Real Prices Research Articles

Cost-Effectiveness Analysis of Vaccines for COVID-19 According to Sex, Comorbidity and Socioeconomic Status: A Population Study.

Coronavirus disease 2019 (COVID-19) vaccines are extremely effective in preventing severe disease, but their real-world cost effectiveness is still an open question. We present an analysis of the cost-effectiveness and economic impact of the initial phase of the COVID-19 vaccination rollout in the Basque Country, Spain. To calculate costs and quality-adjusted life years for the entire population of the Basque Country, dynamic modelling and a real-world data analysis were combined. Data on COVID-19 infection outcomes (cases, hospitalisations, intensive care unit admissions and deaths) and population characteristics (age, sex, socioeconomic status and comorbidity) during the initial phase of the vaccination rollout, from January to June of 2021, were retrieved from the Basque Health Service database. The outcomes in the alternative scenario (without vaccination) were estimated with the dynamic model used to guide public health authority policies, from February to December 2020. Individual comorbidity-adjusted life expectancy and costs were estimated. By averting severe disease-related outcomes, COVID-19 vaccination resulted in monetary savings of €26.44 million for the first semester of 2021. The incremental cost-effectiveness ratio was €707/quality-adjusted life year considering official vaccine prices and dominant real prices. While the analysis by comorbidity showed that vaccines were considerably more cost effective in individuals with pre-existing health conditions, this benefit was lower in the low socioeconomic status group. The incremental cost-effectiveness ratio of the vaccination programme justified the policy of prioritising high-comorbidity patients. The initial phase of COVID-19 vaccination was dominant from the perspective of the healthcare payer.

Design the Fuzzy Variable to Determine the Relation Between Price and Assessment

Accounting produces information that is used for decision-making, so helpful standards of accounting are required. One of the challenging aspects of accounting is trade assessment. This subject is important in the buying process (and also in the fund management). In the buying process, customers are looking for trades with high profit, but determining the exact value for the profit is hard. Profit is categorized into Cash and Obligation. In this paper, the spread of cash operation is considered as a cash component, and the difference in the receive accounts, the difference in the inventory, and the difference in the payment accounts are considered as obligations. To examine, the financial information of 10 trades in the TEHRAN EXCHANGE STOCK are used. In this paper to determine the relation between the assets and the real price, a fuzzy approach is used. Fuzzy systems include some components and diversity in each of them may influence the final result. Therefore the influence of each fuzzy component needs to be examined, but the goal of this paper is to use the fuzzy system. Hence we use a simpler shape of fuzzifier, defuzzifier, and inference engine.

Profitability of Alternative Battery Operation Strategies in Photovoltaic Self-Consumption Systems under Current Regulatory Framework and Electricity Prices in Spain

The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.

A Decentralized Auction Model for Sustainable Housing Rental Market

The Internet of Things has driven the transformation of the traditional offline rental model to online rental platforms such as Zillow, Zigbang, and Lianjia. These platforms provide abundant housing options, detailed information, convenient communication, and greater price transparency. However, existing online rental platforms are still centralized and rely on third-party intermediaries for settlement, which raises concerns about the integrity of real estate data, transaction security, and price transparency. To improve the real estate leasing business, we propose leveraging the decentralized nature of blockchain technology. By eliminating intermediaries, we can ensure customer privacy and reduce transaction costs. This strategy revolves around smart contracts as a core component, automatically matching landlords and tenants based on their offers, achieving transparency, and maintaining anonymity for both parties. We introduce a novel auction system that combines the features of interactive auctions and sealed-bid auctions, enabling landlords and tenants to engage in range bidding. In this study, we developed a detailed algorithm to define how smart contracts function during the auction process. Through a cost analysis, we demonstrate the economic feasibility and provide a secure, transparent, and reliable approach for online auctions.

