A new test on the conditional capital asset pricing model

Testing the validity of the conditional capital asset pricing model （CAPM） is a puzzle in the finance literature. Lewellen and Nagel[14] find that the variation in betas and in the equity premium would have to be implausibly large to explain important asset-pricing anomalies. Unfortunately, they do not provide a rigorous test statistic. Based on a simulation study, the method proposed in Lewellen and Nagel[14] tends to reject the null too frequently. We develop a new test procedure and derive its limiting distribution under the null hypothesis. Also, we provide a Bootstrap approach to the testing procedure to gain a good finite sample performance. Both simulations and empirical studies show that our test is necessary for making correct inferences with the conditional CAPM.

Water Resource Pricing Study Based on Water Quality Fuzzy Evaluation: A Case Study of Hefei City

Price plays an important role in water resources management. The price of water resources can also be considered as a “water resource tax” which reflects the value and opportunity cost of water, and people will pay for the right to use water. Currently, the water resource fees’ effect of regulating resource differential revenues is not manifest and it’s not enough to reflect the principle of paid use of resources as well as regulating resources differential revenues. Due to the ambiguity and complexity of water resources price, this paper uses methods relating to fuzzy mathematics for modeling and processing. The study had a comprehensive consideration of five factors including water quality, water resources per capita, household consumption level, per capita GNP, population or population density to evaluate the water resource price.

Pricing Model of Multiattribute Derivatives Based on Mixed Process

By Analyzing the behavior and character of derivative security, the authors established a pricing model of multiattribute derivative security whose underlying asset pricing process is a mixed process, and obtained a new model for option pricing of multiattribute derivatives based on mixed process, and improved some original results.

Dynamic Pricing of Tropical Fruits in Hainan Based on Internet of Things Technology

In recent years,the Internet of Things(IoT)technology has been widely used in the production and sales of tropical fruits,with strong practicability and wide application prospects.The tropical fruit dynamic pricing model based on the IoT technology can promote the healthy development of the tropical fruit industry in Hainan and ensure the income of fruit farmers.Based on IoT technology,the quality grade of tropical fruits in Hainan is obtained.According to the dynamic pricing strategy of revenue management,a dynamic pricing model based on the quality of tropical fruits and a dynamic pricing model based on consumer segmentation are established to study the dynamic pricing problem under the condition of maximum profit for tropical fruit sellers.The research results show that for different fruit quality and consumer groups,different pricing models are required for pricing,in order to get the maximum profit from tropical fruit sales.Sellers must flexibly adopt different dynamic pricing models to price tropical fruits to enhance the competitiveness of the tropical fruit industry.

Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market

Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. The objectives of the study are: 1) to estimate the relationship between wild Sea buckthorn (SB) price and Supply, Demand, while some other factors of crude oil price and exchange rate by using simultaneous Supply-Demand and Price system equation and Vector Error Correction Method (VECM);2) to forecast the short-term and long-term SB price;3) to compare and evaluate the price forecasting models. Firstly, the data was analyzed by Ferris and Engle-Granger’s procedure;secondly, both price forecasting methodologies were tested by Pindyck-Rubinfeld and Makridakis’s procedure. The result shows that the VECM model is more efficient using yearly data;a short-term price forecast decreases, and a long-term price forecast is predicted to increase the Mongolian Sea buckthorn market.

Cost Management Strategy of Highway Engineering Construction Stage Using the List Pricing Model

Highway engineering requires higher investment and requires a long time of management compared to other construction projects.There are many factors that affect the project cost during the engineering construction stage of a highway.The effective development of cost management in the construction phase of highway engineering under the list pricing model can avoid unnecessary waste and help control the cost of highway engineering.However,there are still some problems in the development of cost management in the construction phase of highway engineering,which will affect the role of the list-based pricing mode in cost management.This paper explores and analyzes the advantages of the list pricing model and the problems existing in the cost management of the highway engineering construction stage under the list pricing model,and proposes effective management strategies to improve cost management of the highway engineering construction stage.

Research on Financial Lease Pricing Model

Financial leasing is a financial innovation product with leasing and financing functions.The research on the theory of financial leasing and risk pricing methods should be highly valued.Rent is set based on the total revenue of the lessor and the total cost of the lessee.The factors that affect pricing include project costs,security deposits,fees,lease terms,revenue,interest rates,etc.Using the principle of net present value to elaborate the components of financial leases and constructing a financial lease pricing model from the perspective of maximizing the profit and interests of the lessor,an empirical analysis of the model was carried out using an actual case,thus concluding that the model is effective.

