peakload generators. I show that aggregate wind generator ownership re- duces the positive impact of the wind generation on the market outcomes and as a result the total peakload production decreases and the market price increases. Furthermore, when all wind generators are owned by the peak- load ﬁrms, the impact of wind generation on the market outcomes vanishes. Additionally, start up and shut down (suspension) price thresholds are sig- niﬁcantly higher when the owner of peakload capacity also owns a share of wind power generators. I also ﬁnd that a feed-in premium support scheme does not aﬀect the peakload ﬁrms production levels and hence the market outcomes. However, under a feed-in tariﬀ type of support scheme, there is an increase in the total production and a decrease in the market price. The third chapter, coauthored with Rune Ramsdal Ernstsen, compares the investment timing and the optimal level of investment for a hypothetical mo- nopolist and a social planner that have a one-time opportunity to invest in a generator with either ﬁxed or ﬂexible production. It speciﬁcally investi- gates how the investment triggers, optimal capacities and technology choices change with the changes to the investment cost function, demand uncertainty and the level of installed capacity in the market. The main contribution of this paper is to document that the choice to invest between generators with ﬁxed or ﬂexible production does not only depend on the diﬀerences in costs for diﬀerent technologies but also on the diﬀerences in operation of those technologies.
are not suﬃcient to control agency problems, takeovers are believed to act as an external control device of last choice (Manne (1965)). Manne (1965) also states that a ﬁrm’s managers are exposed to a threat of takeover if their perfor mance lags behind due to agency problems. On the other hand, some scholars consider M&As as a direct outcome of agency problems rather than a solution. Mueller (1969) argues that managers, whose compensation is assumed to be a function of the size of the ﬁrm, have the incentive to increase the size of their ﬁrms, which can be realized through a takeover deal. Free cash ﬂow hypothesis (Jensen (1986)) provides a combined view of the two points aforementioned. It shows how takeovers are both evidence of conﬂicts of interest between sharehold ers and managers, and a solution to the problem. Free cash ﬂow theory predicts that mergers are more likely to destroy value, rather than create value. The theory believes that free cash ﬂows should be paid out to shareholders, reducing the power of management and subject managers to the scrutiny of public capital markets more frequently, which in turn mitigates agency problems. However, takeover is an alternative way for managers to spend cash instead of paying it out to shareholders and will consequently lead to ineﬃciency. On the other hand, leveraged buyouts (LBO), which normally involve a large amount of debt issuing, provide a device of bonding the managers’ promise to pay out future cash ﬂows to shareholders, which hence alleviates the impact of agency problems.
While this optimization is performed by the individual power producer, who wants to set up a new plant, large investors would typically want to invest in a portfolio of technologies rather than concentrate on a single technology or a single chain. The contribution of this paper is to combine portfolio optimization with the results that we derive from our real op- tions framework. In particular, we use the realoptions model to find the optimal investment strategy and its implied return distributions; the return distributions, then, can be employed as an input into the portfolio optimization. In traditional finance, the standard portfolio op- timization procedure is the mean-variance approach introduced by Markowitz (1952), where the portfolio variance is minimized subject to a constraint on the expected return. Risk man- agement in financial institutions, however, has started to employ another measure of risk, the Value-at-Risk (VaR), since it captures extreme – and thus dangerous – events providing information on the tail of a distribution. Also regulatory requirements as specified by the Basel Committee on Banking Supervision (2003, 2006) are geared towards the use of VaR. Another risk measure, which is closely related to VaR but offers additional desirable prop- erties like coherence and computational ease, is Conditional Value-at-Risk (CVaR). While VaR is generally better known and more widely employed, we think that CVaR is the more appropriate measure to use. Let us define VaR and CVaR to make clear what we are talking about. According to Rockafellar and Uryasev (2000) the β-VaR of a portfolio is the lowest amount α such that, with probability β, the loss will not exceed α, whereas the β-CVaR is the conditional expectation of losses above that amount α, where β is a specified probability level. 2
1990, but by starting in 2000 we have a longer sample period than has been used in previous studies. We also utilize average ticket prices in our revenue function. We estimate our winning percentage function using a logistic regression, rather than the standard linear probability model that has been used in the past. Finally, rather than estimate MRPs for every player, we estimate MRPs only for players who signed contracts with new clubs after free agency. In this way, we isolate the effect that the expectation of future performance has on the negotiated salary. We compare the newly negotiated annual salary of each free agent position player (excluding pitchers) to an estimate of the present value of the marginal revenue products over the lifetime of the new contract.