Option pricing: Examples and open problems

Abstract There is no method of predicting the price of an option other than hedging strategies such as the binomial hedging strategy, the Black–Scholes hedging strategy and others. We will study these two basic hedging strategies in terms of their feasibility, and we will see that the Black–Scholes hedging strategy is not feasible because this strategy demands instantaneously rebuilding the replicating portfolio. Consequently, the real world prices of the options are not relevant at all with the Black–Scholes hedging strategy! We will suitably redefine the binomial hedging strategy so that it will be practically useful and present other feasible and generally more effective hedging strategies with some of them practically useful for options with no tradable underlying assets. Finally, we will mention some open questions related to the above.

Pengaruh Harga Internasional, Ekspor, Harga TBS, Dan Volume Produksi Biodiesel Terhadap Harga CPO Domestik

The palm oil industry downstream program supports the development of new energy made from palm oil, namely biodiesel. Biodiesel is used as a blend for fuels such as diesel. One of the goals of developing the biodiesel industry is to produce new renewable energy sources so that it can reduce dependence on fossil energy. With the development of the biodiesel industry, Indonesia's consumption of crude palm oil (CPO) will increase. It is feared that this could lead to an increase in domestic CPO prices. This research was conducted to analyze the impact of developing the biodiesel industry on domestic CPO prices. The data used is secondary data in the form of annual data from 1997-2021. The research analysis used multiple linear regression which is processed with Ordinary Least Square (OLS). The variables in this research are international CPO prices, CPO export volumes, biodiesel production, prices of fresh fruit bunches (FFB), and productivity of oil palm plantations. The real domestic CPO price was significantly affected by international CPO prices, CPO export volume, FFB prices, and the productivity of oil palm plantations. Biodiesel production was not proven to have a significant effect on domestic CPO prices.

A tensor based price evaluation approach for the used mobile phone recycling

The automatic price evaluation is an important and challenging issue for the used mobile phone recycling. However, due to complicated relationships between attributes and price as well as insufficient sample data, current approaches cannot achieve accurate price evaluation for the used mobile phones, which significantly reduce the efficiency for the used mobile phone recycling. To this end, a tensor based approach is proposed in this paper, which can achieve accurate and efficient price evaluation for used mobile phones. In the proposed approach, first, the mutual information based attributes selection mechanism and the boxplot based sample selection mechanism are employed to select the most relevant attributes and suitable price samples from used mobile phone dataset. Then, a tensor model is constructed to establish multi-dimensional relationships between selected attributes and prices of used mobile phones. Finally, the missing values in the constructed tensor model is completed through the gradient descent and CANDECOMP/PARAFAC decomposition algorithms. The completed tensor model can be directly used for the price evaluation of used mobile phones based on their attributes without further calculation. Based on the real price dataset of used mobile phones, the experiments indicate that the proposed approach outperforms most of current approaches for the price evaluation for the used mobile phones.

Technical-Economic Analysis of Energy Efficiency Solutions for the Industrial Steam System of a Natural Gas Processing Plant

Steam, which is primarily employed as a heat transfer medium in process plants, is one of the most widely utilized energy carriers in the industrial sector. One of the factors that affects the cost of steam is how well the condensate collection, steam supply, and return systems of industrial steam systems perform. In a case study, the steam systems of a natural gas processing plant were simulated. The amount of demineralized water loss and, consequently, the identification of various solutions to improve the system were analyzed. The whole steam system was simulated using the MEASUR software platform (v 1.2), and by placing the operational information of the steam system, it was possible to create a baseline for the system, model saving solutions, and finally, provide a technical and economic evaluation of the solutions. Due to the high loss of steam condensate in the SRU steam system (more than 3000 kg per hour), solutions to improve the energy efficiency of the SRU steam system in the form of a maximum recovery of steam condensate (replacement of defective steam traps, redesign of the low-pressure condensate collection network, and high-pressure waste condensate collection) were evaluated with two price assumptions of current energy prices and real prices (the energy saving value of one cubic meter of natural gas is equal to 13 cents). The results show that, for current prices, the investment return period will be between 11.8 and 3.8 months. Moreover, in the main steam system of the refinery (unit 9200), there are three solutions: replacing and repairing defective steam traps, installing an expansion turbine instead of a steam pressure relief valve (PRV), and other solutions (including increasing boiler efficiency, automatic control of the boiler, and energy recovery boiler blowdown) under two price assumptions, the current and real prices of natural gas and demineralized water, were evaluated, and the modeling results show that the investment return period for each of the above solutions at the current prices is 10.2, 186, and 13.3, respectively. The investment return period is based on assuming real fuel and BFW prices are equal to 2.0, 37.6, and 1.7, respectively.

