Magnitude Of Price Changes Research Articles

Research on Global Olive Oil Price Forecasting

Nowadays, commodity price forecasting has become a popular topic. The study of olive oil prices can inform pricing strategies and price positioning of olive oil. How to analyze the range and magnitude of price changes, as well as how consumers are likely to react to changes in olive oil prices, are important factors that will influence the future price trend of olive oil. Therefore, it is crucial for companies to set prices correctly. Since the ARIMA model has high forecasting accuracy and fewer parameters, the model has been chosen for most similar forecasts and is also chosen as a forecasting tool in this paper. The forecast results show that international olive oil prices will remain wildly high in the future, but the rate of increase will slow down. Since the data of recent years are heavily affected by COVID-19, the outliers of recent years also need to be dealt with if more accurate forecasts are needed, which is an unresolved issue in this paper. This paper also attempts to make some policy recommendations based on previous studies to moderate the rapid increase in olive oil prices.

Belief-dependent pricing decisions

How do inflation and idiosyncratic cost expectations affect firms’ price-adjusting decisions? Evidence on micro price adjustment has emphasized that price changes follow a fixed adjustment schedule, i.e., time-dependent pricing, or that they respond to the state of the economy, i.e., state-dependent pricing. We use firm expectation survey data to argue that price-adjustment decisions are also belief-dependent. While controlling for time- and state-dependent factors, we find that for the decision to adjust prices, inflation expectations do not play any role, but firms’ beliefs about their overall costs do. This highlights a robust correlation between idiosyncratic expectations and economic decisions. We show that the expectation channel is heterogeneous across firms and operates with a delay. When studying firms’ expected magnitude of price changes, the relevance of current inflation expectations is recovered apart from costs. Our findings provide empirical support for the belief-driven nature of pricing decisions and their economic implications.

Pandemic pricing: Evidence from German grocery e‐commerce

AbstractThe sudden demand spike for online grocery purchases during the Covid‐19 pandemic and supply bottlenecks caused by disrupted global value chains put immense pressure on prices. We analyze the prices of the largest German online grocers to test how these challenges affected prices during the first wave of the pandemic. Using a large dataset of online price quotes, we shed light on the magnitude of price changes across retailer types, product categories, and stages of the pandemic. We show that online prices went up as the intensity of Covid‐19 containment measures increased. The magnitude of price increases was heterogeneous across retailers and product categories: pure online retailers showed a lower price response compared to hybrid stores, while the prices of essential food items such as baby foods and pantry products increased more than those of other product categories or beverages. [EconLit Citations: E31, Q31].

VOLATILITAS HARGA BAWANG DI JAWA BARAT DENGAN METODE ARCH/GARCH

Food commodities such as shallots and garlic are still a concern for the Indonesian people, this is because prices are not fixed and fluctuate for each period. It is these price fluctuations that cause high price volatility. An illustration of the magnitude of the risks that will be faced by economic actors in the future can be illustrated by the magnitude of price changes that indicate market fluctuations in a period of time. The model that can be used to analyze the nature of price volatility is Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The purpose of this study was to determine the price volatility of shallots and garlic in West Java Province for the period January 2013 to December 2021. The results showed that retail prices for garlic were heteroscedastic, so the model that was more suitable was ARCH/GARCH. As for the commodity of shallots that are homoscedastic, the model that is carried out is only up to the ARIMA model analysis. The volatility model for garlic is the ARCH(1) model. The results of the model estimation show that garlic price volatility is quite high

The Impact of Historical Price Information on Purchase Deferral

In this article, the authors examine how historical price information influences consumers’ decision to defer a purchase. They focus on two aspects of historical price information: the direction and the frequency of past price changes. The authors advance a theoretical framework postulating that the interaction between these two factors shapes consumers’ decisions to buy now versus later. Controlling for the total magnitude of price changes, the authors propose that consumers are more likely to defer purchase when the price of the product has previously increased compared with when the price has decreased. Importantly, the authors hypothesize that this effect is more pronounced when consumers observe a single large change in price (e.g., a decrease of $100 vs. an increase of $100) compared with when they observe multiple smaller changes that establish a trend (e.g., four decreases of $25 vs. four increases of $25). The authors argue that these effects are driven by differences in consumers’ expectations about future prices. They test their predictions, as well as two moderators of the proposed effects—the monotonicity and the timing of price changes—in six well-powered preregistered experimental studies (N = 5,713) using both hypothetical and actual purchases.