A review of refinery complexity applications

Refinery complexity quantifies the sophistication and capital intensity of a refinery and has found widespread application in facility classification, cost estimation, sales price models, and other uses. Despite the ubiquity and widespread use of refining complexity, however, surprisingly little material has been written on its applications. The pur- pose of this review is to describe the primary applications of refinery complexity and some recent extensions. A secondary purpose of this review is to provide a framework that unifies complexity applications and suggests avenues for future research. Examples illustrate the applications considered.

Does the EVA valuation model explain the market value of equity better under changing required return than constant required return?

Through the Economic-Value-Added(EVA)valuation model,the expected market value of equity can be determined by adding the book value of equity with the present value of expected EVAs under the assumption of constant required return and constant return on equity.The equation of EVA valuation model has taken its shape under the assumption of constant required return and constant return on equity.However,a large body of empirical evidence indicates that required rate of return never remain constant.The EVA-valuation model formulated under constant required return cannot be implemented under the scenario of changing required return.In this study,we explored whether the EVA valuation model could be implemented under changing required return by making any changes in the model and found that it could be implemented under the scenario of changing required return by replacing the book value of the equity of the existing model with the present value of required earnings or normal market earnings.We further examined whether the explanatory ability of the EVA valuation model under the assumption of changing required return is better than that of the valuation model under the assumption of constant required return.Relative information content analyses were conducted by considering sample of the intrinsic value of equities determined by valuation models and the market value of equities of 69 large-cap,88 mid-cap,and 79 small-cap companies.The results showed that the EVA-based valuation model with changing normal market return outperformed the EVA-based valuation model with constant required return.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.

Empirical analysis on risk of security investment

The paper analyzes the theory and application of Markowitz Mean-Variance Model and CAPM model. Firstly, it explains the development process and standpoints of two models and deduces the whole process in detail. Then 30 stocks are choosen from Shangzheng 50 stocks and are testified whether the prices of Shanghai stocks conform to the two models. With the technique of time series and panel data analysis, the research on the stock risk and effective portfolio by ORIGIN and MATLAB software is conducted. The result shows that Shanghai stock market conforms to Markowitz Mean-Variance Model to a certain extent and can give investors reliable suggestion to gain higher return, but there is no positive relation between system risk and profit ratio and CAPM doesn＇t function well in China＇s security market.

Discount Rate of China’s New Energy Power Industry

Under the dual pressures of energy crisis and environmental pollution,China’s new energy power industry has become a focal point for environmental management and requires greater investment.In this context,as a significant input of investment projects,discount rate requires a well-calibrated evaluation because new energy power investment projects are highly capital intensive.The main objective of this paper is to evaluate the discount rate of China’s new energy power industry.First,we use Moving Average to correct the parameters of capital asset pricing model(CAPM)and weighted average cost of capital,which extends the literature on the avoidance of CAPM noise information problem.Second,we study the industry-level annual discount rates of mainly China’s new energy power industries,including hydropower,nuclear power,wind power,and photovoltaic power industries for the period of 2014-2019.The results show that discount rates in China’s new energy power industries evolved differently between the years of 2014-2019 with average annual discount rates being 7.56%,5.83%,5.60%,and 8.64%,for the hydropower,nuclear power,wind power,and photovoltaic power industries,respectively.In 2019,the four annual discount rates were highest for the photovoltaic power industry(8.66%),followed by hydropower(7.17%),wind power(5.72%),and nuclear power industry(5.26%).Forecasting to 2020 from the 2019 evaluation base period,the discount rates are 6.37%,5.00%,6.57%,and 9.05%for the photovoltaic power,hydropower,wind power,and nuclear power industries,respectively.Under the different capital structures,their forecasts for the photovoltaic power,hydropower,wind power,and nuclear power industries in 2020 are,respectively,within[4.35%,9.24%],[3.92%,7.10%],[4.58%,10.40%],[5.46%,14.81%].We also discussed more details on capital structure and forecast period of discount rates for China’s new energy power industries.Our analysis shows that it is necessary to establish a new energy power industry database and steadily promote the implementation of policies.

An Analysis of the Factors that Affect Collective Construction Land Transfer Price: A Case Study of Yichang City

The transfer of the rural collective construction land is increasingly accelerating,and the factors affecting transfer price are manifold. In this paper,the research area is Yichang,and we establish hedonic price model to explore and analyze the factors which affect the collective construction land transfer price. The simulation results show that in geographical factors,the higher degree of prosperity,road accessibility and soundness of infrastructure will result in higher collective construction land transfer price; in economic factors,the higher farmers' per capita net income and added value of the village's tertiary industry will lead to higher collective construction land transfer price; in ownership factors,the integrity of usufruct,disposition and possession has increasingly significant impact on collective construction land transfer price. In the process of establishing rural collective construction land circulation market,the government should gradually improve conditions of collective construction land; strengthen the construction of the rural economy,improve the economic attribute of the collective construction land; establish and improve China's rural collective construction land-related laws and regulations,make the rural collective construction land use rights clear,and give the whole rights of occupation,use,earning and disposition.