Don’t Look Up: House Prices in Emerging Europe

Abstract This paper investigates how housing prices respond to economic, financial and demographic conditions in emerging markets in Europe. We use quarterly data covering 10 countries over the period 1998–2022 and implement a panel quantile regression approach to obtain a granular analysis of real estate markets. Overall, economic, financial and demographic developments explain real house price growth in emerging Europe, with income growth having the most significant impact. Quantile regression estimations show that income growth matters more for higher quantiles of the property market. We also find that an increase in short-term or long-term interest rates has a price-dampening impact, indicating that a higher cost of borrowing is associated with lower real house price growth. These results indicate that the downturn in house prices could deepen with the looming economic recession and soaring interest rates.

Investigating the monetary policy risk channel based on the dynamic stochastic general equilibrium model: Empirical evidence from Iran.

Investigating the credit channel and monetary policy risk channel in Iran's economy is the aim of this article. According to empirical studies, expansionary monetary policy increases the risk of banks, and on the other hand, the risk of banks affects economic activities and price levels. In order to investigate the mechanism of the credit channel and the risk channel (as a new channel), the effect of monetary policy on real variables and price levels in Iran's economy, the Dynamic Stochastic General Equilibrium (DSGE) model has been used by entering the information of the banking system and considering moral hazard and adverse choices. The obtained results show that there is a credit channel and a monetary policy risk channel for Iran's economy, and the expansionary monetary policy shock causes output, inflation, private sector consumption, investment, net worth in the economy and lending to increase. Also, when a credit shock occurs, with the increase in banks' lending power, production, private sector consumption, investment, net worth and total lending increase and the inflation level decreases. Also, by applying the risk shock caused by the increase in inflation and the decrease in consumption and investment, the volume of lending increases and the level of production does not change much.

Bubble detective: City‐level analysis of house price cycles

AbstractThis paper investigates house price dynamics at high frequency using city‐level observations during the period 1994–2022 in Lithuania. We employ multiple time series‐based econometric procedures to examine whether real house prices and house price‐to‐rent ratios exhibit explosive behaviour. According to these recursive right‐tailed test results, we reject the null hypothesis of no‐bubble and find evidence for long and multiple periods of explosive behaviour in the housing market in all major cities during the sample period. While the size of bubbles varies across cities, especially when we use the house price‐to‐rent ratio, there is clearly a similar boom‐bust pattern in Lithuania. Large house price corrections can in turn have adverse effects on economic performance and financial stability, as experienced during the global financial crisis and other episodes in history.

Revisiting the connectedness between oil prices and uncertainty indicators in BRICS countries

