation-producing effects of current biofuel offsets and financial speculation, and the concentration and centralization of agribusiness capital stemming from the enabling conjunctural policies of the corporate food regime. Rising costs, related to peak oil and fuel crop substitutes, combine with monopoly pricing by agribusiness to inflate food prices, globally transmitted under the liberalized terms of finance and trade associated with neoliberal policies.
This paper considers the effects of monopoly third-degree price discrimination on aggregate consumer surplus. Discrimination is likely to reduce surplus (relative to that obtained with a uniform ...price), but surplus can rise under reasonable conditions. If the ratio of the pass-through coefficient to the price elasticity at the uniform price is higher in the market with the higher price elasticity then surplus is larger with discrimination (for a large set of demand functions). The relatively high pass-through coefficient implies a large price reduction in this market. With logit demand functions surplus is higher with discrimination if pass-through is above 0.5.
The impact of extreme events on crude oil markets is of great importance in crude oil price analysis due to the fact that those events generally exert strong impact on crude oil markets. For better ...estimation of the impact of events on crude oil price volatility, this study attempts to use an EMD-based event analysis approach for this task. In the proposed method, the time series to be analyzed is first decomposed into several intrinsic modes with different time scales from fine-to-coarse and an average trend. The decomposed modes respectively capture the fluctuations caused by the extreme event or other factors during the analyzed period. It is found that the total impact of an extreme event is included in only one or several dominant modes, but the secondary modes provide valuable information on subsequent factors. For overlapping events with influences lasting for different periods, their impacts are separated and located in different modes. For illustration and verification purposes, two extreme events, the Persian Gulf War in 1991 and the Iraq War in 2003, are analyzed step by step. The empirical results reveal that the EMD-based event analysis method provides a feasible solution to estimating the impact of extreme events on crude oil prices variation.
Pre-market Trading and IPO Pricing Chang, Chun; Chiang, Yao-Min; Qian, Yiming ...
The Review of financial studies,
03/2017, Volume:
30, Issue:
3
Journal Article
Peer reviewed
Studying the only mandatory pre-IPO market in the world—Taiwan's Emerging Stock Market (ESM)—we document that pre-market prices are very informative about post-market prices and that informativeness ...increases with a stock's liquidity. The ESM price-earnings ratio shortly before an initial public offering explains about 90% of the variation in the offer price-earnings ratio. However, the average IPO underpricing level remains high, at 55%, suggesting that agency problems between underwriters and issuers can lead to excessive underpricing, even with little valuation uncertainty. Also, regulations impact the relative bargaining power of players and therefore IPO pricing.
With the exception of energy, all the key commodity price indices declined significantly in 2013. Fertilizer prices led the decline, down 17.4 percent from 2012, followed by precious metals (down ...almost 17 percent), agriculture (-7.2 percent), and metals (-5.5 percent). Crude oil prices (World Bank average), which have been remarkably stable during the past three years, averaged $104/barrel (bbl) during 2013, marginally lower than the $105/bbl average of 2012. Most non-energy commodity prices, notably grains, followed a downward path during 2013. Other risks for agricultural markets are mostly on the downside as well. For example, the risk of trade policies impacting agricultural prices is low as evidenced by the absence of any export restrictions during 2011-13, despite several spikes in prices (notably maize and wheat). Finally, production of biofuels experienced a third year of little (or no) growth, as policy makers increasingly realize that the environmental and energy independence benefits from biofuels may not outweigh the costs.
This paper aims at comparing the performance of the different state-of-the-art machine learning techniques in anticipating the performance of stock prices of the energy sector. The data collected ...cover the period from January 2020 to February 2023 with a daily frequency for the three most imported refined petroleum products in Morocco and trained four regression machines learning (linear regression, lasso regression, ridge regression, and SVR) and four classifiers machine learning (logistic regression, decision tree, extra tree and Random Forest) so that anticipating one day ahead prices direction can take place no matter whether they are negative or positive prices. The performance of regression algorithm is then evaluated using different evaluation metrics, especially MSE, RMSE, MAE, MAPE and R2 to evaluate the performance of regression algorithm while precision, recall and F1 scores are used to evaluate the performance of classifiers algorithm. The outcomes propose that the performance of linear regression and ridge regression takes place equally and outperform other single regression that is lasso regression and SVR for-one-day predictions as a whole. In addition to that, we have come to find that in the classifiers, algorithms group all machine learning display similar predictive accuracy, this is on one hand. On the other hand, the best of them is the logistic regression. In brief, this study suggests that all performance metrics are significantly improved by ensemble learning. Therefore, this study proves that critical information affecting stock movement can be captured by utilizing historical transactions.
Advances in variance analysis permit the splitting of the total quadratic variation of a jump-diffusion process into upside and downside components. Recent studies establish that this decomposition ...enhances volatility predictions and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions. To appraise the economic gain of this decomposition, we design a new and flexible option pricing model in which the underlying asset price exhibits distinct upside and downside semivariance dynamics driven by the model-free proxies of the variances. The new model outperforms common benchmarks, especially the alternative that splits the quadratic variation into diffusive and jump components.
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