Inflation Dynamics in Pakistan

This study explains the correlation among consumer price behavior and inflation in Pakistan by employing weekly micro data of retail price survey from October 2013 till September 2016, which is considered as the period of high inflation. Inflation is a product of extensive margin (the frequency of price change) and intensive margin (magnitude of price change). The study decompose the variance of inflation into contribution from both margins thus to determine if the prices of various groups of commodities follows State dependent pricing model or Time dependent pricing model. The extensive margin is a source of inflation in most of the State dependent pricing model while intensive margin is a basis of inflation in Time dependent pricing model. The analysis reveals a greater correlation of inflation with the intensive margin of price change compared to the extensive margin. Mounting inflation is although related more with greater frequencies of the price increase and lower frequency of price decrease, however, the size of the price increase and decrease also plays an important role. Findings of the study reveals important role of price change behavior in explaining the inflation dynamics and are believed to assist policy makers in assessing the pricing mechanism and inflation dynamics in Pakistan

Heterogeneity and Competition in Fragmented Markets: Fees Vs Speed

ABSTRACT This paper provides an integrated overview of the effects of the implementation of the SEC’s Tick Pilot program on liquidity and competition in U.S. markets, separated into three groups by tick size. We confirm the standard effects of tick size changes on quoted spreads, realized spreads, and depth, as well as the role of the size of the quoted spread prior to the change in tick size. We add that the increase in the tick size leads to a significant reduction in the frequency and magnitude of price changes, primarily driven by a reduction in the frequency of aggressive limit orders. The major effect of the tick size is to alter competition by driving trading volume to inverted fee and off-exchange venues. We find that traders prefer a larger price improvement rather than lower latency for the smallest tick stocks while the reverse is true for largest tick stocks. Overall, the effect of the tick change has an insignificant effect on volume except for stocks with the smallest tick sizes subject to the trade-at rule, who see a substantial drop in volume.

Derivation of non-classical stochastic price dynamics equations

We analyze the relative price change of assets starting from basic supply/demand considerations subject to arbitrary motivations. The resulting stochastic differential equation has coefficients that are functions of supply and demand. We derive these rigorously. The variance in the relative price change is then also 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 occurs even if supply and demand are not dependent on price trend. The stochastic equation 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.

Stochastic Price Dynamics Equations Via Supply and Demand; Implications for Volatility and Risk

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.

Health-Promoting Food Pricing Policies and Decision-Making in Very Remote Aboriginal and Torres Strait Islander Community Stores in Australia.

Aboriginal and Torres Strait Islander people living in remote communities in Australia experience a disproportionate burden of diet-related chronic disease. This occurs in an environment where the cost of store-purchased food is high and cash incomes are low, factors that affect both food insecurity and health outcomes. Aboriginal and Torres Strait Islander storeowners and the retailers who work with them implement local policies with the aim of improving food affordability and health outcomes. This paper describes health-promoting food pricing policies, their alignment with evidence, and the decision-making processes entailed in their development in community stores across very remote Australia. Semi-structured interviews were conducted with a purposive sample of retailers and health professionals identified through the snowball method, September 2015 to October 2016. Data were complemented through review of documents describing food pricing policies. A content analysis of the types and design of policies was undertaken, while the decision-making process was considered through a deductive, thematic analysis. Fifteen retailers and 32 health professionals providing services to stores participated. Subsidies and subsidy/price increase combinations dominated. Magnitude of price changes ranged from 5% to 25% on fruit, vegetables, bottled water, artificially sweetened and sugar sweetened carbonated beverages, and broadly used ‘healthy/essential’ and ‘unhealthy’ food classifications. Feasibility and sustainability were considered during policy development. Greater consideration of acceptability, importance, effectiveness and unintended consequences of policies guided by evidence were deemed important, as were increased involvement of Aboriginal and Torres Strait Islander storeowners and nutritionists in policy development. A range of locally developed health-promoting food pricing policies exist and partially align with research-evidence. The decision-making processes identified offer an opportunity to incorporate evidence, based on consideration of the local context.

Price Rigidity in Norway in the Nineteenth Century

This paper analyzes nominal price development in Norway from 1830 to 1920 and fills a gap in the literature on nominal price rigidity in Europe during the nineteenth and the beginning of the twentieth centuries. The research question: how did the nominal price rigidity change in Norway during this time period? The focus on Norway is justified because of the availability of historical data and gaps in literature concerning the nominal rigidities.The analysis of some of the digitized data for Oslo, Bergen and Stavanger during the period of 1830–1913 indicate that: a) The flexibility of prices did not change much during the classical Gold Standard in Norway; b) The change in price rigidity mainly came because of the changing magnitude of price changes; c) The decrease in magnitude might have happened before the Gold Standard took effect in Norway.

