Real Prices Research Articles

The Usage of Markov Chain Monte Carlo (MCMC) Methods in Time-varying Volatility Models

Abstract Markov Chain Monte Carlo (MCMC) techniques, in the context of Bayesian inference, constitute a practical and effective tool to produce samples from an arbitrary distribution. These algorithms are applied to calculate parameter values of predictive models of the phenomenon of varying volatility in data time series. For this purpose, 3 such research models of time-varying volatility are simulated in STAN a probabilistic programming language for statistical inference. The accuracy of these models’ predictive function is confirmed by applying in data time series with known prior values. Moreover, Stan models’ performance is illustrated by the real stock prices of two shares in the stock market of New York. Finally, an Information Criterion of the results is applied to each model as well, to evaluate their predictive ability, comparing and selecting the most effective one. Keywords: MCMC, Time-series, Time-varying volatility models, STAN.

Large‐scale impacts of selective logging on canopy tree beta‐diversity in the Brazilian Amazon

Abstract Selective logging is one of the largest drivers of tropical forest degradation. While logged forests often retain high alpha‐diversity of tropical trees at local spatial scales, understanding how selective logging impacts tree beta‐diversity and community composition across far larger spatial scales remains a key unresolved question. We leverage large datasets of more than 155,000 adult trees over 35 cm DBH covering 3100 ha of Amazonian rainforest to inform simulations of selective logging harvests across a gradient of logging intensity (0–40 m3 ha−1). These simulations incorporate real world price data, account for all forest damage throughout the harvest process and assume preferential harvest of the most valuable stems. We use the simulations to assess how selective logging affects canopy tree beta‐diversity and composition across large spatial scales, whether nestedness or turnover of species best explains variation in communities across space, and how the spatial scale of sampling influences observed beta‐diversity effects. Selective logging had minimal impacts on beta‐diversity across the canopy tree community, but caused substantial subtractive heterogenization in community composition for larger trees, in particular very large trees over 110 cm DBH. Turnover is the dominant component of tree beta‐diversity in unlogged and logged forests. Increasing the spatial grain of sampling reduced the observed importance of logging in explaining patterns of beta‐diversity in very large tree communities. Synthesis and applications. Minimal impacts on tree beta‐diversity across large spatial scales points towards the retention of substantial conservation value in logged tropical forests. Strong subtractive heterogenization in very large trees indicates the breakdown of broad scale patterns of composition with potential negative consequences for recruitment processes, fauna reliant upon emergent trees, and other ecosystem functions and services. Avoiding large‐scale erosion of very large tree community composition in the Amazon requires stronger conservation policies, including enforced retention or maximum cutting diameters.

Die Entwicklung des Pkw-Bestands in Deutschland

Abstract The number of passenger cars in Germany has increased continuously in recent years and reached a new high of 48.8 million in 2023. This means that on average, there are 1.2 passenger cars for the 40.7 million households in Germany. On average, new registrations amount to 3.1 million vehicles per year over the past 10 years. The increase in passenger cars took place in almost all German districts and cities. Exceptions are regions with a strong population decline. The thesis that cars would become less important for mobility can be clearly rejected against the background of the present trends. There are two problems associated with the growing number of cars: On the one hand, CO2 emissions are associated with the operation of passenger cars. However, various upcoming regulations will impact this issue.. But another problem will remain: A large number of cars is accompanied by a high consumption parking space. To solve this problem, real prices must be charged for parking space.

A Review on Issues of Using Real Estate Development Trust and Price Trust as Performance Guarantee Mechanisms for Presale Housing

A Review on Issues of Using Real Estate Development Trust and Price Trust as Performance Guarantee Mechanisms for Presale Housing

Optimal Investment of Defined Contribution Pension Based on Self-Protection and Minimum Security

This paper mainly studies the optimal investment problem of defined contribution (DC) pension under the self-protection and minimum security. First, we apply [see formula in PDF] theorem to obtain the differential equation of the real stock price after discounting inflation. Then, under the constraint of external guarantee of DC pension terminal wealth, self-protection is introduced to study the maximization of the expected utility of terminal wealth at retirement time and any time before retirement. The explicit solution of the optimal investment strategy of DC pension at retirement time and any time before retirement should be derived by martingale method. Finally, the influence of self-protection on the optimal investment strategy of DC pension is numerically analyzed.

