Optimal investment in learning-curve technologies

Seta, Marco Della; Gryglewicz, Sebastian; Kort, Peter M.

doi:10.1016/j.jedc.2012.03.014

Cited by 26 publications

(4 citation statements)

References 24 publications

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“…We consider a company that is risk‐neutral and discounted with a risk‐free rate

r

, which has the option to invest in EVs with uncertain future revenue streams. Once the traditional manufacturer decides to invest in EVs, the company immediately begins producing EVs, with an investment cost of

I = i K_{1}

, where

K_{1}

represents the capacity level and

i

represents the investment cost of one unit (Della Seta et al, 2012; Hagspiel et al, 2021). To be as realistic as possible, we assume that the production cost of EVs is larger than that of FVs and the company's production reached capacity

K_{1}

.…”

Section: Modelmentioning

confidence: 99%

When to invest in electric vehicles under dual credit policy: A real options approach

et al. 2023

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This research aims to investigate traditional vehicle manufacturers' green technology investment theory under dual credit policy from the perspective of real options, overcoming earlier investigations of this issue that considered it only from a stability or single uncertainty perspective. An analytical real options model was first provided for traditional automaker investment. Then we solved the analytical solution for the electric vehicle investment threshold based on the uncertainty of credit price and fuel vehicle market scenarios. The optimal electric vehicle investment timing is demonstrated using numerical simulation. Results show that (1) when the fuel vehicle market demand falls to a certain level, automakers will choose to make electric vehicle investments regardless of how the credit price changes in the market; (2) the effect of volatility on the investment threshold depends on the covariance or correlation coefficient; (3) the numerical simulation results revealed that the credit price drift rate, risk‐free rate, correlation parameters, and electric vehicle production cost all have a positive impact on the electric vehicle investment region, whereas the drift rate of fuel vehicle and electric vehicle production cost have a negative impact. These results can be used to make theoretical conclusions about electric vehicle investments.

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“…We consider a company that is risk‐neutral and discounted with a risk‐free rate

r

I = i K_{1}

, where

K_{1}

represents the capacity level and

i

K_{1}

.…”

Section: Modelmentioning

confidence: 99%

When to invest in electric vehicles under dual credit policy: A real options approach

et al. 2023

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“…Therefore, we formalize these considerations into a model for bank loans and account for learning spillovers by building on a recent strand of literature that incorporates learning effects into models of individual investors' technology investment decisions. In particular, Della Seta et al (2012) model a novel technology whose marginal costs decrease in cumulative output and find that optimal investment involves significant initial losses that are compensated by later-stage gains, making the technology particularly prone to downside risk. Their model is extended by Sarkar and Zhang (2020), who introduce the option of debt-financing, which leads to more and earlier investment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Note that we use capitalized L for aggregates of loan amounts across banks and lowercase l for loan amounts of individual banks.3 Our restrictions that c ≥ 0, c ′ < 0, c ′′ > 0 nest the most common functional forms for technological and financial learning curves in the literature(Della Seta et al, 2012;Egli et al, 2018;Samadi, 2018;Thompson, 2012).…”

mentioning

confidence: 99%

Mobilizing Credit for Clean Energy: De-Risking and Public Loan Provision Under Learning Spillovers

Waidelich,

Krug,

Steffen

2023

SSRN Journal

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“…On the other hand, if the realized demand is high, a large capacity will generate a high revenue. Since Bar-Ilan and Strange (1999) and Dangl (1999) develop real options models to study the capacity choice for an individual firm, a considerable number of papers have investigated the timing-capacity optimization in various dynamic investment settings (see, e.g., Azevedo et al, 2021;Della Seta et al, 2012;Hagspiel et al, 2016;Sarkar, 2021;Sarkar & Zhang, 2020;Paxson et al, 2022;Wen et al, 2017). In these studies, demand uncertainty is characterized by a geometric Brownian motion (GBM) process.…”

Section: Introductionmentioning

confidence: 99%

Optimal investment timing and size decision‐making with jump risk and time‐inconsistent preferences

Wang,

2024

Manage Decis Econ

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This paper analyzes a firm's investment timing and capacity decision by considering the manager's time‐inconsistent preferences and continuous economic depreciation of the capital stock cannot be completely offset. The demand shock of the underlying product evolves according to a double exponential jump‐diffusion process. Within a real options framework, we derive the explicit expressions for the optimal investment threshold and investment size when the manager is sophisticated. We find that time‐inconsistent preferences will lead the firm to delay investment when capital depreciation rate is low and accelerate investment when the rate of capital depreciation is high. Further, we show that capital depreciation rate has a monotonic positive effect on the investment size of a time‐consistent manager but a non‐monotonic effect on that of a sophisticated manager. A sophisticated manager tends to choose a smaller investment size than a time‐consistent manager. Finally, we demonstrate that compared with the geometric Brownian motion, the firm invests later with a larger capacity under the jump‐diffusion model.

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Optimal investment in learning-curve technologies

Cited by 26 publications

References 24 publications

When to invest in electric vehicles under dual credit policy: A real options approach

When to invest in electric vehicles under dual credit policy: A real options approach

Mobilizing Credit for Clean Energy: De-Risking and Public Loan Provision Under Learning Spillovers

Optimal investment timing and size decision‐making with jump risk and time‐inconsistent preferences

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