Kriging a soil variable with a simple nonstationary variance model

2009 ◽  
Vol 14 (3) ◽  
pp. 301-321 ◽  
Author(s):  
R. M. Lark
Keyword(s):  
Variance Model ◽  
Nonstationary Variance

scholarly journals Market-based instruments for risk-averse farmers: rubber agroforest conservation in Jambi Province, Indonesia

2016 ◽  
Vol 22 (2) ◽  
pp. 133-155 ◽  
Author(s):  
Utkur Djanibekov ◽  
Grace B. Villamor
Keyword(s):  
Income Inequality ◽  
Risk Aversion ◽  
Real Options ◽  
Risk Averse ◽  
Variance Model ◽  
Income Uncertainty ◽  
Real Options Approach ◽  
Pigovian Taxes ◽  
Rubber Agroforest ◽  
Mean Variance

AbstractThis paper investigates the effectiveness of different market-based instruments (MBIs), such as eco-certification premiums, carbon payments, Pigovian taxes and their combination, to address the conversion of agroforests to monoculture systems and subsequent effects on incomes of risk-averse farmers under income uncertainty in Indonesia. For these, the authors develop a farm-level dynamic mean-variance model combined with a real options approach. Findings show that the conservation of agroforest is responsive to the risk-aversion level of farmers: the greater the level of risk aversion, the greater is the conserved area of agroforest. However, for all risk-averse farmers, additional incentives in the form of MBIs are still needed to prevent conversion of agroforest over the years, and only the combination of MBIs can achieve this target. Implementing fixed MBIs also contributes to stabilizing farmers’ incomes and reducing income risks. Consequently, the combined MBIs increase incomes and reduce income inequality between hardly and extremely risk-averse farmers.

Portfolio optimization with transaction costs: a two-period mean-variance model

2014 ◽  
Vol 233 (1) ◽  
pp. 135-156 ◽  
Author(s):  
Ying Hui Fu ◽  
Kien Ming Ng ◽  
Boray Huang ◽  
Huei Chuen Huang
Keyword(s):  
Transaction Costs ◽  
Portfolio Optimization ◽  
Variance Model ◽  
Mean Variance

Analytical Study of Effects of Truck Tire Pressure on Pavements with Measured Tire–Pavement Contact Stress Data

2005 ◽  
Vol 1919 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Randy B. Machemehl ◽  
Feng Wang ◽  
Jorge A. Prozzi
Keyword(s):  
Conventional Method ◽  
Contact Stress ◽  
Asphalt Concrete ◽  
Pressure Effects ◽  
Tire Pressure ◽  
Variance Model ◽  
Truck Tire ◽  
Concrete Layer ◽  
Pavement Structures ◽  
Asphalt Concrete Pavement

Truck tire inflation pressure plays an important role in the tire–pavement interaction process. As a conventional approximation method in many pavement studies, tire–pavement contact stress is frequently assumed to be uniformly distributed over a circular contact area and to be simply equal to the tire pressure. However, recent studies have demonstrated that the tire–pavement contact stress is far from uniformly distributed. Measured tire–pavement contact stress data were input into an elastic multilayer pavement analysis program to compute pavement immediate responses. Two asphalt concrete pavement structures, a thick pavement and a thin pavement, were investigated. Major pavement responses at locations in the pavement structures were computed with the measured tire–pavement contact stress data and were compared with the conventional method. The computation results showed that the conventional method tends to underestimate pavement responses at low tire pressures and to overestimate pavement responses at high tire pressures. A two-way analysis of variance model was used to compare the pavement responses to identify the effects of truck tire pressure on immediate pavement responses. Statistical analysis found that tire pressure was significantly related to tensile strains at the bottom of the asphalt concrete layer and stresses near the pavement surface for both the thick and thin pavement structures. However, tire pressure effects on vertical strain at the top of the subgrade were minor, especially in the thick pavement.

The Initial Return and Its Conditional Return Volatility: Evidence from the Chinese IPO Market

2014 ◽  
Vol 17 (04) ◽  
pp. 1450022 ◽  
Author(s):  
M. Monica Hussein ◽  
Zhong-Guo Zhou
Keyword(s):  
Time Series ◽  
Linear Functions ◽  
Return Volatility ◽  
Cross Sectional ◽  
Variance Model ◽  
Initial Return ◽  
Traditional Market ◽  
Mean And Variance ◽  
The Mean ◽  
Sectional Analysis

This paper investigates the monthly initial return and its conditional return volatility for Chinese IPOs. We find that the mean initial return (IR) and cross-sectional return volatility are highly auto- and cross-correlated, and time-varying. We propose a system of two simultaneous equations: a GARCH-in-mean (GARCH-M) process with an ARMA(1,1) adjustment in the residuals for the IR and an EGARCH process for the conditional return volatility, assuming that the IR and its conditional return volatility are linear functions of the same market, firm- and offer-specific characteristics. We find that the model captures both time-series and cross-sectional correlations at the mean and variance levels. Our findings suggest that the conditional return volatility affects the IR positively and significantly, in addition to the traditional market, firm- and offer-specific characteristics. IPOs with higher conditional return volatility, as a proxy for information asymmetry, tend to be underpriced more. The paper demonstrates the merit of using a conditional variance model, along with time series and cross-sectional analysis to price Chinese IPOs.

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