Real Prices Research Articles

The impact of oil price shocks on the agricultural commodity prices

AbstractIn this study, we empirically examine the effects of oil‐related shocks on the prices of nine agricultural commodities for the period 1974:01–2022:02. We decompose real oil prices into three independent parts: oil supply shocks; global demand shocks; demand shocks that are specific to the crude oil market applying a symmetric VAR model. Then, we assess the effects of these three shocks on the prices of nine major agricultural commodities individually. Based on our findings, we reveal that the responses of agricultural commodity prices to an oil price change depend significantly on the timing and the driver of the underlying oil price change.

Estimation of Value-Based Price for 48 High-Technology Medical Devices.

Value-based price is estimated quite frequently for medicines, but its application to medical devices is scarce. While some reports have been published in which this parameter has occasionally been determined for devices, no large-scale application has yet been reported. Our objective was to pursue a systematic analysis of the literature published on value-based pricesof medical devices. Pertinent papers were selected upon the criterion that the value-based pricewas reported for the device examined. The real prices of the devices were compared with their values of value-based priceand the ratios between real priceversus value-based pricewere calculated. A total of 239economic articles focused on high-technology medical devices were selected from a standard PubMed search. Among these, the proportion of analyses unsuitable for value-based price estimation was high (191/239; 80%), whereas adequate clinical and economic information for estimatingthis parameter was available in 48 cases (20%). Standard equations of cost-effectiveness were applied. The value-based price was determined according to a willingness-to-pay threshold of 60,000 € per quality-adjusted life year. Real prices of devices were compared with the corresponding estimates of value-based prices. From each analysis, we extracted also the value of incremental cost-effectiveness ratio (ICER). Our final dataset included 47 analyses because one was published twice. There were five analyses in which the ICER could be estimated for the treatment, but not for the device. In the dataset of 42 analyses with complete information, 36 out of 42 devices (86%) were found to have an ICER lower than the pre-specified threshold (favorable ICER). Three ICERs were borderline. A separate analysis was conducted on the other three devices that showed an ICER substantially greater than the threshold (unfavorable ICER). Regarding value-based prices, the values of real price were appreciably lower than the corresponding value-based price in 36 cases (86%). For three devices, the real price was substantially higher than the value-based price. In the remaining three cases, real prices and value-based priceswere very similar.To our knowledge, this is the first experience in which a systematic analysis of the literature has been focused on the application of value-based pricing in the field of high-technology devices. Our results are encouraging and suggest a wider application of cost-effectiveness in this field.

Dynamic pricing and strategic retailers in the energy sector: A multi-leader-follower approach

We consider strategic retail pricing in markets, where retail companies buy commodities at fluctuating wholesale prices and resell them to final consumers by applying dynamic retail tariffs. This is of especially large relevance in the context of energy markets where substantial wholesale price fluctuations are observed. Policy makers currently foster the introduction of such dynamic tariff schemes. From a modelling point of view, we propose a multi-leader-follower problem to investigate the implications of strategic retail pricing and we compare the impacts of implementing dynamic tariffs on retailers and final consumers. Our analysis tackles different aspects: first, we formulate the model and provide theoretical results. Second, we develop algorithms, which solve the multi-leader-follower problem and allow us to characterize the resulting market equilibria. Third, we calibrate and solve our framework based on data of the German retail electricity market for the years 2020 and 2021. This allows us to quantitatively assess the impact of introducing real time prices on retailers’ profits and customers’ benefits. As our results show, dynamic real-time pricing on the one hand typically increases market efficiency, which confirms previous results obtained without the explicit consideration of strategic behavior. On the other hand, however, as a novel aspect, dynamic real-time pricing turns out to significantly reduce equilibrium profits in case of strategic firms. This effect is especially large in environments with strongly fluctuating wholesale prices.