Estimating Long-run Elasticity between Crude Oil Consumption, Real Oil Price, and Real GDP in Global Markets

The study examine the long-run relationship between crude oil consumption, real oil price, and real GDP using a quarterly time series from 1993 to 2020. the empirical analysis uses the Dynamic Least Squares (DOLS) model for both short-run and long-run elasticity among the model variables to estimate the short-run and long-run elasticity of demand for crude oil consumption in 10 regions using Panel Dynamic Least Squares (DOLS) and Pooled Mean Group-AR Distributed Lag Models (PMG/ARDL). The empirical analysis findings confirme that the demand for crude oil internationally is highly insensitive to changes in price and real GDP.

Alcohol prices, the April effect, and the environment, in violence-related injury in England and Wales

Violent injury varies widely across England and Wales as does the price of alcohol. While the links between alcohol consumption and violence are well established in the medical and epidemiological literature, a causal link is questionable. This paper cuts through the causative argument by reporting a link between the general price of alcohol and violence-related injury across the economic regions of England and Wales. It examines the influence of the real price of alcohol and identifies an ‘April effect’ that coincides with the annual uprating of alcohol prices for excise duties, on violence-related injuries recorded at Emergency Department attendance. The data are monthly frequency of violent injury rates covering the period 2005–2014 across the economic regions. The principal finding is that a one-way relationship between the real price of alcohol and violent injury is established, and tax policy can be used to reduce the incidence of violent injury and the associated health costs.

Price Incentives and Supply Response in Cocoa -A Case of Border Regions of Ghana

Smuggling of cocoa between Ghana and its neighbouring producing countries such as Cote d’Ivoire and Togo is a frequent occurrence often to the advantage of the country posting higher producer price. Ghana is a net loser with regards to the smuggling of cocoa given relatively lower real producer price compared to its counterparts in production. Additionally, the close geographical location of key producing regions to the border lends support to such activities. This study investigates the supply response of cocoa in producing region such as Western North, Western South and Brong-Ahafo with proximity to Cote d’Ivoire and Volta which shares borders with Togo using data from 1981 to 2017. The study further estimates the volume of cocoa crop lost from these regions to the country’s neighbours from 2000 to 2017. In the findings, relative price differences significantly impact the volumes of reported crop for the country with 1% higher price abroad driving down production volumes in Western North, South and Brong-Ahafo by 0.53%, 0.48%, and 0.11% respectively. In 2013/14 crop year the country lost averagely 25,494 MT and 21,883 MT due to smuggling out of Western North and South, moderating to 1,559 and 1,358 in 2016/17. The Volta region consistently records outflow in each crop year except for 1999/2000, with peak losses in 2010/11 of 2,160 MT moderating to 1,197 in 2016/17. Net gains into the country from cocoa smuggling spanning from 1999/2000 to 2016/17 is averagely 23,534 MT and 15,336MT into Western North and South. However cocoa smuggling for both Ghana and its neighbours is on the descent in recent years as price disparities reduce. Intensifying the collaboration between Ghana and its close counterparts to minimize real crop price differences will effectively curtail the menace and the negative fallout of this to each country.

Energy Crisis—Alternative Use of Winter Bread Wheat Grain Depending on Protein Content

Our economic analysis aimed to evaluate the profitability of winter bread wheat production based on two fundamental aspects. The first was the grainprotein content as a criterion for determining grain prices. The other was a comparative simulation of production profitability relying on grain production costs in 2015 and 2022. We used the results of a field experiment conducted in 2014 and 2015 involving winter bread wheat fertilised with nitrogen applied at progressive increments of 40 kg N ha−1 within arange from 0 to 240 N ha−1 with or without fungicide protection. We assumed that experimental factors significantly affected both the yield and the market value of grain, and hence the profitability conditioned by wheat prices on global markets. The working hypothesis of this paper is: wheat production profitability has not changed in the face of a global energy crisis. Our analysis shows that growing bread wheat generates profit when inputs are high: these inputs include high nitrogen rates and full crop protection. The real grain selling price guarantees production profitability. We should consider that, in the circumstances of a global energy crisis, the world should possibly switch to baking products from low-protein flour. Only upon such an assumption can the expenditure on fertilisers and fungicides be significantly reduced.