Comparing impacts of climate change and mitigation on global agriculture by 2050

Systematic model inter-comparison helps to narrow discrepancies in the analysis of the future impact of climate change on agricultural production. This paper presents a set of alternative scenarios by five global climate and agro-economic models. Covering integrated assessment (IMAGE), partial equilibrium (CAPRI, GLOBIOM, MAgPIE) and computable general equilibrium (MAGNET) models ensures a good coverage of biophysical and economic agricultural features. These models are harmonized with respect to basic model drivers, to assess the range of potential impacts of climate change on the agricultural sector by 2050. Moreover, they quantify the economic consequences of stringent global emission mitigation efforts, such as non-CO2 emission taxes and land-based mitigation options, to stabilize global warming at 2 °C by the end of the century under different Shared Socioeconomic Pathways. A key contribution of the paper is a vis-à-vis comparison of climate change impacts relative to the impact of mitigation measures. In addition, our scenario design allows assessing the impact of the residual climate change on the mitigation challenge. From a global perspective, the impact of climate change on agricultural production by mid-century is negative but small. A larger negative effect on agricultural production, most pronounced for ruminant meat production, is observed when emission mitigation measures compliant with a 2 °C target are put in place. Our results indicate that a mitigation strategy that embeds residual climate change effects (RCP2.6) has a negative impact on global agricultural production relative to a no-mitigation strategy with stronger climate impacts (RCP6.0). However, this is partially due to the limited impact of the climate change scenarios by 2050. The magnitude of price changes is different amongst models due to methodological differences. Further research to achieve a better harmonization is needed, especially regarding endogenous food and feed demand, including substitution across individual commodities, and endogenous technological change.

TSFDC: A trading strategy based on forecasting directional change

SummaryDirectional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market's trend under the DC context. We examine the profitability, risk and risk‐adjusted return of TSFDC in the FX market using eight currency pairs. The results suggest that TSFDC outperforms the buy and hold approach and another DC‐based trading strategy.

The responsiveness of fuel demand to gasoline price change in passenger transport: a case study of Saudi Arabia

Empirical estimates of fuel demand changes to price variation are based on historical consumption and prices, and can be applied as a single point estimate to a wide range of price movements. However, if fuel prices are set outside the boundaries of historical changes, policymakers may be concerned as to the validity of the empirically assessed price elasticity. We have developed a transport model to provide a techno-economic estimate of the price elasticity of fuel demand. It incorporates consumers’ choices as a result of several factors, including fuel substitutes, available transport modes, income, value of time and magnitude of price change.1.) Our findings from the application of this transport model to Saudi Arabia show that policymakers can have confidence that the empirical estimates are broadly valid, even for large changes and if prices move outside historical variations.2.) In general, gasoline demand in Saudi Arabia is price inelastic due to the lack of fuel and modal substitutes. However, our approach suggests that the response may become more pronounced when the magnitude of the change increases.3.) The cross-price elasticity of diesel is not constant. Demand for diesel will increase if gasoline price is raised significantly. Motorists may, for example, opt for diesel-based public transport, such as trains or buses, for long distance local trips. The change in jet-fuel use is negligible.

Forecasting ETFs with Machine Learning Algorithms

In this article, the authors apply cutting-edge machine learning algorithms to one of the oldest challenges in finance: predicting returns. For the sake of simplicity, they focus on predicting the direction (either up or down) of several liquid exchange-traded funds (ETFs) and do not attempt to predict the magnitude of price changes. The ETFs serve as asset class proxies. The authors employ approximately five years (from January 2011 to January 2016) of historical, daily data obtained through Yahoo Finance. Using supervised learning classification algorithms, readily available from Python’s Scikit-Learn, they employ three powerful techniques: (1) deep neural networks, (2) random forests, and (3) support vector machines (linear and radial basis function). They document the performance of the three algorithms across four information sets: past returns, past volume, dummies for days/months, and a combination of all three. They use a gain criterion to compare classifiers’ performance. First, they find that these algorithms work well over one- to three-month horizons. Short-horizon predictability, over days, is extremely difficult, and thus the results support the short-term random walk hypothesis. Second, they document the importance of cross-sectional and intertemporal volume as a powerful information set. Third, they show that many features are needed for predictability because each feature makes very small contributions toward predictability. The authors conclude that ETF returns can be predicted with machine learning algorithms, but practitioners should incorporate prior knowledge of markets and intuition on asset class behavior. <b>TOPICS:</b>Exchange-traded funds and applications, big data/machine learning, performance measurement

An Empirical Investigation on Spot and Futures Market With Reference to Energy Sector on NSE

The importance of volatility is widespread in the area of financial economics. Equilibrium prices, obtained from asset pricing models, are affected by changes in volatility, investment management lies upon the mean-variance theory, while derivatives valuation hinges upon reliable volatility forecasts. Portfolio managers, risk arbitrageurs, and corporate treasurers closely watch volatility trends, as changes in prices could have a major impact on their investment and risk management decisions. Volatility may be defined as the degree to which asset prices tend to fluctuate. Volatility is the variability or randomness of asset prices. Volatility is often described as the rate and magnitude of changes in prices and in finance often referred to as risk. This study has been carried out on Spot and Futures market with reference to energy sector stocks on NSE. It also analysed the co-integration and Causal relationship between Spot and Futures prices of stocks and index. The study also estimated the impact of Spot market on Futures market. The study has been carried out on volatility of Indian Stock Market represented by S&P CNX Nifty Index along with influence by energy sector stocks.

