Distribution Of Price Changes Research Articles

Price Setting and Price Stickiness: A Behavioral Foundation of Inaction Bands

Abstract This paper puts forward a behavioral theory of price setting where managers maximize perceived profits following a process of mental accounting. The theory predicts a pricing rule that is similar to—but crucially different from—that of a standard menu cost theory: there is an inaction band, but there are two rather than just one target prices, depending on whether the firm updates its price upwards or downwards. The calibrated model replicates two patterns of price microdata that standard menu cost models have difficulty accounting for: (i) The distribution of price changes has both small and large price changes, and (ii) the hazard function of price changes is downward sloping initially, that is, firms that have just recently changed their price have a higher probability of changing it again, while this probability becomes constant thereafter.

New Facts on Consumer Price Rigidity in the Euro Area

Using CPI microdata for 11 euro area countries over 2010–2019, we document new findings on consumer price rigidity in the euro area: (i) the average frequency of price changes is 12 percent; (ii) the distribution of price changes is highly dispersed, with frequent large and small changes; (iii) price changes are more frequent in January; and (iv) the overall size of price changes rises with inflation, but their frequency does not; these changes in the size are driven by movements in the fraction of price changes that are increases, not by the absolute size of price increases or decreases. (JEL E31, E52, F33, L11)

Empirical Investigation of a Sufficient Statistic for Monetary Shocks

Abstract In a broad class of sticky-price models the non-neutrality of nominal shocks is captured by a simple sufficient statistic: the ratio of the kurtosis of the price change distribution over the frequency of price changes. We test the sufficient statistic proposition using data for a large sample of products representative of the French economy. We first extend the theory to allow for empirically relevant monetary shocks with a transitory predictable component. We then use the micro data to measure kurtosis and frequency for about 120 PPI industries and 220 CPI categories. We use a Factor Augmented VAR to measure the industries response to monetary shocks, under alternative identification schemes. The estimated degree of non-neutrality correlates with the kurtosis and the frequency consistently with the predictions of the theory. Several robustness checks are discussed.

Do firm expectations respond to monetary policy announcements?

This paper investigates whether UK firms’ price growth expectations respond to the Bank of England (BoE) monetary policy announcements and explores the underlying mechanism. Using microdata from the UK Decision Maker Panel survey, we isolate the exogenous component of the monetary policy decisions by comparing firms’ responses filed before and after BoE announcements. Guided by a model of dispersed information, our analysis suggests that firms respond to monetary policy announcements but are, overall, not as informed and sophisticated as financial market participants. Firms’ price expectations respond to actual interest rate changes, as well as to bank rate changes purged from their systematic component, but not to high-frequency surprises. The left tail of their expected price change distribution is particularly sensitive to monetary policy announcements. Furthermore, we unveil significant non-linear effects, with changes in interest rates of 50 basis points being mostly responsible for revisions in expectations. This implies that the recent tightening cycle was effective in shifting firms’ expectations primarily at its peak when a sequence of consecutive large rate hikes was implemented. We also show that UK news coverage of the BoE’s activities increases following policy rate changes, highlighting the media’s crucial role in shaping public expectations.

Implementing the Variance-Covariance Method for Assessing Market Transaction Risks in Raw Material Sector Stocks

The capital market plays a crucial role in supporting a country's economic growth. Besides being a funding source, the capital market also serves as an investment avenue for investors, particularly through stocks. Every investor must be willing to bear risks in line with their targeted returns. Risk is defined as the uncertainty of future outcomes due to market condition changes, and VaR (Value at Risk) is used to determine the tolerated loss at a certain confidence level. This study discusses the application of the Value at Risk (VaR) method using the Variance-Covariance approach to mitigate market risks in the portfolio of raw material sector stocks. The study focuses on two raw material sector stocks in Indonesia, assuming a normal distribution of asset price changes. The measurement results indicate that with an investment of Rp. 100,000,000.00, a 95% confidence level, and a 1-day period, the VaR of the portfolio of these five stocks is Rp. 2,769,750.00. This research provides critical insights to assist investors in understanding and managing portfolio risks, making VaR a key indicator to measure potential future risks and laying the foundation for decision-making in risk management.

An information theory approach to stock market liquidity.

A novel methodology is introduced to dynamically analyze the complex scaling behavior of financial data across various investment horizons. This approach comprises two steps: (a) the application of a distribution-based method for the estimation of time-varying self-similarity matrices. These matrices consist of entries that represent the scaling parameters relating pairs of distributions of price changes constructed for different temporal scales (or investment horizons); (b) the utilization of information theory, specifically the Normalized Compression Distance, to quantify the relative complexity and ascertain the similarities between pairs of self-similarity matrices. Through this methodology, distinct patterns can be identified and they may delineate the levels and the composition of market liquidity. An application to the U.S. stock index S&P500 shows the effectiveness of the proposed methodology.

