Price Variance Research Articles

Monetary shocks and house prices in Europe

PurposeThe aim of this study is to analyse the effect of conventional and unconventional monetary policy shocks on housing price dynamics in Europe (2000–2020). We propose a pan-European comparative analysis at a city market level, contrary to the previous literature.Design/methodology/approachWe build a quarterly market dataset for 13 European cities (Paris, Lyon, Marseille, Berlin, Munich, Frankfurt, Amsterdam, Madrid, Barcelona, Seville, London, Birmingham and Manchester). We proceed in two steps. First, we develop a structural VAR (vector autoregression) model. Second, we conduct a forecast error variance decomposition analysis.FindingsWe show that a contractionary policy rate has a negative influence on house prices with relevant differences. A balance sheet shock displays a heterogeneous effect on housing prices. Globally, we observe that a conventional monetary policy shock explains a larger share of total housing price variance than an unconventional monetary policy shock. Finally, our results report that conventional and unconventional monetary policy shocks have a greater impact in more liberalized credit markets.Originality/valueWe develop a pan-European analysis of house prices at a market level for a sample of 13 European cities. A parsimonious structural VAR model is used to study the dynamics of conventional and unconventional monetary policies on house prices in major European markets: Paris, Lyon, Marseille, Berlin, Munich, Frankfurt, Amsterdam, Madrid, Barcelona, Seville, London, Birmingham and Manchester. Our results highlighting the relative importance of conventional and unconventional monetary shocks, identify the existence of heterogeneous effects of monetary policies in European city markets.

Research on the Influencing Factors of House Prices in Chicago

This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Using detailed regression analysis, several key variables were identified as significant predictors of property value. The study included variables such as the number of bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, number of garages, and the condition of the house. The R-squared value of the model is 0.732, indicating that approximately 73.2% of the house price variance can be explained by the selected variable. Houses with more rooms and more square footage tend to cost more. In addition, characteristics such as the number of garages and bathrooms can also have a significant impact on house prices. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate the suburban housing market and ensure more accurate assessments and strategic planning.

Which factor reduces pharmaceutical expenditure, number of entrants or price variance? Updated generic drug markets in South Korea

BackgroundIntroducing more generics has been a successful strategy for lowering pharmaceutical prices and expenditure. However, the effect of the strategy depends on the pricing schemes for generics. We aimed to update the South Korean generic markets in terms of effective competition, and to examine the effects of number of manufacturers and price variance on pharmaceutical expenditure.MethodsWe constructed balanced panel data provided by the Health Insurance Review and Assessment Service covering 726 reimbursed substances from 2019 to 2023. We developed original indicators to analyze the generic markets: the maximum-minimum price variance (MMPV) and the maximum-weighted price variance (MWPV). Panel regression with fixed and time-fixed effects was used.ResultsOver the study period, the number of manufacturers increased from 17.81 in 2019 to 20.98 in 2020 and then decreased to 18.70 in 2023. The MMPV increased from 204.70 in 2019 to 230.07 in 2022 and then decreased slightly to 225.34 in 2023. The MWPV increased from 59.70 in 2019 to 72.58 in 2023. Two types of segmented markets were noteworthy: low use of low-cost generics with sufficient manufacturers and high use of low-cost generics with insufficient manufacturers. In the fixed and time-fixed effects panel analyses, the MWPV presented a negative association with the number of manufacturers and a positive association with the MMPV.ConclusionsA newly introduced tiered pricing scheme, designed to differentiate generic prices, was associated with a decrease in the number of manufacturers and an increase in price dispersion. The pricing schemes for generics should be designed with price variance in mind and limit the number of too many generics in South Korea.

How do global commodities react to increasing geopolitical risks? New insights into the Russia-Ukraine and Palestine-Israel conflicts

The present study examines the impact of geopolitical risk on global commodities returns by including both major conflicts spanning from January 3, 2023 to December 18, 2023 to cover both the Russia-Ukraine and Palestine-Israel wars. Our study utilizes the QVAR model, the Quantile-on-Quantile regression (QQR) and the frequency causality test. Surprisingly, during the pre-conflict period, our findings reveal a weak positive influence of GPR on commodities, indicating their resilience to extreme negative shocks. Variance decompositions further highlight the limited role of GPR in explaining the commodity price variance. The spillover analysis highlights a low interconnectedness between GPR and commodities, demonstrating a high hedging effectiveness against expected negative GPR shocks. The heterogeneous reaction of commodities to extremely positive GPR shocks suggests diverse responses, emphasizing the need for nuanced investment strategies. Conversely, during the crisis periods, commodities exhibit a resilience to both negative and positive shocks from geopolitical tensions, therefore, presenting diversification opportunities for investors. The causality frequency analysis emphasizes the complex dynamics between GPR and commodities in both the time and frequency domains. Our study also offers significant implications for investors, portfolio managers, and policymakers, advocating for recognition of the weak pre-conflict link between GPR and commodities.