The Impact of Price Controls in Two-sided Markets: Evidence from US Debit Card Interchange Fee Regulation

We study the pricing of deposit accounts following a regulation that capped debit card interchange fees in the United States and provide the first empirical investigation of the link between interchange fees and granular deposit account prices. This link is broadly predicted by the theoretical literature on two-sided markets, but the nature and magnitude of price changes are key empirical issues. To examine the ways that banks adjusted their account prices in response to the regulatory cap on interchange fees, we exploit the cap's differential applicability across banks and account types, while accounting for equilibrium spillover effects on banks exempt from the cap. Our results show that banks subject to the cap raised checking account prices by decreasing the availability of free accounts, raising monthly fees, and increasing minimum balance requirements, with different adjustment across account types. We also find that banks exempt from the cap adjusted prices as a competitive response to price changes made by regulated banks. Not accounting for such competitive responses underestimates the policy's impact on the market, for both banks subject to the cap and those exempt from it.

Time-Dependent or State-Dependent Pricing? Evidence From a Large Devaluation

State-dependent and time-dependent price setting models yield distinct implications for how frequency and magnitude of price changes react to shocks. This note studies pricing behavior in Brazil following the large devaluation of the Brazilian Real in 1999 to distinguish between models. The results are consistent with state-dependent pricing.

Pricing in the Digital Economy: A Quantitative Study of Just Noticeable Differences in the Services Industry

Price is a key management lever for firm performance (McKinsey 2002) and a key determinant of purchasing decision for a consumer (Bishop 1984, Doyle 1984, Sawyer and Dickson 1984, Schechter 1984). However, customers do not remember the exact price but have a band of prices that are acceptable (Monroe 1973) (Olson 1976) (Monroe 1969). Globally, online sales for both products and services are on the rise and are projected to increase by 10% annually to $370 billion by 2017 (Lomas 2013). This quantitative field study uses three and half years of online non-contractual transactional and customer level data from a B2C company in the service industry. Two phased investigations are conducted. First, we determine the pricing threshold range using price increase and decrease. We then examine the difference in price threshold between service and consumer goods industry. Theoretically, the study furthers the conceptual understanding of the pricing thresholds in the service industry. The use of real customer level data is a methodological contribution of this study. Third, this research confirms that customer purchase frequency does not have a linear relationship to price and that certain magnitude of price change results in higher magnitude customer losses. These findings are key to predicting demand post pricing action and understanding consumer behavior to maximize profitability.

Modelling the Relationships between Duration and Volatility in Asset Prices

This paper proposes a new model that captures the interaction between duration and magnitude of changes in asset prices, and thus provides a convenient framework to test statistically the existence of such relationship. The model is flexible and contains various well known models as special cases, including, the Exponential Generalised Autoregressive Heteroskedasticity (EGARCH) model of Nelson (1991) and the Logarithmic Conditional Duration (Log-ACD) model of Bauwens and Giot (2000). Despite having the EGARCH model as a special case, the objective of the model is not trying to model conditional duration and conditional volatility jointly. As shown in Ghysels and Jasiak (1998), modelling conditional duration and volatility jointly is technically challenging. This is due to the fact that volatility is defined over a regular sampling frequency but duration is defined over irregular time intervals. Given GARCH model is not generally closed under temporal aggregation, this creates a challenging modelling problem. The aim of this paper is to avoid this challenge by not modelling the conditional volatility, but instead, model the dynamics in the magnitudes of price change. The paper argues that since volatility is a function of the magnitudes of price change, testing the relationship between duration and the magnitude of price change provides an indirect test on the relationship between duration and volatility. The paper also obtains theoretical results for the Quasi-Maximum Likelihood Estimator (QMLE) for the proposed model. Specifically, sufficient conditions for consistency and asymptotic normality are derived under mild assumptions. Monte Carlo experiments also provide further support of the theoretical results and demonstrate that the QMLE has reasonably good finite sample performance. The paper then applies the model to nine different assets from three different asset classes, namely two exchange rate, two commodities and five stocks. The two currencies are Australia/US and British Pound/US exchange rates; the two commodities are Gold and Silver and the five stocks are BHP, Rio Tinto, CBS, ANZ and Apple. The sample spans from 4 January 2010 to 30 December 2011 with an average of 100,000 observations.