The paper revisited the connectedness between oil prices (ROILP), economic policy uncertainty (EPU), and geopolitical risks (GPR) in the BRICS countries using data from January 1997 to October 2022. The study applied the spillover index, a rolling window based on the generalized VAR framework, and a wavelet coherence approach to investigate the passthrough, multiple regime changes, co-movements, and lead-lag status between the series. The study found that oil prices were a significant transmitter of shocks to other variables in the system among countries, with Brazil having the highest spillover index (11.7%) followed by South Africa (10.5%), India (10.2%), China (9.5%), and Russia (9.2%). The rolling spillover from GPR to ROILP suggested that GPR had a varying spillover effect on real oil prices in the BRICS economies, with Russia and India experiencing relatively stronger effects compared to the other countries. The spillover from EPU to ROILP showed that Brazil and China recorded significant increases in spillover effects, both respectively reached peaks of 54% and 61% suggesting their vulnerability to EPU and its impact on real oil prices during economic stress. The wavelet coherence largely revealed mixed directions of co-movement and heterogeneity between ROILP and EPU (GPR) across countries, indicating that the relationship between the variables was not consistent, varied over time and scale. Different from previous studies, the analysis focused on the direction of the spillover between oil prices and uncertainty measures, then estimated the numerical amount of the spillover to discover the variable that gave more shock than it received. While the wavelet coherence identified variations in coherence, across the time and frequency domain, the adoption of the rolling-window technique enabled the capture of the influence of arbitrary multiple regime changes in the spillover. The findings had some policy implications.

Quantifying the financial value of building decarbonization technology under uncertainty: Integrating energy modeling and investment analysis

The built environment accounts for approximately 40% of global emissions. The evaluation of returns linked to investments in building decarbonization is impeded when confronted with uncertainties surrounding future energy prices, building emission regulations, and real estate market conditions. This paper develops an integrated framework that combines energy modeling with investment analysis to support the adoption of decarbonizing technologies under a context of uncertainty. The integrated framework includes projections of costs and benefits associated with the adoption of technology (i.e., changes in rental cashflows, vacancy rates, maintenance and energy costs) under a wide range of potential pathways of the energy markets and regulatory environment, and local real estate market demand for green buildings using Monte Carlo simulations. Additionally, this paper develops the approach to investigate the potential value of flexibility in designing for building electrification (i.e., the ability to fully electrify gas heating systems at a later year). We illustrate the use of this approach in a case study of a new office building in New York City, faced with design choices between natural gas and electrified heating systems. We consider 10,000 scenarios changing future greenhouse emission penalties, investments in the local electric grid, local real estate market conditions and energy prices. In approximately 60 percent of simulations, the most profitable decision for the building owner is to adopt a natural gas-powered heating system. However, adopting a building design that provides a building the flexibility to fully electrify at a later date is more profitable than a natural gas-heating building in 96 percent of scenarios. A sensitivity analysis shows that the most influential parameter in the valuation of a design option is the size of the green premiums associated with improved energy efficiency.

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

AbstractAs the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

On foreign drivers of emerging markets fluctuations

We quantify the fraction of business cycles fluctuations in emerging markets explained by the combined dynamics of a set of common external drivers. By means of specific constraints on a state-space model fitted to a panel of real, financial and commodity price data for twelve emerging economies, we single out financial, commodity and growth common factors driving jointly a third of GDP fluctuations. Our partition of foreign shocks faced by emerging markets offers new insights into the primitive sources of emerging market cycles and feeds policy discussions in terms of the effects of global financial cycles versus commodity prices. Even when we control for the presence of financial cycles, fluctuations in commodity prices play a predominant role on the performance of emerging markets.

Prices of Orphan Drugs in Four Western European Countries Before and After Market Exclusivity Expiry: A Cross-Country Comparison of List Prices and Purchase Prices.

Increasing pharmaceutical expenditure challenges the sustainability and accessibility of healthcare systems across Europe. Confidentiality restraints hinder assessment of actual prices of Orphan Medicinal Products (OMPs). Hence, we assessed the real prices of brand-name OMPs around market exclusivity expiry (MEE). We aimed to explore developments in published list prices (LPs) and confidential hospital purchase prices (PPs) of brand-name OMPs relative to their market exclusivity status in Western European countries with similar GDPs. We analyzed LPs and PPs of 13 selected OMPs purchased by university hospitals in Western European countries between 2000 and 2020. For confidentially reasons, proportions were used, with the Dutch LPs of the selected OMPs at the year of MEE serving as reference values. PPs included pre-purchase discounts. Rebates were not considered. Data were analyzed from hospitals in Denmark (DK) (n=1), France (FR) (n=1), Germany (DE) (n=2), and the Netherlands (NL) (n=1). Average LPs and PPs of included OMPs dropped gradually but limited over time, with no explicit price drop after MEE. LP levels differed more per country than PP levels: LP range before MEE was 164% (DE)-101% (FR) and after MEE was 135% (DE)-82% (FR); PP range before MEE was 150% (DE)-102% (FR) and after MEE was 107% (DE)-80% (FR). Overall differences between LPs and PPs were <3% in all countries, except for Denmark. No evident price drops of included brand-name OMPs were observed around MEE and differences in purchase prices are modest in the selected Western European countries. Results were not subject to significance testing. More robust data are needed to strengthen negotiations with suppliers.