Additional simplifying assumptions.We further assume the market-to-book-value of non-performing loans and performing loans remains constant. In line with option pricing practice, we also assume that price volatility and the risk free rate are stationary, there are no transaction costs or taxes, and the stock (home) does not pay a dividend. It is true that these assumptions do not always hold in the real world. However, the transaction costs and taxes will be minimal for a distressed property due to a major decline in value. In addition, although rental revenue may be positive, it is likely offset by costs of maintaining the property. Finally,  describes, if real es- tate investments of publicly-traded firms “are chosen in a manner consistent with value maximization, then real estate prices will be determined in equilibrium as if markets were really frictionless.”
Modelling migration in this manner is a direct extension of Dixit’s (1989) work on the entry and exit problem of a competitive firm, and was first worked on by Burda (1993) and then by O’Connell (1997). In the entry-exit case, the variable driving the decision process is the price of output, which reflects demand uncertainty. The natural thresholds for entry and exit from “traditional” microeconomics, i.e. the long run average cost and short run minimum average variable cost, respectively, are shown to span a smaller range of prices than those found using the optionsapproach. This implies a bigger range of prices for “inactivity” or staying in a state the firm is currently in, inside or outside of the market. Dixit attributes this difference to the firm’s approach to uncertainty. The former (traditional) approach assumes “static” expectations, where a firm would expect the current price to prevail forever while the latter explicitly takes into account the nature of uncertainty or the stochastic process driving the price of output.
Waiting may be costly. In particular, the greater the length of time that the investment is postponed, the lower ADSL selling prices are likely to be. More significantly, the main negative impact of deferral on project value stems from the subsidy paid by local public administrations. The amount of the subsidy is at the sole discretion of local authorities. These bodies are interested in introducing cutting-edge technology to their areas which ordinarily would have been made available only years later on pure market criteria.
Uncertainties that exist in property development pose considerable risks to developers in the form of unfavourable changes in economic conditions. One strategy adopted by residential property developers engaged in land banking in Australia against future unfavourable outcomes due to uncertainties, is the use of presales before commencement of construction. Developers primarily, use presales as a risk management tool to mitigate potential downside risks from uncertainties because they have not accepted the idea that uncertainties can have positive impact on profitability. However, presales can cause loss of future revenue to developers if residential property prices rise in future albeit locked in contracts, and when property values plummet, the potential of losses is also imminent as default in settlement may occur. In view of this, residential property developers require strategies that can deal with uncertainties better. For example, discussions with developers suggest that the most difficult variable to estimate accurately in financial evaluation analysis is value on completion. Managing uncertainties in residential developments require active decision making in the form of inherent strategic alternative decisions that, can serve as both a hedge against future unfavourable outcomes and at the same time enable property developers to capitalise on emerging opportunities when market conditions are favourable. The value of such strategic flexible future decision rights are generally tied to uncertainty and the ability of developers to flexibly respond to changes in economic conditions during the execution of projects. These managerial flexibilities or strategies have been termed as options (Myers, 1984) and categorised to include defer, expand, stage, abandon, temporary shut down and compound options in real estate (Lucius, 2001) based on a general categorisation (Trigeorgis, 1996). The real option to stage a residential development project is a common flexible managerial right a developer can adopt to mitigate future risks and uncertainties, while at the same time
Figure 1 shows the dynamics of the option values at different initial prices of diesel. Result shows that the op- tion values decrease over diesel price as the cost of gen- erating electricity increases with fuel price. The trigger price as indicated by the intersection of option value curves indicates the minimum price of diesel that maxi- mizes the decision of shifting from diesel based to RE generation. The result in the baseline scenario at US$168/barrel is higher than the current price at US$101.6/barrel. Intuitively, this implies that waiting to invest in RE is a better option than investing at the current price of diesel. However, the value of waiting to invest as describe by the distance between option value curves from initial to terminal period is negative. As seen in Table 4, the option value at the current price of diesel at the initial period of investment is US$141.38 million and decreases to 104.97 million at the terminal period. This results to a US$36.41 million loss from delaying or waiting to invest. This implies that waiting to invest in RE incurs losses.