Applying the AD-AS Model to the Housing Market of Post-Socialist Economies

We propose applying the standard aggregate demand and aggregate supply model (AD-AS) to the housing market. It is a very simple and intuitive tool that can help shed light on the major forces at play in the market and that can supplement the use of the general equilibrium and dynamic stochastic equilibrium models (DSGE). The latter models are very sophisticated and aim to cover many aspects of the economy, but they require a significant number of long time series to estimate the model parameters. However, in many countries, such as post-socialist countries, the time series are short. Those models moreover cover only real house prices, but in certain situations we should consider real and nominal prices at the same time.

A novel LASSO-ATT-LSTM model of stock price prediction based on multi-source heterogeneous data

Stock price volatility is influenced by many factors, including unstructured data that is not easy to quantify, such as investor sentiment. Therefore, given the difficulty of quantifying investor sentiment and the complexity of stock price, the paper proposes a novel LASSO-ATT-LSTM intelligent stock price prediction system based on multi-source data. Firstly, establish a sentiment dictionary in the financial field, conduct sentiment analysis on news information and comments according to the dictionary, calculate sentiment scores, and then obtain daily investor sentiment. Secondly, the LASSO (Least absolute shrinkage and selection operator) is used to reduce the dimension of basic trading indicators, valuation indicators, and technical indicators. The processed indicators and investor sentiment are used as the input of the prediction model. Finally, the LSTM (Long short-term memory) model that introduces the attention mechanism is used for intelligent prediction. The results show that the prediction of the proposed model is close to the real stock price, MAPE, RMSE, MAE and R2 are 0.0118, 0.0685, 0.0515 and 0.8460, respectively. Compared with the existing models, LASSO-ATT-LSTM has higher accuracy and is an effective method for stock price prediction.

Energy market manipulation: Criminal law analysis and signs

Article 42 of the Constitution of Ukraine establishes the principles of preventing abuse of a monopoly position in the market and unfair competition in economic activity. Manipulation of the Energy Market – an act that violates the procedure for state regulation of the electric energy market and the natural gas market approved by the current legislation – poses an increased public danger. The purpose of the study is to investigate objective signs of manipulation in the energy market as part of a criminal offence under Article 222-2 of the Criminal Code of Ukraine. Within the framework of the study, a complex of scientific methods was applied: dialectical, hermeneutical, systemic, formal logical, generalisation, system and structural, method of ascent from the abstract to the concrete, analysis, and synthesis. It is proved that the object of manipulation in the energy market is covered by public relations that control the procedure for state regulation of the electric energy market and the natural gas market approved by the current legislation – the implementation by the state of comprehensive measures to manage the demand, prices, and volumes of the wholesale energy market, prevent abuse and violations in this area. The criminal legal content of the concept of “manipulation” is determined by the deliberate activity of a person(s) who violate the conditions of functioning of the wholesale energy market, is carried out by performing a set of illegal actions, the purpose of which is to obtain financial benefits for themselves and third parties, by changing the price of a financial asset. The essence of manipulation of the wholesale energy market is highlighted through its inherent features, which indicate a violation of the conditions necessary for making transactions on the wholesale energy market and a deviation of the asset price from the real market price in the right direction for manipulators (the presence of a process of overstating (underestimating) the total price of the contract, which leads to the loss of its market status). The practical significance of the study lies in the fact that the analysis carried out is of auxiliary importance for law enforcement practice in solving issues related to the legal qualification of manipulation in the energy market

Comparison of Real Estate Price Prediction Based on LSTM and LGBM

Housing price prediction is one of the widely discussed topics, and the application of machine learning and deep learning models to housing price prediction is a research hotspot. Exploring which models are suitable for predicting real housing price datasets has significant implications for guiding government and homebuyers' decision-making. In this study, the LSTM and LightGBM models were selected as research objects, and the suitability of the models was explored and compared using the second-hand housing price dataset in Beijing. Based on the analysis, in the task of pure time series prediction based on historical housing prices, the LSTM model had a better fit (R2=0.91), while in the task of housing price prediction based on multiple influencing factors, the LightGBM model had better comprehensive evaluation index results (R2=0.53). Both models can be used to predict pure time series, but the LSTM model is not suitable for predicting multi-factor input models that are not time series.

