Showing papers on "Price variance published in 2019"

Prices and Promotions in U.S. Retail Markets: Evidence from Big Data

Abstract: We document the degree of price dispersion and the similarities as well as differences in pricing and promotion strategies across stores in the US retail (grocery) industry Our analysis is based on “big data” that allow us to draw general conclusions based on the prices for close to 50,000 products (UPC’s) in 17,184 stores that belong to 81 different retail chains Both at the national and local market level we find a substantial degree of price dispersion for UPC’s and brands at a given moment in time We document that both persistent base price differences across stores and price promotions contribute to the overall price variance, and we provide a decomposition of the price variance into base price and promotion components There is substantial heterogeneity in the degree of price dispersion across products Some of this heterogeneity can be explained by the degree of product penetration (adoption by households) and the number of retail chains that carry a product at the market level Prices and promotions are more homogenous at the retail chain than at the market level In particular, within local markets, prices and promotions are substantially more similar within stores that belong to the same chain than across stores that belong to different chains Furthermore, the incidence of price promotions is strongly coordinated within retail chains, both at the local market level and nationally We present evidence, based on store-level demand estimates for 2,000 brands, that price elasticities and promotion effects at the local market level are substantially more similar within stores that belong to the same chain than across stores belonging to different retailers Moreover, we find that retailers can not easily distinguish, in a statistical sense, among the price elasticities and promotion effects across stores using retailer-level data Hence, the limited level of price discrimination across stores by retail chains likely reflects demand similarity and the inability to distinguish demand across the stores in a local market Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at wwwnberorg

Microstructure Noise and Realized Variance in the Live Cattle Futures Market

Abstract: Recently, U.S. live cattle futures prices have experienced high levels of intraday price variance, which have raised concerns about the possible impact of microstructure noise from high frequency trading on market instability. This article identifies both the magnitude and the duration of the bias caused by market microstructure noise in measuring efficient price variance in the live cattle futures market from 2011 to 2016, with emphasis on price variance behavior in recent years. Market microstructure noise increases observed price variance, but its effects are not large and do not last more than three to four minutes in response to changing information. Intraday price variance has increased in recent years, but the findings provide little evidence that high frequency traders were responsible for economically meaningful market noise. Informatively, steps taken by the CME and cattle producers to mitigate noise have not been fruitful to date, and signal that the magnitude of noise will likely vary with the magnitude of changes in demand and cyclical supply.

Prices and Promotions in U.S. Retail Markets

Abstract: We provide generalizable results on the price and promotion tactics employed in the U.S. retail grocery industry. First, we document a large degree of price dispersion for UPCs and brands across stores, both nationally and at the local market level. Base price differences across stores and price promotions contribute to the overall price variance, and we show how to decompose the price variance into base price and promotion components. Second, we document that a large percentage of the variation in prices and promotion tactics across stores can be explained by retail chain and especially market/chain factors, whereas market factors explain only smaller percentage of the variation. Third, we show that the chain-level price and promotions similarity can be explained by similarity in demand. In particular, a large percentage of the variance in price elasticities and promotion effects can be explained by retail chain and especially market/retail chain factors. Further, price elasticities and promotion effects across stores of the same chain are hard to distinguish from the chain-market-level mean, and cross-price elasticities are typically imprecisely estimated. These findings suggest that retail managers may plausibly consider price discrimination across stores to be infeasible.

Intermittent Price Changes in Production Plants: Empirical Evidence Using Monthly Data

Abstract: The price‐setting behaviour of manufacturing plants is examined using a large panel of monthly surveyed plant‐ and product‐specific prices. The sample shows a high frequency of zero changes, relatively small price changes and a strong seasonal price‐change pattern. The intermittent feature of price changes is modelled with thresholds which are smaller in January, and a quadratic loss function associated with the distance from the target price. The findings show statistically significant pricing thresholds, which are only two‐thirds in January, and partial adjustment parameters implying that 60% of the deviation between the target price and the current price is closed each month.

Option-Implied variance asymmetry and the cross-section of stock returns

Abstract: We find a positive relationship between individual stocks’ implied variance asymmetry, defined as the difference between upside and downside risk-neutral semivariances extracted from out-of-money options, and future stock returns. The high-minus-low hedge portfolio earns the excess return of 0.90% (0.67%) per month in equal-weighted (value-weighted) returns. We show that implied variance asymmetry provides a neat measure of risk-neutral skewness and outperforms the standard risk-neutral skewness in predicting the cross-section of future stock returns. Risk-based equilibrium asset pricing models can not explain such a positive relationship, which instead can be potentially explained by information asymmetry and informed trading.