Where is my footprint located? Estimating the geographical variance of hybrid LCA footprints

AbstractCurrent implementations of hybrid life cycle assessment (LCA) mostly do not fully exploit valuable information from multi‐regional input–output databases by aggregating regional supply chains to the lower geographical resolution of process LCA databases. We propose a method for sampling the various individual regions within the aggregated regional scope of LCA processes. This sampling maximizes the information content of hybrid LCA footprint results by preserving the regional variance, and it allows for regional price distributions from BACI/UN‐COMTRADE international trade statistics to be used to simultaneously improve the accuracy of the hybrid model. This work makes the previously hidden regional and price variance explicit and analyzes uncertainty of the hybrid carbon (global warming potential 100, GWP100) and land use footprints arising from these variances, both separately and in combination. We find that the median process footprint intensity increases by for the GWP100 due to hybridization, and for the land use footprint. Results show that the magnitude of the footprint uncertainty strongly depends on the product sector of the LCA process and environmental impact considered. In a case study of Swiss household consumption, we find truncation error estimates of for the GWP100 and for the land use footprint. Our results highlight the importance of regionalization of process LCA databases, as it has the potential to significantly improve both the precision and accuracy of derived hybrid LCA models. This article met the requirements for a gold/gold JIE data openness badge described at http://jie.click/badges.

A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu.

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

Assessing the baseline model of WTI oil and stock returns under financial volatility and spillover effects

Do financial volatility shocks matter for WTI oil and stock markets? They should since world economic activity responds to volatility. To investigate this question, we revisit two empirical frameworks widely used in the literature. Our baseline involves the standard identification of oil shocks in structural vector autoregressions (SVAR) with (world) oil supply, demand, and real oil prices. We then propose a global vector autoregressive (GVAR) model integrating 14 major world economies. We introduce the volatility measure (VIX), various stock markets, and compare two oil demand proxies: real economic activity (REA) based on commodity prices and world industrial production (WIP). The results of the impulse response analysis do not indicate major differences across models. However, when measuring the share of oil price variance explained by shocks to VIX (or to U.S. high-bond corporate yield) SVAR overestimates the influence of VIX. We find (world) demand shocks explain a larger part of the variance of oil and stock markets compared to supply shocks. In SVAR, new information coming from a one standard deviation shock to VIX is incorporated negatively and instantaneously on real oil prices: −1.6 for REA and −1.4 for WIP. In GVAR these are reduced slightly to −1.2 from VIX and to −0.6 if coming from corporate bond yield shocks. Our study highlights the sensitivity of econometric results to distinct approaches: one that identifies shocks and another that captures spillover effects.

Predicting Cryptocurrency Returns Using Classification and Regression Machine Learning Model

People are starting to see the cryptocurrency market as a viable source of income and investment, similar to the stock market, as the concept of cryptocurrencies continues to gain popularity. Predicting Bitcoin returns is related to financial machine learning, which uses time series to forecast price variance. This study starts with the daily close price of Bitcoin for its initial dataset. The price is transformed into percentages and binary classes, which categorize into “Up” and “Down”, after which a time series is applied to produce two datasets: a categorical dataset for classification and a numerical dataset for regression. For classification that represents a Binary classification in asset-price forecasting, k-fold cross-validation is applied to ensure that the best classifiers are selected for testing and analysis. Most of the regression analysis was based on visualization, which displayed the predicted prices by each regressor in front of the original values and helped analyse the models’ results more accurately. The outcomes of this study were achieved by anticipating bitcoin returns using classification and regression machine learning models, despite the approaches’ low accuracy and significant precision rate to the “Up” class. At this stage, with a significant limitation regarding the dataset and a lack of other indicators, a model capable of predicting future variations is considered a beneficial addition for many trading tools or even for crypto market analysts.

