scholarly journals Incorporating stand level risk management options into forest decision support systems

2018 ◽  
Vol 26 (3) ◽  
pp. e013
Author(s):  
Kyle Eyvindson ◽  
Rami Saad ◽  
Ljusk Ola Eriksson
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Decision Support ◽  
Value At Risk ◽  
Net Present Value ◽  
Conditional Value At Risk ◽  
Management Options ◽  
Monte Carlo Approach ◽  
Small Forest

Aim of study: To examine methods of incorporating risk and uncertainty to stand level forest decisions.Area of study: A case study examines a small forest holding from Jönköping, Sweden.Material and methods: We incorporate empirically estimated uncertainty into the simulation through a Monte Carlo approach when simulating the forest stands for the next 100 years. For the iterations of the Monte Carlo approach, errors were incorporated into the input data which was simulated according to the Heureka decision support system. Both the Value at Risk and the Conditional Value at Risk of the net present value are evaluated for each simulated stand.Main results: Visual representation of the errors can be used to highlight which decision would be most beneficial dependent on the decision maker’s opinion of the forest inventory results. At a stand level, risk preferences can be rather easily incorporated into the current forest decision support software.Research highlights: Forest management operates under uncertainty and risk. Methods are available to describe this risk in an understandable fashion for the decision maker.

A Net Present Value-at-Risk Objective Function for Uncertainty Mitigation in the Design of Hybrid Ground-Coupled Heat Pump Systems

2020 ◽  
Author(s):  
Bernard Dusseault ◽  
Philippe Pasquier
Keyword(s):  
At Risk ◽  
Standard Deviation ◽  
Objective Function ◽  
Heat Pump ◽  
Value At Risk ◽  
Net Present Value ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Present Value ◽  
Ground Coupled Heat Pump

<p>The design by optimization of hybrid ground-coupled heat pump (Hy-GCHP) systems is a complex process that involves dozens of parameters, some of which cannot be known with absolute certainty. Therefore, designers face the possibility of under or oversizing Hy-GCHP systems as a result of those uncertainties. Of course, both situations are undesirable, either raising upfront costs or operating costs. The most common way designers try to evaluate their impacts and prepare the designs against unforeseen conditions is to use sensitivity analyses, an operation that can only be done after the sizing.</p><p>Traditional stochastic methods, like Markov chain Monte Carlo, can handle uncertainties during the sizing, but come at a high computational price paid for in millions of simulations. Considering that individual simulation of Hy-GCHP system operation during 10 or 20 years can range between seconds and minutes, millions of simulations are therefore not a realistic approach for design under uncertainty. Alternative stochastic design methodologies are exploited in other fields with great success that do not require nearly as many simulations. This is the case for the conditional-value-at-risk (CVaR) in the financial sector and for the net present value-at-risk (NPVaR) in civil engineering. Both financial indicators are used as objective functions in their respective fields to consider uncertainties. To do that, they involve distributions of uncertain parameters but only focus on the tail of distributions. This results in quicker optimizations but also in more conservative designs. This way, they remain profitable even when faced with extremely unfavorable conditions.</p><p>In this work, we adapt the NPVaR to make the sizing of Hy-GCHP systems under uncertainties viable. The mixed-integer non-linear optimization algorithm used jointly with the NPVaR, the Hy-GCHP simulation algorithm and the g-function assessment methods used are presented broadly, all of which are validated in this work or in referenced publications. The way in which the NPVaR is implemented is discussed, more specifically how computation time can be further reduced using a clever implementation without sacrificing its conservative property. The implications of using the NPVaR over a deterministic algorithm are investigated during a case study that revolves around the design of an Hy-GCHP system in the heating-dominated environment of Montreal (Canada). Our results show that over 1000 experiments, a design sized using the NPVaR has an average return on investment of 126,829 $ with a standard deviation of 18,499 $ while a design sized with a deterministic objective function yields 137,548 $ on average with a standard deviation of 33,150 $. Furthermore, the worst returns in both cases are respectively 35,229 $ and -32,151 $. This shows that, although slightly less profitable on average, the NPVaR is a better objective function when the concern is about avoiding losses rather than making a huge profit.</p><p>In that regard, since HVAC is usually considered a commodity rather than an investment, we believe that a more financially stable and predictable objective function is a welcome addition in the toolbox of engineers and professionals alike that deal with the design of expensive systems such as Hy-GCHP.</p>

scholarly journals Implementing the conditional value at risk approach for even-flow forest management planning