Identifying Common and Idiosyncratic Explosive Behaviors in the Large Dimensional Factor Model with an Application to U.S. State-Level House Prices

Abstract This study applies the date-stamping methodologies for explosive behaviors proposed in the seminal work of Phillips, P. C. B., and J. Yu. (2011. “Dating the Timeline of Financial Bubbles during the Subprime Crisis.” Quantitative Economics 2 (3): 455–91), Phillips, P. C. B., S. Shi, and J. Yu. (2015a. “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500.” International Economic Review 56 (4): 1043–78), and Phillips, P. C. B., S. Shi, and J. Yu. (2015b. “Testing for Multiple Bubbles: Limit Theory of Real Time Detectors.” International Economic Review 56 (4): 1079–134) to a large dimensional factor model. To this end, we compare two methods of identifying common and idiosyncratic components: the Panel Analysis of Nonstationarity in Idiosyncratic and Common Components (PANIC) method by Bai, J., and S. Ng. (2004. “A Panic Attack on Unit Roots and Cointegration.” Econometrica 72 (4): 1127–77) and the Cross-Sectional regression (CS) method by Yamamoto, Y., and T. Horie. (2022. “A Cross-Sectional Method for Right-Tailed PANIC Tests under a Moderately Local to Unity Framework.” Econometric Theory (forthcoming)). We show that, when the explosive behavior lies only in the common component, the origination and termination dates are precisely estimated by either method. However, when the explosive behaviors exist in idiosyncratic components, only the CS method can detect them. We apply our method to the U.S. state-level real house price indices. We find that the 2000s boom was driven by not only the national bubble factors but also local components, while the 2010s onward expansion is dominated by the effect of national components.

Analysis of Factors Affecting the Dynamics of Residential Real Estate Prices in Russia

In this paper, we have constructed a VAR model to identify and assess the impact of real interest rate shocks, real estate demand, oil prices, uncertainty, and aggregate business activity on residential real estate prices in Russia. The relevance of the research is due to the following: the dynamics of real estate prices determines the consumer and investment behavior of households, and serious fluctuations in real estate prices lead to adverse consequences in many areas of life, so more and more researchers are asking questions about the presence of bubbles in the real estate market, which can be dangerous to the stability of the economy. In addition, a sharp increase in the cost of housing in Russia in 2020 is an open question for researchers. Our goal is to determine what factors caused the rise in real estate prices in Russia in the time interval from the Q1 of 2000 to the Q2 of 2022. A VAR model with a Cholesky decomposition was used for the evaluation. Several specifications were considered with the inclusion of the real oil price as an exogenous variable and a set of endogenous variables: real GDP, real interest rate, uncertainty index and housing price index. The main conclusion of the paper is that the housing market is sensitive to identified macroeconomic shocks, and a decrease in the interest rate leads to an increase in demand and real estate prices. The estimate of the long-term elasticity of housing prices for oil prices was 0.35, the dynamics of oil prices explained a significant proportion of the variation in real estate prices, but the predominant role in housing price fluctuations is given to housing demand shocks. The housing demand shocks in Russia itself had a negligible impact on GDP.

A complex adaptive agent modeling to predict the stock market prices

Understanding the behaviors of financial markets and their participants remains a challenging problem to resolve. Adaptive agents, which switch from fundamentalist to chartist behavior, are examined in some recent work. In this paper, we propose an adaptive agent-based model that combines three forecasting behaviors of financial agents: fundamentalist, chartist, and mimetic. The weighting of each type of behavior in the final forecasting changes according to the market cycle. Our model adapts to the different cycles of the market.We assess the ability of the proposed model to predict and explain the dynamics of stock market price formation. First, on the microscopic level, we consider four agent-based models: fundamentalist, chartist, mimetic and adaptive. We compare the prices generated by the different prediction models to the real prices generated by the US market (S&P 500 index) over the period (1990–2021). Second, at a macroscopic level, we set up a multi-agent system to simulate an artificial stock market composed of the four types of agents with different market fractions. We compare the prices generated by the artificial market with the real data generated by the US market.The series of statistical analyses that we have carried out allows us to conclude that: the proposed adaptive agent exists in the stock market, he offers a better accuracy of price predictions compared to fundamentalist, chartist and mimetic models and that he can explain the dynamics of the stock market prices formation when he dominates.