Effectiveness of Random Forest Model in Predicting Stock Prices of Solar Energy Companies in India

The solar energy industry's positive impact on India's GDP is perceptible through increased investments, innovation, and enhanced energy security. As the nation continues to prioritize clean energy solutions, the solar sector stands as a key player driving both economic prosperity and environmental sustainability, aligning India with world wide determinations to battle climate change and encourage a greener future. As the Indian government continues to champion initiatives promoting renewable energy, Solar Energy Companies have seen unprecedented growth and have become increasingly attractive to investors seeking long-term, sustainable returns. This influx of interest, however, brings with it the challenge of navigating the volatile and dynamic nature of the stock market. In this context, forecasting the stock prices of solar energy corporations in India becomes a pivotal aspect of investment strategy for both institutional and retail investors. This paper targets to add to the prevailing body of knowledge by evaluating the efficacy of the Random Forest model, a machine learning technique known for its versatility and robustness, in forecasting the stock prices of top four Solar Energy Companies in India on the basis of market capitalization, by using the daily opening, high, low and closing stock prices ranging from 1st October, 2019 to 30th September, 2023 i.e. 4 years. The findings reveal that high Coefficient of Determination (R2) values for all companies, ranging from 0.9928 to 0.9939 is a clear indication of the model's ability to predict a substantial portion of the variance in each company's stock prices. But in case of Adani Green Energy Ltd a notably higher MSE and RMSE are exhibited, implying a greater degree of fluctuation in prediction accuracy compared to the other companies. On the other hand, all the selected solar energy companies display lower MAE values, indicating tightly clustered predictions around actual values.

Public Procurement price variance in Kenya: extent, Drivers, and Proposed Mitigation

Developing economies, often dependent on donor funding and debt, such as Kenya could benefit from sealing budget leakages. Public procurement price variance is deemed a key inefficiency in public expenditure contributing to the loss of colossal amounts of Money. Mechanisms to achieve realistic public procurement prices are stipulated in the existing public procurement regulatory regime; However, Price variances, manifesting in inflated tenders, are abound. The extent and drivers are yet to be empirically examined. This study adopted an exploratory approach to examine the extent of public procurement price variance; by conducting variance analysis of market prices and prices of goods contained in procurement contracts of 336 public procuring entities (Ministries, Departments, and Agencies) in Kenya in the 2022/2023 Financial Year. Analysis of data drawn from a random sample of 40 MDAs, indicates that all items acquired by MDAs were overpriced: at between 20% and 220% of the market price. Analysis of drivers of overpricing ranked: “budgeted kickbacks/bribes”; “Response to Late Payments by MDAs”; “Collusive Bidding”; and “Over-use of non-competitive methods”; as key drivers. The study reviewed the price oversight mechanism of the public procurement system in Kenya and recommends the adoption of a live internal public procurement audit model to monitor and oversight pricing. This will require a review of the regulatory regime to entrench the internal procurement audit model into the public procurement process. The study also recommends a public procurement study to examine the extent and magnitude of procurement price variance across Kenya and its fiscal effect on the Economy.

On the long-run properties of income and stock prices: the stability of the “golden ratios”

PurposeThe challenge of predicting changes in aggregate income and stock prices is one that has occupied the research agendas of economists. This paper aims to use the consumption–income ratio and the dividend–price ratio to predict future income and stock prices.Design/methodology/approachTo examine the stability of the consumption–income ratio and the dividend–price ratio, the authors run a two-variable, two-lag reduced-form VAR in the vein of Cochrane (1994), using a lag of each respective ratio as exogenous to the VAR. Additionally, the authors estimate an AR(4) model for income and prices.FindingsThe consumption–income ratio and the dividend–price ratio remain key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. The dividend–price ratio does not significantly predict future price growth. Consumption and dividend shocks have lasting impacts on income and prices.Originality/valueThe consumption–income ratio and the dividend–price ratio are still key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. However, the dividend–price ratio does not significantly predict future price growth, a change from previous research from the 1990s, despite the increasing complexity of stock markets. Consumption and dividend shocks have lasting impacts on income and prices and appear to be significant drivers in both the short- and long-run variance in income and prices.

The impact of price and image warnings on the social perception of gifting cigarettes in China.

The tobacco gift-giving culture in China poses a significant challenge to public health; however, there is limited research on effectively curbing the tobacco gift-giving culture and its associated tobacco gift consumption. This study examines the potential impact of two tobacco control measures that the Chinese government may consider adopting on cigarette gifting behavior in the future in Chinese society. This study employed a randomized survey experiment to examine the effects of cigarette price treatment and pictorial health warning labels (HWLs) on cigarette gifting. The total sample size of this study is 1035. Four groups of participants were presented with representative cigarettes categorized into high-, medium-, and low-priced products, along with different prices (normal and double) or external packaging imagery (normal and pictorial HWL versions). The price of cigarettes for personal consumption forms an L-shaped distribution, and the price of cigarette gifts forms a W-shaped distribution. Increasing cigarette prices reduces smokers' willingness to gift high-priced cigarettes but stimulates the consumption of low-price cigarettes as gifts. Pictorial HWLs do not directly influence smokers' intentions to gift cigarettes, but they enhance the effectiveness of price regulation concerning medium-priced cigarette products. If the price variance of cigarettes is not reduced, the effect of price regulation will be very limited. Implementing combined interventions of pictorial HWLs and price regulation or modifying the pricing structure of tobacco products may yield stronger control outcomes.