Battery Degradation Impact on Long-Term Benefits for Hybrid Farms in Overlapping Markets

Participation in the electricity market requires making commitments without knowing the real generation or electricity prices. This is problematic for renewable generators due to their fluctuating output. Battery energy storage systems (BESSs) integrated with renewable sources in a hybrid farm (HF) can alleviate imbalances and increase power system flexibility. However, the impact of battery degradation on long-term profitability must be taken into account when choosing the correct market participation strategy. This study evaluates the state-of-the-art on energy management systems (EMS) for HFs participating in day-ahead and intraday markets, incorporating both BESSs’ calendar and cycling degradation. Results suggest that efforts to attain additional profits in intraday markets can be detrimental, especially when the degradation effect is considered in the analysis. A new market participation strategy is proposed that aims to address the limitations of market overlapping and forecasting errors. The results demonstrate that the proposed method can enhance long-term benefits while also reducing battery degradation.

The electric shock: Causes and consequences of electricity prices in the United Kingdom

This study explores the primary causes of wholesale electricity price fluctuations in the United Kingdom (UK). Using the structural vector autoregression (SVAR) model that identifies both supply-side and demand-side shocks, and monthly data for the period from January 1996 to May 2022, the findings show that the impact of electricity price increases on the real electricity price is dependent on the underlying cause of the price increase. Electricity price movements in the most recent period of global hardships from 2020 to 2022, including the Covid-19 pandemic and the Russian invasion of Ukraine, are further examined, which were widely discussed to have affected the global energy segment. The findings show that although shocks to natural gas had a part to play in the recent price increase of electricity, some major contributing factors remain unique to the UK. Finally, the findings put into perspective and question the effectiveness of the energy cap considering the large contribution of renewable power generation.

An IMEX-based approach for the pricing of equity warrants under fractional Brownian motion models

In this paper, the pricing of equity warrants under a class of fractional Brownian motion models is investigated numerically. By establishing a new nonlinear partial differential equation (PDE) system governing the price in terms of the observable stock price, we solve the pricing system effectively by a robust implicit-explicit numerical method. This is fundamentally different from the documented methods, which first solve the price with respect to the firm value analytically, by assuming that the volatility of the firm is constant, and then compute the price with respect to the stock price and estimate the firm volatility numerically. It is shown that the proposed method is stable in the maximum-norm sense. Furthermore, a sharp theoretical error estimate for the current method is provided, which is also verified numerically. Numerical examples suggest that the current method is efficient and can produce results that are, overall, closer to real market prices than other existing approaches. A great advantage of the current method is that it can be extended easily to price equity warrants under other complicated models. doi: 10.1017/S1446181123000159

The Evolution of Market Power in the U.S. Automobile Industry

Abstract We construct measures of industry performance and welfare in the U.S. automobile market from 1980 to 2018. We estimate a demand model using product-level data on market shares, prices, and attributes, and consumer-level data on demographics, purchases, and stated second choices. We estimate marginal costs assuming Nash-Bertrand pricing. We relate trends in consumer welfare and markups to trends in market structure and the composition of products. Although real prices rose, we find that markups decreased substantially, and the fraction of total surplus accruing to consumers increased. Consumer welfare increased over time due to improved product quality and improved production technology.