Oil prices in the real economy

SummaryThis paper presents a macro‐finance model of the US economy and the spot and futures markets for oil. The performance of the model is greatly enhanced by using the Kalman filter to model latent variables representing the inflation asymptote, the real price of oil and the slope of the futures curve. We find that these are dominated by innovations in observed futures prices, reflecting the importance of market expectations. Using the Kalman filter to capture inflationary shocks helps solve the notorious price puzzle, the tendency for increases in interest rates to anticipate such developments and apparently cause inflation. Futures prices also depend upon risk premiums, which we find are dominated by the latent variable representing the real oil price rather than macro variables like inflation and interest rates.

Higher Cattle Prices Are Good but Wide Profit Margins Are Better

Feeder and fed cattle prices have continued to rise throughout the first part of the year. Reduced cattle supplies and relatively stable beef demand have helped support higher prices. Fed and feeder cattle prices have reached all-time heights, at least nominally. In low inflationary environments comparing nominal prices across time would be less problematic. Contrary to previous sentiments, inflation has not been transitory. Inflation, as measured by the Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index), has increased significantly in the last 3 years (see Figure 1) and thus comparison across years should be done using real prices – adjusting nominal prices by inflation. Real prices indicate that we have yet to surpass prices producers received for feeder and fed cattle in 2013-2015.

Revisiting Balassa-Samuelson Hypothesis for Asia – A Critical Appraisal of Literature

Purpose: The present study is aimed at presenting a critical appraisal of the empirical literature on the Balassa-Samuelson hypothesis for emerging and developing Asian countries. Design/methodology/approach: A critical appraisal of the relevant studies is carried out across various important dimensions of the empirical estimation of the Balassa-Samuelson hypothesis including the scheme of sectoral division followed, definitions and proxy variables used for constructing real exchange rate and price series, choice of output and employment series and their subsequent transformation, empirical methodology followed and (theoretically) different variants of the hypothesis chosen for empirical estimation. Findings: Only a handful of studies have investigated the Balassa-Samuelson hypothesis for Asia. Nevertheless, these studies are characterized by a variety of irregularities in dealing with different important features of the theory, which may be of critical importance for yielding consistent empirical estimates. In multi-country studies for Asia, serious inconsistencies are observed whilst handling these critical aspects of the hypothesis. Such irregularities may hold serious implications for model estimates since the empirical evidence from Asia is very mixed, and in many cases, not even robust. Practical implications: The inconsistencies highlighted in this review paper hold strong implications for future research in this area. The selection of price indicators for the construction of real exchange rate series, choice of econometric methodology and the theoretical framework followed are aspects of the empirical verification of the Balassa-Samuelson hypothesis that need to be handled with great caution since they turn out to be most sensitive in relation to yielding intuitively correct and robust model estimates. Originality: To our knowledge, so far, no study on Asia has presented such an extensive appraisal of literature on the productivity-real exchange rate nexus. The present study is therefore novel in the sense that it critically evaluates studies on Asia against all those features of the Balssa-Samuelson theory which may stand responsible for yielding mixed and even contrasting empirical estimates for Asia.

Inflation Expectations and the Pass-Through of Oil Prices

Abstract Inflation expectations and the associated pass-through of oil price shocks depend on demand and supply conditions underlying the global oil market. We establish this result using a structural VAR model of the global oil market that jointly identifies transmissions of oil demand and supply shocks through real oil prices to both expected and actual inflation. We demonstrate that economic activity shocks have a significantly longer-lasting effect on inflation expectations and actual inflation than other types of real oil price shocks, and resolve disagreements around the role of oil prices in explaining the missing deflation puzzle of the Great Recession.

A long-run approach to money, unemployment, and equity prices

We propose a search and matching model that can account for a long-run joint relationship between inflation, unemployment, and equity prices. The model predicts the following long-run joint relationship: (i) a positive relationship between inflation and unemployment; (ii) a negative relationship between unemployment and equity prices; and (iii) a negative relationship between inflation and equity prices. Empirical evidence for this trivariate relationship in the post-WWII US data has been documented in previous studies. We reconfirm this evidence with expanded dataset. Furthermore, our calibration exercises show that the model results driven solely by US monetary policy can account for 62.9% and 29.8% of variations of the long-term trends of US unemployment rate and real equity prices, respectively.