Multivariate time series analysis of COVID-19 Pandemic and gold price by using Error Corrections Model

Coronavirus (COVID-19) severe acute respiratory syndrome is an infectious disease that has a direct influence on the world’s population, as is obvious from the prevalence of COVID-19. However, as the COVID-19 virus continues to proliferate, its impact is becoming more and more significant and extensive. Accordingly, the study’s goal is to use the Error Corrections Model (ECM) to examine the relationships between the COVID-19 pandemic and gold prices. An Error Corrections Model was used by the researchers in this investigation. Error Correction Models and long-term equilibrium can be found for non-stationary time series that have been cointegrated together. Two-step estimation can be used to estimate the ECM since a proportion of the imbalance from one period is adjusted in the succeeding period, the system returns to equilibrium. The calculated cointegrating relations are used to create the error correction terms, and the estimation of VAR is done by a first differencing process, and then appears in the model as an explanatory variable, with the term of error correction. The COVID-19 pandemic and gold price data were made available to the general public in a sample data. For the year 2020, it includes data on daily observations for the relevant variables. R-language results show that the COVID-19 pandemic and gold prices have been demonstrated to have a long-term causal relationship. During the most recent period of variance decomposition, 99.76 percent of the variance in covid-19 explains only 0.24 percent of the variance in gold price. This explains that there is no short-term causal relationship between covid-19 and gold price, as indicated by the Wald test results, the granger causality results have shown that the gold price is influenced not only by itself but also by the Covid-19 pandemic. Finally, there is a positive relationship between the Covid-19 pandemic and the price of gold, and this has a negative impact on human life during 2020.

An Econometric Analysis of Volatility Discovery

We investigate information processing in the stochastic process driving stock’s volatility (volatility discovery). We apply fractionally cointegration techniques to decompose the estimates of the market-specific integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component. The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identification of the integrated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-fill asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identifies a distinct information process than that based on the price discovery analysis.

Empowering Home Buyers: A Data-Ddriven Approach to Real Estate Pricing in Ames, Iowa

The demand for both housing and investment real estate has significantly increased due to rising urbanization and growing household savings. To address the need for operable real estate pricing models, this study explores potential variables affecting the sale prices of properties in Ames, Iowa, from 2006 to 2010. Utilizing data mining techniques and regression analysis, this study develops a model incorporating 12 independent variables that can be easily obtained during property visits. By offering an easily appliable tool, this research enables potential home buyers to estimate property sale prices, even without extensive expertise in Data Science, Investment, and Economics. The findings demonstrate that analyzing 12 variables directly related to the property itself such as interior finish of the garage, foundation material, and remodeling date can explain approximately 85.2% of the variance in sale prices. Empowering consumers with this knowledge can help reduce the information gap in the real estate market and promote informed decision-making in property purchases.

Risk-managed operation of community integrated energy systems in day-ahead and real-time markets based on portfolio theory

Operation strategy is a critical issue for the community integrated energy system (CIES) to improve its economic performance. However, the uncertainties in energy markets may result in severe deviation and fluctuation in the operation cost. It is challenging to balance the cost and risk in the operation optimization of CIES. This paper proposes a risk-managed operation optimization method based on portfolio theory for the CIES in day-ahead and real-time electricity markets. First, both kinds of markets are modeled as risky investment markets, whose returns and risks are described by the mean value and the variance of the electricity price. Second, the power purchase of the CIES from different markets is modeled as risky investment activities, in which the Markowitz portfolio theory is employed to balance the risk and cost. Third, the optimal portfolio can be obtained by solving the operation optimization problem of the CIES considering the risk preference of the owner. The case studies show that the CIES can reduce price volatility by 10.11% with only a 3.46% increase in cost in the day-ahead market. The proposed method provides a balance between different returns and risk fluctuations according to user preferences, which verified the validity and feasibility.

