Janne Kettunen

Optimization of Electricity Retailer's Contract Portfolio Subject to Risk Preferences

When an electricity retailer faces volume risk in meeting load and spot price risk in purchasing from the wholesale market, conventional risk management optimization methods can be quite inefficient. For the management of an electricity contract portfolio in this context, we develop a multistage stochastic optimization approach which accounts for the uncertainties of both electricity prices and loads, and which permits the specification of conditional-value-at-risk requirements to optimize hedging across intermediate stages in the planning horizons. Our experimental results, based on real data from Nordpool, suggest that the modeling of price and load correlations is particularly important. The sensitivity analysis is extended to characterize the behavior of retailers with different risk attitudes. Thus, we observe that a risk neutral retailer is more susceptible to price-related than load-related uncertainties in terms of the expected cost of satisfying the load, and that a risk averse retailer is especially sensitive to the drivers of the forward risk premium.

New product development flexibility in a competitive environment

Managerial flexibility can have a significant impact on the value of new product development projects. We investigate how the market environment in which a firm operates influences the value and use of development flexibility. We characterize the market environment according to two dimensions, namely (i) its intensity, and (ii) its degree of innovation. We show that these two market characteristics can have a different effect on the value of flexibility. In particular, we show that more intense or innovative environments may increase or decrease the value of flexibility. For instance, we demonstrate that the option to defer a product launch is typically most valuable when there is little competition. We find, however, that under certain conditions defer options may be highly valuable in more competitive environments. We also consider the value associated with the flexibility to switch development strategies, from a focus on incremental innovations to more risky ground-breaking products. We find that such a switching option is most valuable when the market is characterized by incremental innovations and by relatively intense competition. Our insights can help firms understand how managerial flexibility should be explored, and how it might depend on the nature of the environment in which they operate.

Managing Reliability and Stability Risks in Forest Harvesting

The timing of forest stands harvesting is an important operational decision in forestry. Major goals of private nonindustrial forest owners are to achieve a steady flow of profits while reaching an overall satisfactory and reliable profit level. These goals are pursued under uncertainties in the growth of trees in different regions and in the prices of wood products. We propose an optimization framework that uses financial risk concepts to capture the above goals and uncertainties, and apply it to a real forestry problem in Finland. Our results demonstrate that the obtained harvesting schedules outperform those obtained without the explicit consideration of the stability and reliability requirements in harvest profits. More generally, our results indicate that the forest owner can improve the profit stability by (i) harvesting a greater number of forest stands early and (ii) harvesting in the first periods of the planning horizon stands that are predominantly composed of slow-growing forests. This researc...

Risk induced resource dependency in capacity investments

A capacity acquisition process is resource dependent when the existing resources impact the valuation of new resources and thereby influence the investment decision. Following a formal analysis of resource dependency, we show that uncertainty and aversion to risks are sufficient conditions for resource dependent capacity acquisition. Distinct from the technology lock-in effects of increasing returns to scale or learning, risk aversion can induce diversity. We develop a stochastic programming framework and solve the optimization problem by decomposing the problem into investment and operational horizon subproblems. Our computational results for an application to the electricity sector show, inter alia, that technology choices between low carbon and fossil fuel technologies, as well as their investment timings, are dependent upon the resource bases of the companies, with scale, debt leverage and uncertainty effects increasing resource dependency. Particularly, we show that resource dependency can significantly impact the optimal investment decisions and we argue that it should be evaluated at both company and policy levels of analysis.

Predicting Project Environmental Performance under Market Uncertainties: Case Study of Oil Sands Coke

A method combining life cycle assessment (LCA) and real options analyses is developed to predict project environmental and financial performance over time, under market uncertainties and decision-making flexibility. The method is applied to examine alternative uses for oil sands coke, a carbonaceous byproduct of processing the unconventional petroleum found in northern Alberta, Canada. Under uncertainties in natural gas price and the imposition of a carbon price, our method identifies that selling the coke to China for electricity generation by integrated gasification combined cycle is likely to be financially preferred initially, but eventually hydrogen production in Alberta is likely to be preferred. Compared to the results of a previous study that used life cycle costing to identify the financially preferred alternative, the inclusion of real options analysis adds value as it accounts for flexibility in decision-making (e.g., to delay investment), increasing the projects expected net present value by 25% and decreasing the expected life cycle greenhouse gas emissions by 11%. Different formulations of the carbon pricing policy or changes to the natural gas price forecast alter these findings. The combined LCA/real options method provides researchers and decision-makers with more comprehensive information than can be provided by either technique alone.

Is certainty in carbon policy better than uncertainty

Uncertainty in the strictness of carbon policy can have significant impacts on power generating firms’ capacity investment decisions and market outcomes. We investigate the effects of this policy uncertainty on firms’ expected profits, consumers’ surplus, and firms’ cost for reaching a CO2 emission target. Our results, derived from a game-theoretical model, and applied to realistic data, indicate that uncertainty in the carbon policy induces more capacity investments in fossil and renewable technologies. We find that it is optimal for firms with higher risk aversion to invest more in renewable technologies than their less risk-averse rivals. For policy makers, our results suggest counter-intuitively that retaining the flexibility to update emission targets, whilst causing uncertainty in the carbon policy, is beneficial. This is because it provides higher expected consumer surplus and lower expected electricity price. Power generating firms are also better off under the policy uncertainty by having lower expected costs for reaching the emission goal and higher expected profits when firms’ risk-aversions are low. These results support the approach, employed in European power markets, to periodically update the CO2 emission cap depending on the prevailing circumstances, rather than having certainty in the decrements of the caps over a longer time horizon. Our insights can help policy makers and firms to make better decisions by understanding how carbon policy uncertainty impacts the optimal capacity investments and how these investments might depend on the firms’ heterogeneity in risk aversion.

Valuing Credit Default Swaps on Correlated LMM Processes

The article derives a model with a closed form solution for valuing default swaps including reference asset—counterparty default correlation. The default correlation between the reference asset and the counterparty is incorporated in two quadruple trees. One tree represents the default swap payoff of the default swap seller; the other tree represents the default swap premium payments of the default swap buyer. Swap valuation techniques are then applied to derive the fair default swap price. The model is combined with three LMM (Libor Market Model) processes. One LMM process simulates risk-free short-term interest rates. Two other LMM processes generate the reference asset default probabilities and the counterparty default probabilities. Two examples of the model are provided. In one model the default swap premium is paid upfront (at the beginning of each period) whereas in the other model the default swap premium is paid in arrears (at the end of each period).

A fractional stochastic integer programming problem for reliability-to-stability ratio in forest harvesting

We propose a new fractional stochastic integer programming model for forestry revenue management. The model takes into account the main sources of uncertainties—wood prices and tree growth—and maximizes a reliability-to-stability revenue ratio that reflects two major goals pursued by forest owners. The model includes a joint chance constraint with multirow random technology matrix to account for reliability and a joint integrated chance constraint to account for stability. We propose a reformulation framework to obtain an equivalent mixed-integer linear programming formulation amenable to a numerical solution. We use a Boolean modeling framework to reformulate the chance constraint and a series of linearization techniques to handle the nonlinearities due to the joint integrated chance constraint, the fractional objective function, and the bilinear terms. The computational study attests that the reformulation of the model can handle large number of scenarios and can be solved efficiently for sizable forest harvesting problems.