Norsk Hydro ASA has a sequence of five hydraulically coupled power plants installed in a river system in their production portfolio. These are aged plants that were designed to provide base load electricity production to industries in the surrounding area. The existing configuration suffers from high a response time and low efficiency. The majority of the inflow used for electricity production is accumulated and stored in a large reservoir. This reservoir has a 67.5 % degree of regulation, defined as reservoir size relative to mean yearly inflow (Norsk Hydro, 1987). That is, with no discharge and average inflow, an empty reservoir is refilled in approximately eight months. Due to the degree of regulation opportunities, it is regarded as an multiseasonal reservoir.
Extensive studies have been conducted in recent years about the structure of the energy market that a summary of the most important of these studies is presented in table 1. Garcia and Arblis have conducted simulations for the Columbia Electricity Market in an article entitled “Market Power Analysis for the Columbia Electricity Market” (Garcia and Arbelaez, 2002). They have used a dynamic Cournot model to show the potential impacts of mergers in the nation’s electricity wholesale market. These simulations have shown that the price level after the merger would be on average 24% more than before. The study also shows that by adding a large number of predicted contracts to the model, not only will prices not increase but will even decrease in some cases.
Battery technologies that are either in use/or are potentially suitable for grid application include lead acid, vanadium redox battery (VRB), sodium sulphur (NaS) and lithium ion (li-ion). Table 3.2 presents some of their characteristics. The most widely used worldwide are lead acid, due to its availability and low cost. VRB has potentially the lowest cost and highest cycle life, but the lowest efficiency and energy density of the four. This means that it requires a large amount of space and that it is only suitable for small or medium applications. NaS competes with lead acid due to its higher energy density and longer lifetime. However, NaS batteries must be kept at 300-350 ◦ C. The heat source uses the battery’s stored energy, partially reducing performance. Li-ion has the most favorable characteristics with respect to efficiency, energy and power density. The main obstacle is its capital cost.
In recent years, the state of market microstructure has changed considerably. There are many ways in which these changes have come about, but one of the biggest changes is that markets have become highly fragmented. When markets are fragmented, traders must search across many markets for venues which will execute their orders at their specified prices. This can result in delayed or partial execution which is costly. In response to the increase in market fragmentation, there has been a demand for speed by traders, and various types of expensive technologies have been developed. Such technologies enable traders to compare all trading venues instantaneously or obtain a glimpse of the true state of the market before everyone else. In this paper I derive a dynamic model, using techniques from realoptions analysis, which provides an optimal timing strategy for slow traders to invest in a high frequency trading technology. The model prescribes waiting longer to invest if the level of high frequency trading in the market increases, and it prescribes earlier adoption if the probability of finding a liquid venue decreases. It also prescribes waiting longer if the uncertainty of the profit process increases and, if the probability of finding a liquid venue is low, if the discount rate increases and/or the shortfall decreases. However, if the probability of finding a liquid venue is high, an increase in the discount rate and a decrease in the shortfall make early adoption optimal.
ture that deals with voluntary disclosure and the literature that addresses the non-exclusivity feature inherent in a real option, in particular, the issue of imperfect competition. While corporate voluntary disclosure has become an important and topical area of research in recent years, particularly in the ac- counting literature (see Verrecchia  for a detailed discussion), there have been very few real option applications concerned with voluntary disclosure and none, as far as I am aware, concerning competitive interactions between firms in determining equilibrium exercise policies from a realoptions perspective. Therefore, such an analysis provides an interesting and useful contribution to the literature.