2016 ◽  
Vol 46 (5) ◽  
pp. 637-644 ◽  
Author(s):  
Kyle Eyvindson ◽  
Zhuo Cheng
Keyword(s):  
At Risk ◽  
Forest Management ◽  
Value At Risk ◽  
Net Present Value ◽  
Growth Models ◽  
Management Plan ◽  
Conditional Value At Risk ◽  
Forest Owner ◽  
Management Planning ◽  
Forest Management Planning

Deciding on a plan of action for a forest holding involves a significant amount of uncertainty. As forest planning involves the use and extraction of resources, uncertainty lies in both the future development of the forest (biological uncertainty) and the development of the market for forest-based products (economic uncertainty). Additionally, natural hazards can be a source of unexpected losses. In traditional forest management planning, the most common way to deal with uncertainty is to ignore it. Growth models are used that are assumed to be correct, and timber prices are assumed to be held constant. By ignoring the fact that these models provide only one representation of what may happen, the forest owner may get an overly optimistic (or pessimistic) view of the potential value of the forest holding. Through a stochastic programming formulation, these uncertainties can be modelled directly into the optimization formulation, and a management plan can be created that incorporates the risk preferences of the decisionmaker. This is highlighted through an example that maximizes the net present value of the holding while minimizing the conditional value at risk of obtaining a stated even flow of income.

scholarly journals Estimation of the Portfolio Risk from Conditional Value at Risk Using Monte Carlo Simulation

2021 ◽  
Vol 17 (3) ◽  
pp. 370-380
Author(s):  
Ervin Indarwati ◽  
Rosita Kusumawati
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Estimation ◽  
Conditional Value At Risk ◽  
Portfolio Risk ◽  
Portfolio Returns

Portfolio risk shows the large deviations in portfolio returns from expected portfolio returns. Value at Risk (VaR) is one method for determining the maximum risk of loss of a portfolio or an asset based on a certain probability and time. There are three methods to estimate VaR, namely variance-covariance, historical, and Monte Carlo simulations. One disadvantage of VaR is that it is incoherent because it does not have sub-additive properties. Conditional Value at Risk (CVaR) is a coherent or related risk measure and has a sub-additive nature which indicates that the loss on the portfolio is smaller or equal to the amount of loss of each asset. CVaR can provide loss information above the maximum loss. Estimating portfolio risk from the CVaR value using Monte Carlo simulation and its application to PT. Bank Negara Indonesia (Persero) Tbk (BBNI.JK) and PT. Bank Tabungan Negara (Persero) Tbk (BBTN.JK) will be discussed in this study.  The  daily  closing  price  of  each  BBNI  and BBTN share from 6 January 2019 to 30 December 2019 is used to measure the CVaR of the two banks' stock portfolios with this Monte Carlo simulation. The steps taken are determining the return value of assets, testing the normality of return of assets, looking for risk measures of returning assets that form a normally distributed portfolio, simulate the return of assets with monte carlo, calculate portfolio weights, looking for returns portfolio, calculate the quartile of portfolio return as a VaR value, and calculate the average loss above the VaR value as a CVaR value. The results of portfolio risk estimation of the value of CVaR using Monte Carlo simulation on PT. Bank Negara Indonesia (Persero) Tbk and PT. Bank Tabungan Negara (Persero) Tbk at a confidence level of 90%, 95%, and 99% is 5.82%, 6.39%, and 7.1% with a standard error of 0.58%, 0.59%, and 0.59%. If the initial funds that will be invested in this portfolio are illustrated at Rp 100,000,000, it can be interpreted that the maximum possible risk that investors will receive in the future will not exceed Rp 5,820,000, Rp 6,390,000 and Rp 7,100,000 at the significant level 90%, 95%, and 99%

scholarly journals Análise de um modelo inteligente de contratação de energia elétrica no curto prazo para distribuidoras