Ultra-low emission flexible plants for blue hydrogen and power production, with electrically assisted reformers

This study investigates the potential of natural gas-based plants designed to produce blue hydrogen and decarbonized electric power, conceived to operate flexibly depending on the electricity price. This paper considers plants based on fired-tubular reforming (FTR) and auto-thermal reforming (ATR) technologies, with MDEA-based pre-combustion CO2 capture process and partial electrification of the reformer, designed to achieve CO2 capture efficiency higher than 95 %. Heat and mass balances for the chemical and power island are evaluated at both full and part load to define the corresponding operating maps. With pre-combustion CO2 capture only, FTR plants can achieve CO2 capture rates higher than 90 %, H2 production efficiency of 73–74 % and power generation efficiency of around 47 %. Reformer electrification allows increasing overall capture efficiency to 95 %. Plants based on ATR can approach 95 % capture efficiency without electrification and achieve H2 efficiency similar to FTR but higher electric efficiency, close to 51 %. An economic analysis is performed to assess profitability of the plants under electricity price scenarios with different penetration of renewables. The economic analysis shows that flexible plants may be profitable in future scenarios with high penetration of renewables and high price variance, resulting in IRR around 10–17 % for hydrogen selling prices of 2.0–2.5 €/kg, natural gas price of 9 €/GJ and carbon tax of 100 €/tCO2.

Accrual-Based Earnings Management and the COVID-19 Pandemic

In this study, we document the accrual-based earnings management of Old Economy firms and New Economy firms (firms in the technology industry) and loss-making firms (firms with negative earnings in the pre-pandemic year, 2019) and profit firms in each economy, respectively, before, during, and in the recovery year of the COVID-19 pandemic. Using both univariate and difference-in-difference regression analyses, we find that old and new economy firms adopt different accrual-based earnings management, and Old Economy Loss firms changed their accrual-based earnings management the most during and in the recovery of the pandemic. During the 2020 pandemic, Old Economy Loss reported the lowest amount of accrual-based discretionary accruals. This suggests that Old Economy Loss firms are engaged in the most conservative approach to reporting their earnings, consistent with the big bath proposition. In the recovery year of the pandemic, 2021, we find that accrual-based earnings management reversed, with the old economy losing firms reporting the highest amounts of discretionary accruals. However, we do not find that the explanatory power of earnings on the variance of stock prices for the old economy loss firms is affected by their discretionary change in accounting accruals.

How to determine temporal yield variances of various cropping systems for modelling farmers’ production risk – Illustrated by results from a long-term field trial

When making choices among different cropping systems like conventional farming, organic farming or cropping systems without pesticides, but with mineral fertilizers, risk averse farmers will not only account for expected (i.e. average) farm income but also for income stability. The smaller the variance of a farm’s total contribution margin, the more stable is the income from a cropping system. The overall income variance can be calculated and included in quadratic risk programming models that apply the expected value-variance criterion when temporal (co-)variances of the contribution margins of the single crops are known. In this context, there is a lack in empirically well-founded approaches to identify the latter. Thus, taking a single farmer’s perspective, we first outline a way to derive temporal (co-)variances of contribution margins for crops grown at one location. Second, neglecting variances of crop prices and variable costs, we show for different cropping systems how to consistently estimate temporal crop yield (co-)variances based on available yield data from a long-term field trial by the Julius Kühn-Institut in Dahnsdorf (Germany). The four cropping systems were (b1) without fertilizer and pesticides; (b2) without fertilizer but with pesticides; (b3) with fertilizer but without pesticides; and (b4) with fertilizer and pesticides. For each system, we estimated a covariance matrix for yield data of winter rye, winter barley and pea grown from 1998 to 2021 using a mixed effects model. Moreover, we calculated means, standard deviations and coefficients of variation for the different crop yields. The size of the (co-)variances found is a valuable indicator for their order of magnitude to be used as input in quadratic risk programming with the intention of optimizing a cultivation program that accounts for individual farmers’ preferred levels of risk. Further, our statistical approach should be applied to other long-term field experiments to get deeper insights into average yield level and (co-)variance structures of crops grown in different cropping systems.

Heterogeneous awareness in financial markets

The overlook of some economic scenarios may result in unforeseen negative outcomes for investors. In this paper, we consider an order-driven financial market in which a fraction of the traders is only partially aware of the possible payoffs of a risky asset, but is aware of the possibility of facing unknown contingencies. Investors decide whether to acquire a costly signal about the payoff of the risky asset and whether to buy such asset given their awareness level and their perceived relations among signals, order flows, and prices. We show that as unawareness becomes more severe, the value of the signal to the partially aware traders diminishes. In turn, through its impact on the price, the reduced number of partially aware informed investors increases the incentives of the fully aware to acquire the signal. In the aggregate, the latter effect does not outweigh the former, so that the overall proportion of informed investors in the market is (weakly) decreasing in the unawareness level. As for the equilibrium price, a lower amount of informed traders makes it more difficult for market makers to distinguish between good and bad signals, and this brings the conditional expectations of the price closer to the unconditional one and reduces the price variance.