Russian housing price forecast based on BP neural Network

Contemporarily, Russia's real estate market has huge potential for investors. The purpose of this paper is to investigated the impact of various factors on the real estate market price based on various statistical models. We collected past housing price data from the Internet. Subsequently, PCA model was adopted to reduce the dimension of the data to simplify the calculation. Finally, the BP model is implemented to predict the price changes in the real estate market. After calculation, our forecast data and the reality of housing prices are slightly different, but the forecast of the real estate market is consistent with the reality. In order to further find the influence of various factors on housing price, we also calculated the characteristic importance of different factors and found the most important influencing factor, i.e., the region. These results can provide investors with an idea to analyze the real estate market prices, and help policy makers find the real estate market prices in line with the market rules to avoid the occurrence of bubbles.

Valuation and Forecasting of Cryptocurrency: Analysis of Bitcoin, Ethereum and Dogecoin

Currently, cryptocurrency has become a research and investment topic of great concern and attracted considerable attentions in a wide range of fields. Stocks are well known as a form of investment, and there are countless studies on how its price trends. Cryptocurrencies, however, are not easily predicted due to their extremely high volatility. This paper implements the forecasting results by taking three major cryptocurrencies (i.e., Bitcoin, Ethereum, Dogecoin) as examples for the period starting from January 1st, 2020 to May 31st 2022. Four popular prediction models (XGBoost, LightGBM, GARCH, ARIMA) are applied in Python by training models and testing the prediction results. According to the analysis, XGBoost and LightGBM can forecast the future prices for all three cryptocurrencies exactly apart from the turning points when prices rise and drop suddenly. Although the predicting trends of GARCH, ARIMA have differences from the real price, they can forecast well except the unexpected situations such as COVID-19. Overall, relatively reliable and accurate prediction models can be provided for investors to apply and make wise decisions in investments based on the results of this study.

Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning

Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR approach has been useful for correcting supply–demand imbalances as technology has evolved. There are several systems for determining and controlling the DR. However, most of these solutions are unable to control rising demand or forecast prices for future time slots. This research provides a Real-Time Pricing DR model for energy management based on deep learning, where the learning framework is trained on demand response and real-time pricing. The study data in this article were taken from the Australian Energy Market Operator (AEMO), and the learning framework was trained over 17 years of data to forecast the real future energy price and demand. To investigate the suggested deep learning-based dynamic pricing strategy, two prediction instances are addressed: actual–predicted demand and actual–predicted price. We estimated pricing and demand outcomes using long short-term memory (LSTM), which were then greatly improved by architectural changes in the model. The findings showed that the suggested model is suitable for energy management in terms of demand and price prediction.

Two-Way Fixed Effects Estimation on the Impact of rising house prices on the capital structure of real enterprises in China

Since the implementation of China's reform and opening-up policy, rapid economic growth has led to the rapid development of the real estate market and housing prices have entered the fast lane of growth. As housing prices rise, the allocation of credit funds in China will continue to be tilted towards the real estate sector. This has a "crowding out" effect on the real economy and is not conducive to the positive optimisation of corporate capital structure. Little research exists on the lagged nature of house price inflation and its impact on the capital structure of real enterprises. To fill this gap, this paper investigates the lagged effect of house price changes on the adjustment of capital structure of real enterprises and the heterogeneity between regions based on a two-way fixed effects model with a sample of 30 Chinese provinces over the period 2000-2020. The study finds that in the short run, rising house prices do not have a significant impact on the capital structure of real enterprises. In the long run, enterprises' debt levels rise and their capital structure adjusts negatively. There is a lag in the central and western regions and no lag in the eastern regions. After a series of robustness tests, the results remain significant and robust. This study provides a reference for the government to establish a long-term mechanism for the stable development of the real estate market.