Quantum Financial Modeling on Noisy Intermediate-Scale Quantum Hardware: Random Walks Using Approximate Quantum Counting

Quantum computers are expected to contribute more efficient and accurate ways of modeling economic processes. Quantum hardware is currently available at a relatively small scale, but effective algorithms are limited by the number of logic gates that can be used, before noise from gate inaccuracies tends to dominate results. Some theoretical algorithms that have been proposed and studied for years do not perform well yet on quantum hardware in practice. This encourages the development of suitable alternative algorithms that play similar roles in limited contexts. This paper implements this strategy in the case of quantum counting, which is used as a component for keeping track of position in a quantum walk, which is used as a model for simulating asset prices over time. We introduce quantum approximate counting circuits that use far fewer 2-qubit entangling gates than traditional quantum counting that relies on binary positional encoding. The robustness of these circuits to noise is demonstrated. While this paper is mainly about robust simplified quantum circuit designs, we compare some aspects of the results with price change distributions from stock indices, and compare the behavior of circuits with and without mid-measurement to trends in the housing market.

On the dynamic effects of the cross‐section distribution of sectoral price changes in China

AbstractThis paper investigates the dynamic interactions of the cross‐section distribution of sectoral price changes and the output growth in the Chinese economy. We compare in depth the results of Granger causality tests, Impulse Response, and Forecast Error Variance Decompositions from Mixed Sampling Frequency Vector Autoregression (MFVAR) with those from common frequency vector autoregression (VAR). It shows that potential causalities for inflation, relative price variability, relative price skewness, and output growth can be successfully detected by the MFVAR. The cross‐section distribution of sectoral price changes stands to be a fundamental determinant of fluctuations in the aggregate economy, not only in the short run but also in the long run. Moreover, the empirical results are robust to the identification restrictions imposed as well as to alternative measures for model variables. Our findings are in line with the predictions of a standard sticky‐price model, and thus pricing frictions are important factors behind the short‐run nonneutrality of nominal shocks. We highlight the primacy of the information contained in the higher‐order moments of cross‐section distribution of sectoral price changes. We propose that policy authorities should make proper use of all of the valuable information available, particularly those embodied in the distribution of sectoral prices.

The real effects of monetary shocks: Evidence from micro pricing moments

Empirically, what pricing moments are informative about monetary non-neutrality? The frequency of price changes is robustly informative among a set of pricing moments and across specifications: A lower frequency is statistically significantly associated with higher monetary non-neutrality, in line with models of price rigidities. Other moments that describe the price change distribution are not consistently or significantly related to monetary non-neutrality. While the frequency explains the largest share of variation in non-neutrality, no pricing moments individually or jointly explain a majority of the variation in a linear empirical setting. Non-pricing moments explain additional variation, however are not consistently associated with monetary non-neutrality. A multi-sector menu cost model featuring different price adjustment technologies across sectors can rationalize our main findings.

Propagation of shocks in an input-output economy: Evidence from disaggregated prices

Using disaggregated industry-level data, this paper empirically evaluates predictions for the cross-sectional price change distribution made by input-output models with sticky prices. The response of prices to shocks is found to be consistent with the price sensitivities predicted by the input-output model. Moreover, moments of the sectoral price change distribution vary over time in response to the evolution of the network structure. Finally, through a quantitative analysis, demand and supply shocks are disentangled during the pandemic period. Counterfactual analyses show that sectoral supply shocks, aggregate demand shocks and the production network structure contributed significantly to the inflation surge in 2021–2022.

Investor sentiment and bitcoin prices

Using a rich data set of transaction-level buy and sell orders from the major digital currency exchange Coinbase, we formulate a measure for investor sentiment and shed new evidence on the sentiment-return relation for bitcoin. Using a bootstrapped quantile regression procedure we show a significant and robust relation between rising sentiment and price increases, and vice versa, across the distribution of bitcoin price changes. This relation is shown to be robust when controlling for a variety of exchange-specific and blockchain-wide variables. This relation is also robust when controlling for aggregate momentum across major cryptocurrencies. This finding is important as our data sample spans a period before and after the introduction of futures markets for bitcoin, which has arguably resulted in a regime shift in the time series behavior of its price. Taken together, our results show that bitcoin prices can undergo regime changes and that conventional regression-type models that focus on the center of the distribution of bitcoin price changes can yield misleading estimates.

개별 주택가격 데이터를 이용한 주택가격 변동률 분포의 특성 분석*

This study departs from the convention of using only the house price index when analyzing the housing market price change, and examines the market through the lens of house price change distribution. By using a panel data of individual house prices, I find the following. First, house price change distribution has a positive skewness; so, the median is smaller than the mean. Second, when the price index falls, the individual price also falls; however, when the price index rises, the deviation between individual price change increases. Third, even when the price index rises by 10%~20%, 5.2% of the individual houses experience a price fall. Last, the large, high price houses in the capital area shows high price volatility; so, when the price index rises, their price rises more than the average houses, and when the price index falls, their price falls more than the average houses. The results of this study can be utilized in risk management in financial institutions.