Influence of Sensation Seeking and Life Satisfaction Expectancy on Stock Addiction Tendency: Moderating Effect of Distress Tolerance

Due to the COVID-19 pandemic, a very low interest rate policy was economically applied in Korea, and various investment activities through loans were activated. Real estate and stock prices rose rapidly, and many people became involved in stock investments because of economic instability. However, hastily started investment behavior resulted in economic loss and addictive behavior in stocks. The phenomenon of using stock investment to satisfy individual sensation seeking or addictive dependence on stocks due to lowered life satisfaction expectancy can become a serious social problem. However, the improvement of distress tolerance and the ability to endure pain despite frequent stock price fluctuations or lowered life satisfaction expectancy would be good alternatives to prevent stock addiction tendency. Therefore, the purpose of this study is to test the moderating effect of distress tolerance on the effect of adults' sensation seeking and life satisfaction expectancy in stock addiction tendencies. The participants were 272 adults with stock investment experience. As a result, distress tolerance significantly moderated the positive effect of sensation seeking on stock addiction tendency. In addition, life satisfaction expectancy did not significantly increase in the group with high distress tolerance even if life satisfaction expectancy was lowered. These results suggest that stock addiction can be prevented by enhancing distress tolerance.

Rational bubbles: Too many to be true?

Issues that arise in the practical implementation of the Phillips et al. (2011) and Phillips et al. (2015a) recursive procedures for identifying and dating explosive episodes in time series are considered. It is argued that the use of critical values for right-tailed unit-root tests obtained under the assumption of a drift whose magnitude depends on the sample size and becomes negligible in large samples results in over-rejection of the unit-root hypothesis when, as in many financial time series, the deterministic drift effect is non-negligible relatively to the stochastic trend. In addition, the standard practice of using conventional levels of significance for critical values involved in the algorithms that locate the origination and termination dates of explosive episodes lead to false discoveries of explosiveness with high probability. The magnitude of these difficulties is quantified via simulations using artificial data whose properties reflect closely those of real-world time series such as stock prices and dividends. The findings offer a potential explanation for the relatively large number of apparent explosive episodes that are often reported in applied work. Ways of overcoming the aforementioned difficulties by using bootstrap-based calibration techniques are considered. An empirical example focusing on monthly U.S. data on real stock prices and real dividends is also discussed.

The impact of oil price shocks on economic growth in Algeria

In this study, we investigate the consequences of oil price shocks on Algerian economic growth, both on an aggregated and a disaggregated level. We adopt the method used in (Kilian, 2009) in which global oil supply shocks, global demand shocks, and anticipated demand shocks are defined. We use a two structural VAR models. The first model identifies shocks to oil prices using monthly data on oil production, oil real prices, and indicators of global eco- nomic activity. The second model describes the dynamics of a set of macroeconomic varia- bles: real overall GDP, hydrocarbon GDP, agricultural GDP, construction GDP, and interna- tional trade variables. To grasp the impact of oil shocks on sectoral growth, the model is aug- mented via three external shocks identified in the first stage. Our results are twofold. First, they show the significance of the oil price shocks' impact on the Algerian economy. Second, they indicate an asymmetry in this impact. Negative shocks to oil prices have depressive ef- fects on economic activity as a whole, including the non-hydrocarbon sector. We detect a delay-effect; global demand shocks affect economic sectors after 5-7 quarters, whereas oil price shocks affect the hydrocarbon sector after 1-2 quarters only.

