Searching for an optimal rotation age for forest stand management under stochastic log prices

Yoshimoto, Atsushi; Shoji, Isao

doi:10.1016/s0377-2217(97)00040-4

Cited by 33 publications

(25 citation statements)

References 8 publications

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“…Haight and Holmes, 1991, Thomson, 1992, Yoshimoto and Shoji, 1998, Insley, 2002, Insley and Rollins, 2005, Yoshimoto, 2009. This is mostly because of the tractability of such stochastic processes and being common in Financial analysis.…”

Section: Discussionmentioning

confidence: 99%

“…The parameters are estimated by the local linearization method introduced by Ozaki (1985) and applied in forest economics by Yoshimoto and Shoji (1998Shoji ( , 2002. The basic idea of the method is that an original nonlinear stochastic differential equation is first converted to a stochastic differential equation having a constant diffusion term, and then the nonlinear drift of the derived stochastic differential equation is locally approximated by a linear function of state * 2 .…”

Section: Parameter Estimationmentioning

confidence: 99%

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Appropriate Stochastic Price Models for the Finnish Stumpage Market

Bayazidi

Yoshimoto²

2011

FORMATH

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Abstract:In searching for an appropriate time series model based on historical data we applied unit root tests and considered 12 different continuous-times stochastic models prices of six saw log and pulp wood products from two important species of Scots pine (Pinus.sylvesteris) and Norway spruce (Picea.abies), as well as, average softwood log prices for annual long run and shorter monthly time series in the Finnish wood market. For each product we conducted a comparative analysis between models on the basis of Akaike's Information criteria (AIC), the mean square error (MSE) of the models after one period of forecasting, and a likelihood ratio test. Parameter estimation was performed by quasi maximum likelihood estimation and local linearization method. The unit root tests results showed that while in the long run the price of softwood is trend stationary, in short run it shows non-stationary behaviour. Our results also showed that the level of effect of state the variable on volatility has a major role in refining a general model in to simpler models. The model with a general form of diffusion and no drift yields the highest AIC for most products, and the diffusion part of the model plays an important role in ranking by AIC, while in ranking by MSE for one period of forecasting, the drift part of models plays important role.

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Section: Discussionmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Appropriate Stochastic Price Models for the Finnish Stumpage Market

Bayazidi

Yoshimoto²

2011

FORMATH

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“…We would also like to direct interested readers to Yousefpour et al [9] who reviewed techniques for modeling climate change under the scheme of adaptive forest management. Other papers that addressed stochasticity or risk and uncertainty, in a DP framework include the following: Gunn [18], Díaz-balteiro and Rodriguez [19], Zhou and Buongiorno [20]; Ferreira et al [21] and Ferreira et al [22]; Yoshimoto and Shoji [23]; Yoshimoto [24]. In this study, we use deterministic solution techniques to describe a situation where the future state of a forest stand can be predicted exactly from knowledge of the present and all inputs and events are assumed to be known with certainty.…”

Section: Review Of Methodologiesmentioning

confidence: 99%

Stand-Level Forest Management Planning Approaches

2016

Self Cite

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Seeking an optimal operational regime under different management environments has been one of the main concerns of forest managers. Traditionally, the main operational regime includes planting density or regeneration scheme, thinning time/intensity, and optimal time to harvest over the given time horizon. Deterministic approaches to tackle this type of optimization problem with different controls have dominated the solution techniques in forestry literature. We present in this paper an overview of the methodologies used in stand-level optimization, in which we show the strengths and weaknesses of these methodologies as well as provide comments on the effectiveness of the methodology. We then propose a new dynamic programing approach for generalizing solution specification and techniques.

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“…The price uncertainty is likely to increase when using e.g., GBM [35][36][37] instead of GMR [32][33][34] as process for future price increments. The increasing price variation increases also the degree of yield value prediction uncertainty.…”

Section: Forest Property Dmentioning

confidence: 99%

“…A more advanced, and also complicated, approach is to try to predict future timber price development based on realized past price development, by which long-term trends can be depicted and factors causing price peaks identified. Such predictions can be carried out e.g., by using geometric mean-reverting (GMR, [32][33][34]) or geometric Brownian motion (GBM, [35][36][37]) price processes.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Forest Net Present Value Estimations

Holopainen

Mäkinen

Rasinmäki³

et al. 2010

Forests

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Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant OPEN ACCESSForests 2010, 1 178 in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

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Searching for an optimal rotation age for forest stand management under stochastic log prices

Cited by 33 publications

References 8 publications

Appropriate Stochastic Price Models for the Finnish Stumpage Market

Appropriate Stochastic Price Models for the Finnish Stumpage Market

Stand-Level Forest Management Planning Approaches

Uncertainty in Forest Net Present Value Estimations

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