Technology Shocks and the Efficiency of Equity Markets in the Developed and Emerging Economies: A Global VAR Approach

We tested the connection between technology shocks and the efficiency of equity markets in developed and emerging economies. We augmented the Global Vector Autoregressive (GVAR) database that covers data on 33 developed and emerging markets with the newly constructed data for technology shocks involving two variants, one with 164 countries (GTS-164), and the other, which is more region-specific. covering only Organization for Economic Co-operation and Development (OECD) countries (GTS-OECD). Our analysis was then modeled with GVAR methodology. We found that a one standard positive innovation shock to global technology (GTS-164) raises real equity prices in nearly 70% of the markets considered, and this is sustained over the forecast periods. However, the response of real equity prices to a global-specific technology shock (GTS-OECD) is rather different. While this shock resulted in the immediate rise in real equity prices, it is only transient and dissipated after the third quarter of the forecast horizon in about 85% of these markets. By implication, the efficiency of the real equity market was assured for the region-specific technology shock rather than for the more encompassing measurement that takes account of numerous markets, not minding whether these markets are developed or emerging. In sum, technological shocks seem to have greater impacts on the efficiency of developed (including Euro) markets than other markets.

Estimating the real exchange rate misalignment of the Algerian dinar during the period (1980-2019) : an empirical study (Arabic)

The aim of this study is to estimate the long-run Equilibrium Real Exchange Rate (ERER) and to assess the degree of misalignment of the Algerian Dinar for the period (1980-2019) using ARDL approach. Considering that the equilibrium exchange rate of the Algerian dinar is determined by a set of fundamentals of the national economy, the study reached the following main results: In the long run, there are positive relationships between the real effective exchange rate and the differential productivity, real oil price and net capital flows. Whereas, there are inverse relationships between the dependent variable and each of the trade openness, General government final consumption expenditure (% of GDP). The second result is that the REER registered a number of deviations from its equilibrium level, that vary between overvaluation and undervaluation. The third result is that the exchange rate cannot be the only adjustment variable, given the extent of macroeconomic imbalances (internal and external). Parallel efforts at fiscal consolidation and deep structural reforms to diversify the economy and accelerate growth are also essential.

Asymmetric effects of oil price shocks on the demand for money in Algeria

This paper investigates the symmetric and asymmetric effects of real oil prices on real money demand in Algeria over the period 1999:Q1–2020:Q1. Using a linear autoregressive distributed lag model (ARDL), and a nonlinear autoregressive distributed lag model (NARDL), we find no evidence of a long-term relationship among the variables. Interestingly, we reveal that the NARDL model provides strong evidence of a stable money demand. Moreover, the evidence shows that, in the short-term, only real oil price reductions will result in a decrease in real money demand. In the long-term, our findings suggest that oil price increases (decreases) will lead to higher (lower) money demand. Additionally, we find that negative oil price shocks will yield a stronger effect on money demand compared to positive shocks. These results indicate that money demand is a channel through which oil price shocks may affect the macroeconomic activity in Algeria. Therefore, Algerian policymakers should incorporate this asymmetric relationship between oil prices and money demand in designing their monetary policies.

Wild Mushrooms as a Source of Protein: A Case Study from Central Europe, Especially the Czech Republic

Wild mushroom foraging has a long tradition, especially in the region of Central Europe. Wild mushrooms are a valuable food resource, as they provide nutritional benefits to the European population. They offer a relatively high content of protein and are traditionally used in many European cuisines as a substitute for meat. This is particularly true in times of crisis, such as wars and pandemics. The study presented in this paper shows that wild mushrooms can substitute around 0.2 percent of daily protein intake and contribute around 3% to the agricultural output of the Czech economy, which was selected as a representative for Central Europe. The calculated real price of wild mushrooms indicates their increasing popularity as a source of food protein in Central Europe, while their price seems to be independent of the quantity supplied.