Risk Analysis in Investment Appraisal

Savvides, Savvakis C.

doi:10.2139/ssrn.265905

Cited by 14 publications

(15 citation statements)

References 3 publications

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“…It is also the most commonly used distribution for modeling expert opinion (Vose 2000). All possible correlations between input variables have been built in the techno-economic model, so that the remaining uncertain variables can be considered as independent and the construction of correlated variables in the Monte Carlo simulations is not appropriate (Savvides 1994). For instance, it is reasonable to expect some negative covariance between investment costs (I) and processed quantity (Φ) due to expected economies of scale.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Combining Monte Carlo simulations and experimental design for incorporating risk and uncertainty in investment decisions for cleantech: a fast pyrolysis case study

Kuppens

Rafiaani

Vanreppelen

et al. 2018

Clean Techn Environ Policy

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The value of phytoextracting crops (plants cultivated for soil remediation) depends on the profitability of the sequential investment in a conversion technology aimed at the economic valorization of the plants. However, the net present value (NPV) of an investment in such an innovative technology is risky due to technical and economic uncertainties. Therefore, decision makers want to dispose of information about the probability of a positive NPV, the largest possible loss, and the crucial economic and technical parameters influencing the NPV. This paper maps the total uncertainty in the NPV of an investment in fast pyrolysis for the production of combined heat and power (CHP) from willow cultivated for phytoextraction in the Belgian Campine. The probability of a positive NPV has been calculated by performing Monte Carlo simulations. Information about possible losses has been provided by means of experimental design. Both methods are then combined in order to identify the key economic and technical parameters influencing the project's profitability. It appears that the case study has a chance of 87 % of generating a positive NPV with an expected value of 3 million euro (MEUR), whilst worst case scenarios predict possible losses of 7 MEUR. The amount of arable land, the biomass yield, the purchase price of the crop, the policy support and the product yield of fast pyrolysis are identified as the most influential parameters. It is concluded that both methods, i.e. Monte Carlo simulations and experimental design, provide decision makers with complementary information with regard to economic risk.

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Section: Monte Carlo Simulationsmentioning

confidence: 99%

Combining Monte Carlo simulations and experimental design for incorporating risk and uncertainty in investment decisions for cleantech: a fast pyrolysis case study

Kuppens

Rafiaani

Vanreppelen

et al. 2018

Clean Techn Environ Policy

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“…Sensitivity analysis is widely used by the forestry industry, probably due to its simplicity and easy interpretability of results. Probability analysis (or stochastic analysis) (F) is executed to estimate the probability distribution of the output values, based on the probability distributions of the inputs . Although probability analysis is also identified as risk analysis or uncertainty analysis in the literature, we believe that risk analysis is a broader concept that includes sensitivity and probability analysis methods, as shown in Fig. .…”

Section: Literature Reviewmentioning

confidence: 99%

Risk management consideration in the bioeconomy

Assis

Gonzalez

Kelley

et al. 2017

Biofuels Bioprod Bioref

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In investing in a new venture, companies aim to increase their competitiveness and generate value in scenarios where volatile markets, geopolitical instabilities, and disruptive technologies create uncertainty and risk. The biobased industry poses additional challenges as it competes in a mature, highly efficient market, dominated by petroleum‐based companies, and faces significant feedstock availability and variability constraints, limited technological data, and uncertain market conditions for newly developed products. Thus, decision‐making strategies and processes for these investment projects must consider solid risk estimation and mitigation measures. Focusing on the biobased industrial sector, this paper critically reviews state‐of‐the‐art probabilistic and deterministic methodologies for assessing financial risk; discusses how a complete risk analysis should be performed; and addresses risk management, listing major risks and possible mitigation strategies. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd

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“…Monte Carlo methods were initially developed for the physics and chemistry communities. The advantage to deal with probabilistic events, such as Brownian motion, was a major factor in using these methods in various areas of finance, such as corporate finance [Savvides 1994], portfolio optimization [Detemple et al 2003], and risk analysis [Evans, Olson 1998]. These methods are widely used in dealing with quantities that have a high degree of uncertainty, and where traditional analytical ways might fail.…”

mentioning

confidence: 99%

Monte Carlo simulation approach to calculate Value at Risk: application to WIG20 and mWIG40

Pasieczna¹

2019

Financial Sciences

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This paper reports our estimates of the Value at Risk using Monte Carlo simulations for which we developed a computer program. Our approach involves obtaining Monte Carlo parameters by fitting real historical data of different periods to probability distributions. We applied the algorithm to the WIG20 and mWIG40 stock indices, and performed simulations for the Value at Risk at 95% and 99% confidence intervals over six estimation periods ranging from 1 trading day to 250 trading days. This approach was evaluated using the percentage failures and the Kupiec Proportion of Failures test. Our results indicate that this method is highly influenced by the choice of past historical and estimation period lengths considered. Overall, we observed that the Monte Carlo computational scheme is a reliable method for quantifying VaR when parametrized well.

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Risk Analysis in Investment Appraisal

Cited by 14 publications

References 3 publications

Combining Monte Carlo simulations and experimental design for incorporating risk and uncertainty in investment decisions for cleantech: a fast pyrolysis case study

Combining Monte Carlo simulations and experimental design for incorporating risk and uncertainty in investment decisions for cleantech: a fast pyrolysis case study

Risk management consideration in the bioeconomy

Monte Carlo simulation approach to calculate Value at Risk: application to WIG20 and mWIG40

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