Clearing price distributions in call auctions

Abstract: We propose a model for price formation in financial markets based on clearing of a standard call auction with random orders, and verify its validity for prediction of the daily closing price distribution statistically. The model considers random buy and sell orders, placed following demand- and supply-side valuation distributions; an equilibrium equation then leads to a distribution for clearing price and transacted volume. Bid and ask volumes are left as free parameters, permitting possibly heavy-tailed or very skewed order flow conditions. In highly liquid auctions, the clearing price distribution converges to an asymptotically normal central limit, with mean and variance in terms of supply/demand-valuation distributions and order flow imbalance. By means of simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting a distinction between high- and low-volume auction price variance. To verify the validity of the model statistically, we predict a year's worth of daily closing price distributions for 5 constituents of the Eurostoxx 50 index; Kolmogorov-Smirnov statistics and QQ-plots demonstrate with ample statistical significance that the model predicts closing price distributions accurately, and compares favourably with alternative methods of prediction.

Multiagent model of prices dispersion on the retail market of petroleum products

Abstract: In this study a multiagent model of behaviour of the dispersion of retail prices for petroleum products has been developed, depending on changes of external factors, in particular, sharp changes in wholesale prices. Therefore, there is a need for a model that would not only have the potential to test the existence of a price dispersion as a consequence of the specifics of competition in the market of petroleum products and consumer search strategies, but would have the ability to quantify the price variance as a consequence of the behaviour of individual market agents. The basis of the behaviour of market agents of this model is algorithms of price oligopolistic competition from traders and user price search strategies. Calibration models and verification of historical data of the Kyiv region, where they were previously established empirical data on the dispersion of prices showed a fairly good correspondence between the model and the actual data. In particular, the existence of a price pattern has been established at jump-like changes of wholesale prices. The presence of price strategy of buyers, which are based on the strategy of the base price, is shown. The coincidence of model and real data still needs to be improved.

Prices and Promotions in U.S. Retail Markets: Evidence from Big Data

Abstract: We document the degree of price dispersion and the similarities as well as differences in pricing and promotion strategies across stores in the U.S. retail (grocery) industry. Our analysis is based on “big data” that allow us to draw general conclusions based on the prices for close to 50,000 products (UPC’s) in 17,184 stores that belong to 81 different retail chains. Both at the national and local market level we find a substantial degree of price dispersion for UPC’s and brands at a given moment in time. We document that both persistent base price differences across stores and price promotions contribute to the overall price variance, and we provide a decomposition of the price variance into base price and promotion components. There is substantial heterogeneity in the degree of price dispersion across products. Some of this heterogeneity can be explained by the degree of product penetration (adoption by households) and the number of retail chains that carry a product at the market level. Prices and promotions are more homogenous at the retail chain than at the market level. In particular, within local markets, prices and promotions are substantially more similar within stores that belong to the same chain than across stores that belong to different chains. Furthermore, the incidence of price promotions is strongly coordinated within retail chains, both at the local market level and nationally. We present evidence, based on store-level demand estimates for 2,000 brands, that price elasticities and promotion effects at the local market level are substantially more similar within stores that belong to the same chain than across stores belonging to different retailers. Moreover, we find that retailers can not easily distinguish, in a statistical sense, among the price elasticities and promotion effects across stores using retailer-level data. Hence, the limited level of price discrimination across stores by retail chains likely reflects demand similarity and the inability to distinguish demand across the stores in a local market.

Derivation of non-classical stochastic price dynamics equations

Abstract: We analyze the relative price change of assets starting from basic supply/demand considerations. The resulting stochastic differential equation has coefficients that are functions of supply and demand. The variance in the relative price change is then dependent on the supply and demand, and is closely connected to the expected return. An important consequence for risk assessment and options pricing is the implication that variance is highest when the magnitude of price change is greatest, and lowest near market extrema. This differs from the standard equation in mathematical finance in which the expected return and variance are decoupled. The methodology has implications for the basic framework for risk assessment, suggesting that volatility should be measured in the context of regimes of price change. The model we propose shows how investors are often misled by the apparent calm of markets near a market peak. Risk assessment methods utilizing volatility can be improved using this formulation.