Inner-City Neighbourhood Changes Predicted from House Prices in Windsor, Ontario, since the Early- or Mid-1980s

Changes in prices of homes are hypothesized as correlated with the times of their sale and resale and the attributes of their dwelling unit and neighbourhood and those of neighbouring homes. They may also be correlated with the occurrences of events inside the neighbourhoods caused by the activities of individuals and organizations outside the neighbourhoods, such as whether the local economy is in a recession or has a high unemployment rate. Calibrated hybrid housing price models predict precipitous decreases in house prices of approximately 2900 sold and resold homes in two inner-city neighbourhoods in Windsor, Ontario, during those events since 1981 or 1986. Overall modest predicted percentage increases in houses’ prices during more than 30 years therefore subsumed periods of inner-city neighbourhood deterioration in dispersed locations of unimproved and disimproved homes. Compensatory predictions however are of increasing prices for minorities of homes with improvements to several attributes of the dwelling unit and neighbourhood.

The Influence of Built Environment on the Spatial Distribution of Housing Price:Based on Multiple Big Data and Hedonic Model

In recent years,more and more researches focus on the self characteristics and spatial location of housing,and explore the influencing factors of urban housing price from the micro perspective.As representative of big cities,spatial distribution pattern of housing price in national central cities has attracted much attention.In order to return the spatial distribution pattern of housing price to the research on influencing factors of housing price,the reasons behind the spatial distribution pattern of housing price in three national central cities:Beijing,Wuhan and Chongqing are explored.The results show that①urban housing price is affected by many factors.Due to different social and economic conditions in each city,there are differences in the influence direction of the proximity to expressways,city squares,universities and living facilities,characteristics of companies and enterprises on Beijing,Wuhan and Chongqing.②Various factors have different value-added effects on housing price in different cities.The location of ring line in Beijing and Wuhan has the greatest increase effect on housing price,while metro station of Chongqing has the greatest increase effect on housing price.

Multi-Energy Simulation of a Smart Grid with Optimal Local Demand and Supply Management

A simulation approach of a smart grid by cooperative bargaining is presented in this paper. Each participant of the smart grid determines its optimal schedule to meet its power and heating demand at minimal costs employing solar panels, fuel cells and batteries. This is done by solving a quadratic optimisation problem which takes the energy prices and the available devices into account. The energy prices are related to the demand and supply in the smart grid, so that a lower demand yields lower prices. The cooperative bargaining game is used to tune the participants’ optimal solution to obtain a Nash equilibrium. The computed solutions of the participants are validated against the capacities and structure of the smart grid by solving a multi-commodity flow problem. The presented model features multiple types of energy, so that they may be substituted to meet the participants’ demand. Furthermore, the participants may also act as supplier and not only as consumer, which allows decentralised generation of energy. The approach is validated in several experiments where effects like negative energy prices if generated energy exceeds the smart grid’s total demand and peak-shaving with even small-capacity batteries are exhibited.

A Pricing Model for Big Personal Data

Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market： How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model is put forward to implement positive rating and reverse pricing for Big Personal Data by investigating data attributes that affect data value, and analyzing how the value of data tuples varies with information entropy, weight value, data reference index, cost, and other factors. The model can be adjusted dynamically according to these parameters. With increases in data scale, reductions in its cost,and improvements in its quality, Big Personal Data users can thereby obtain greater benefits.

A Study of Pricing Deposits of Postal Savings in the Central Bank

This paper analyzes the advantage and disadvantage of cost-plus pricing modelin pricing deposits of postal savings in the central bank and inability of the model with thederegulation of interest rates. Based on price leadership model, this article establishes analternative method to price deposits of postal savings.

A Stock Pricing Model Based on Arithmetic Brown Motion

This paper presents a new stock pricing model based on arithmetic Brown motion. The model overcomes the shortcomings of Gordon model completely. With the model investors can estimate the stock value of surplus companies, deficit companies, zero increase companies and bankrupt companies in long term investment or in short term investment.

Option Pricing under the Double Exponential Jump-Diffusion Model with Stochastic Volatility and Interest Rate

This paper proposes an efficient option pricing model that incorporates stochastic interest rate(SIR),stochastic volatility(SV),and double exponential jump into the jump-diffusion settings.The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns,rare events,and an SIR.Using the model,we deduce the pricing characteristic function and pricing formula of a European option.Then,we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV.For verification purposes,we conduct time efficiency analysis,goodness of fit analysis,and jump/drift term analysis of the proposed model.In addition,we compare the pricing accuracy of the proposed model with those of the Black-Scholes and the Kou(2002)models.The empirical results show that the proposed option pricing model has high time efficiency,and the goodness of fit and pricing accuracy are significantly higher than those of the other two models.