Adjusting Median and Trimmed-Mean Inflation Rates for Bias Based on Skewness

Median and trimmed-mean inflation rates tend to be useful estimates of trend inflation over long periods, but they can exhibit persistent departures from the underlying trend over shorter horizons. In this Commentary, we document that the extent of this bias is related to the degree of skewness in the distribution of price changes. The shift in the skewness of the cross-sectional price-change distribution during the pandemic means that median PCE and trimmed-mean PCE inflation rates have recently been understating the trend in PCE inflation by about 15 and 35 basis points, respectively.

Spatial Analysis and Modeling of the Housing Value Changes in the U.S. during the COVID-19 Pandemic

COVID-19 has affected almost all sectors of the economy, including the real estate markets across different countries in the world. A rich body of literature has emerged in analyzing real estate market trends and revealing important information. However, few studies have used a spatial perspective to investigate the impact of COVID-19 on property values. The main purposes of this study are as follows: (1) to explore the spatial distribution and spatial patterns of housing price changes during the COVID-19 pandemic crisis in the U.S. real estate market and (2) to model the spatially nonstationary relationships between the housing price change and COVID-19 characteristics. We find that housing price changes differ across space and appear associated with the spatial distribution of the COVID-19 case rates. The housing market volatility is amplified by the uneven distribution of some socioeconomic factors. The spatially uneven housing price changes may bring an uneven spillover effect to the rest of the economy and lead to divergence in economic growth across different areas.

Phillips Correlation and Price‐Change Distributions under Declining Trend Inflation

AbstractThe relationship between inflation dynamics and micro‐level pricing behavior is an underexplored research area. In this article, we first document that deflationary periods in Japan were associated with (i) variations in the distribution of price changes at the micro level, and (ii) a weakening in the Phillips correlation at the macro level. We construct a multisector general equilibrium model with sector‐specific menu costs, a feature that is supported by our estimation using sectoral data, and show that declining trend inflation and an increasing share of services in private consumption together can quantitatively explain the above two observations.

Consumer behavior and food prices during the COVID‐19 pandemic: Evidence from Chinese cities

AbstractWe find that Chinese consumers responded strongly to government restrictions during the COVID‐19 crisis. Our event‐study framework shows that emergency declarations raised average food prices by as much as 7.8 standard deviations of the price change distribution, with a much larger effect on non‐perishable vegetable prices (e.g., 17.0 standard deviations for Chinese cabbage prices). The effects of lockdowns were smaller but longer‐lasting. These results suggest that consumers panic bought non‐perishables under emergency declarations while under lockdowns there was a sustained increase in demand for non‐perishables. Such consumer behavior likely caused sizable losses in consumer welfare, especially among poor households.

The Macroeconomics of Sticky Prices with Generalized Hazard Functions

Abstract We give a full analytic characterization of a large class of sticky-price models where the firm’s price-setting behavior is described by a generalized hazard function. Such a function allows for a vast variety of empirical hazards to be fitted. This setup is microfounded by random adjustment costs, as in Caballero and Engel (1999), or by information frictions, as in Woodford (2009). We establish two main results. First, we show how to identify all the primitives of the model, including the distribution of the fundamental adjustment costs and the implied generalized hazard function, using the distribution of price changes. Second, we derive a sufficient statistic for the aggregate effect of a monetary shock: given an arbitrary generalized hazard function, the cumulative impulse response of output to a once-and-for-all monetary shock is proportional to the ratio of the kurtosis of the steady-state distribution of price changes over the frequency of price adjustment. We prove that Calvo’s model yields the upper bound and Golosov and Lucas’s model the lower bound on this measure in the class of random menu cost models.

Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s Gi∗ statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

The impact of the exchange rate regime on the dispersion of the price-change distribution: Evidence from a large panel of countries

This paper studies the impact of the exchange rate regime (ERR) on the dispersion of the cross-section distribution of price changes in a large panel of advanced and developing economies. We show that the ERR has an economically and statistically significant nonlinear impact on the dispersion of the price-change distribution. While intermediate regimes are associated with higher dispersion, floating exchange rates deliver lower dispersion. Fixed ERRs are also associated with lower dispersion; however, the impact is only marginally significant. This finding holds after controlling for different types of economic shocks, the characteristics of the monetary policy regime, as well as structural and institutional variables which impact cross-country variation in the price-change distribution. With few exceptions, the results are robust to controlling for mean inflation, alternative ERR classifications, different country groups, and the use of instruments to address the potential endogeneity of the choice of ERR.

Repricing Avalanches in the Billion-Prices Data

Nirei and Scheinkman (2021) proposed an equilibrium model of price adjustments with menu-costs with a finite number of firms and derived a “reproduction number” for repricing and a limit functional form for the distribution of the number of simultaneously price-adjusting firms. We show that the distribution of price-changes in data from the Billion Prices Project is well fitted by this functional form and exhibits a reproduction number that is close to unity, indicating that complementarity in price-changes plays a major role in repricings. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.