2012 ◽  
Vol 23 (6) ◽  
pp. 711-725
Author(s):  
Juan G. Lazo Lazo ◽  
Delberis Araujo Lima ◽  
Karla Figueiredo
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Para Determinar

Neste artigo é apresentada uma nova abordagem de um modelo inteligente de otimização sob incerteza para determinar a contratação de energia elétrica no curto prazo (referente aos leilões A-1 e Ajuste) para distribuidoras de energia elétrica. Nesse modelo estão incluídas todas as regras de contratação e repasse à tarifa, definidas pela ANEEL, para as distribuidoras. O processo de otimização utiliza um algoritmo genético, e busca minimizar o custo associado à contratação de energia elétrica, as penalidades por subcontratação e o custo da liquidação (compra/venda) desta energia ao PLD (Preço de Liquidação das Diferenças). A contratação ótima é calculada considerando vários cenários de consumo, obtidos a partir de simulação Monte Carlo, para um período de cinco anos de análise. As decisões de contratação do modelo são tomadas nos dois primeiros anos desse período. A avaliação dos resultados do sistema é feita considerando uma combinação entre o Valor Esperado (VE) da distribuição de custos e o CVaR (Conditional Value at Risk), para os diferentes cenários de consumo. O modelo também usa o PLD_robusto, que busca minimizar a exposição da distribuidora ao PLD. Para ilustrar os resultados do modelo proposto é apresentado um estudo de caso baseado em dados reais. Os resultados obtidos são comparados com alguns resultados de contratação que não consideram o modelo de otimização proposto. Essa comparação é feita para se verificar o quanto o método proposto pode ser melhor que soluções baseadas apenas em análises intuitivas. Além disso, estudos adicionais são apresentados considerando os mecanismos de compensação de sobras e déficits, notadamente MCSD4% e MCSD_Ex-post, previstos na legislação vigente do setor elétrico para minimizar os riscos associados à contratação de energia para as distribuidoras.

scholarly journals Pengukuran Conditional Value At Risk (CVAR) Pada Aset Tunggal dengan Metode Simulasi Monte Carlo

2021 ◽  
Vol 18 (1) ◽  
pp. 130-135
Author(s):  
T N Ondja ◽  
S Musdalifah ◽  
D Lusiyanti ◽  
Andri
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Central Asia ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Monte Carlo Data

Conditional value at risk (CVaR) merupakan suatu ukuran risiko yang memperhitungkan kerugian melebihi tingkat Value at risk (VaR). Tujuan dalam penelitian ini adalah mendapatkan hasil pengukuran Conditional Value at Risk (CVaR) pada aset tunggal dengan menggunakan metode simulasi Monte Carlo. Data yang digunakan dalam penelitian ini ialah harga penutupan saham harian PT. Bank Central Asia Tbk (BBCA.JK) dengan tingkat kepercayaan . Dari hasil perhitungan CVaR pada tingkat kepercayaan 99%, menghasilkan nilai CVaR sebesar . Hal ini dapat diartikan bahwa ada keyakinan sebesar  kerugian yang mungkin akan diderita investor tidak akan melebihi  dalam jangka waktu satu hari setelah tanggal 30 September 2020, atau dengan kata lain ada kemungkinan sebesar  bahwa kerugian investasi pada periode tersebut sebesar  atau lebih dalam jangka waktu satu hari dengan dana awal yang diinvestasikan sebesar Rp

Value-at-risk time scaling: a Monte Carlo approach

2016 ◽  
Vol 10 (1) ◽  
pp. 47-57
Author(s):  
Moepa Malataliana ◽  
Michael Rigotard
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Value At Risk ◽  
Monte Carlo Approach ◽  
Time Scaling
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