Empirical Study on Chinas House Prices

This study aims to empirically study house prices and their relationship with China's critical economic and financial metrics. The key metrics include interest rate, inflation rate, and stock index prices. This study shows that stock index prices do not significantly impact house prices in China. At the same time, long-term interest and inflation rates positively impact house prices in China.Additionally, short-term interest rate possesses a negative im-pact on house prices. This can shed light on policymakers in China when making economic decisions to influence house prices in China. On top of this, this study also compared house prices and real estate stock index prices and concluded that the two differ significantly.

Automation of Krishi Market System and Real Time Price Display System

A krishi market system is automated by utilising technology to speed up the buying and selling of agricultural goods. To gain a complete picture of the agricultural market, the system gathers information from a variety of sources, including farmers, dealers, and government organisations. In order to find trends, patterns, and insights that can aid in decision-making, the data is then processed and analysed. The system displays the data in a comprehensible way using visualisation techniques like charts, graphs, and maps to make it simpler for users to understand and make defensible judgements. Additionally, the system automatically creates invoices for transactions based on the gathered and processed data. This guarantees correctness and gets rid of mistakes that could happen with manual bill production. The krishi market system with visualization of data and an automated bill generation system is implemented as a website application that provides a user-friendly interface for farmers, traders, and government agencies to access the data and bills. The website application also integrates with payment gateways to enable online payment for transactions, making the process faster, more secure, and more convenient for all parties involved. Furthermore, the website application provides alerts and notifications to users based on their preferences, including alerts for price changes, new crops in the market, and payment reminders. The system also ensures the security and privacy of the data collected and stored, using encryption and secure data storage methods to prevent unauthorized access. Overall, the automation of a krishi market system with visualization of data and an automated bill generation system for a website application streamlines the process of buying and selling agricultural products, making it more efficient and convenient for all parties involved. The website application provides a user-friendly interface that makes it easy to access the data and bills, and the integration with payment gateways makes transactions more secure and convenient.

STOCK PRICE PREDICTION USING MACHINE LEARNING – AN UNPRECEDENTED APPROACH

In this research, we make an effort to put a machine learning method to stock price prediction into practice. Stock price forecasting uses machine learning effectively. In order to make wiser and more accurate financial decisions, the goal is to forecast stock prices. In order to improve stock forecast accuracy and generate lucrative trades, we suggest a stock price prediction system that combines mathematical functions, machine learning, and other external aspects. There are two different stock types. You may be familiar with intraday trading through the phrase "Day trading". Interday traders frequently hold securities positions for multiple days up to weeks or months, but at least from one day to the next. Because they have the capacity to store historical data, LSTM’s are particularly effective at solving sequence prediction issues. This is significant in our situation since a stock's historical price plays a key role in determining its future price. While predicting a stock's real price is difficult, we can create a model that will predict whether it will rise or fall. Keywords: LSTM, CNN, ML, DL, Trade Open, Trade Close, Artificial Intelligence, Stock Market, Machine Learning, Predictions.

A weekly structural VAR model of the US crude oil market

We present a weekly structural Vector Autoregressive model of the US crude oil market. Exploiting weekly data we can explain short-run crude oil price dynamics, including variations related with the COVID-19 pandemic and with the Russia’s invasion of Ukraine. The model is set identified with a Bayesian approach that allows to impose restrictions directly on structural parameters of interest, such as supply and demand elasticizes. Our model incorporates both the futures-spot price spread to capture shocks to the real price of crude oil driven by changes in expectations and US inventories to describe price fluctuations due to unexpected variations of above-ground stocks. Including the futures-spot price spread is key for accounting for feedback effects from the financial to the physical market for crude oil and for identifying a new structural shock that we label expectational shock. This shock plays a crucial role when describing the series of events that have led to the spike in the price of crude oil recorded in the aftermath of Russia’s invasion of Ukraine.

Stock Price Prediction using LSTM Model

Stock prices represent the development prospects of a company and play an important role in predicting future economic trends. With the development of deep learning, in this paper, our goal is to predict stock prices through deep learning algorithms. The author uses the Long-Short Term Memory (LSTM) network to make predictions by predicting the real stock prices of Google, Amazon, and Apple, and gives the visualization results of the training and testing stages. The results validate the effectiveness of the LSTM model.