Theoretical advances in realoptions methodology have been rapidly formulated and assimilated in several empirical applications. Realoptions have been identified and valued in natural resources  and a growing body of literature provides various examples of flexible investment strategies [11, 12, 13, 14, 15, 16, 17]. A few studies, also, implement realoptions in agriculture. Among them, Purvis, et al.,  try to examine the technology adoption of a free-stall dairy housing under irreversibility and uncertainty and its implications in the design of environmental policies. Ekboir,  through a stochastic dynamic model analyzes the investment decisions of an individual farmer under risk in the presence of irreversibility and technical change. Winter-Nelson & Amegbeto,  present a model of investment under uncertainty to analyze the effect of the variability of prices on the decision to invest in conservation with application to terrace construction. Price & Wetzstein,  develop a model for determining optimal entry and exit thresholds for investments in irrigation systems when irreversibility and uncertain returns are given, with price and yield as stochastic variables. Khanna et al.,  analyze the impact of price uncertainty and expectations of declining fixed costs on the optimal timing in site-specific crop management. Hyde et al.  present the optimal investment in an automatic milking system. Tauer  tried to find when to get in and out of dairy farming and Rahim et al.  tried to analyze farmers’ economic incentives for abandoning or expanding gum Arabic production.
expenses and increase efficiency. This is especially an important theme in the current climate with inflated costs and low oil prices (Norwegian Petroleum Directorate (NPD), 2005). Johan Castberg, Alta and Gohta (Alta/Gohta) are currently the most prospective fields located adjacently in the mild part of the Barents’ Sea. Following Goliat, the two areas can be the second and third two oil fields developed in the Norwegian High North. Lacking supporting infrastructure, and being far from markets in an unexplored basin – Johan Castberg and Alta/Gohta are challenged by high expenditures. To reduce costs, there has been discussions on developing Johan Castberg as a central hub, with capacity to include nearby licenses - as Alta/Gohta (Bjørsvik, 2015).
In the scenario of nuclear accident, the probability is set to 0.01% per year with damage cost comparatively higher than the values reported at the NEA (2016b) . The accident cost is set greater than the values reported in literature to describe a more realistic maximum potential for nuclear damages. The last scenario incorporates the externality cost of electricity generation from various sources. The values used here are in line with the external cost of generating electricity in the Philippines (Meller and Marquardt, 2013) and average external costs for electricity generation technologies (EEA, 2010) . The externality values are first set to US$6/MWh for renewable energy, US$1/MWh for nuclear energy to US$1/MWh while zero for electricity generation from coal (as described in base scenario). The value of externality for using coal are then adjusted from 0 to US$100/MWh at US$25/MWh increment. In this scenario describes how increasing externality cost from coal affects the options values and trigger prices of shifting technology from coal to alternative energies. This scenario also finds the threshold of externality cost for shifting technologies from coal to alternatives.
Bar-Ilan and Strange ( 1996) present a more sophisticated model in a similar vein to Capozza and Helsley. Rents are modeled to follow a Geometric Brownian Motion (GBM), and decisions to develop agricultural land are made reversible. Their model incorporated fixed development time lags to explain the phenomena in which distant land is sometimes developed prior to nearby land. Leishman et al. ( (2000)) conducted empirical research on builder behaviour to test the validity of the realoptions based approach, but didn’t provide conclusive evidence either in support or in opposition to the real option valuation approach. They stopped short of modeling the relationship between house and land prices, instead relying on builders’ projections versus observed prices to test the influence of uncertainty on land prices.
The combination of uncertainty, irreversibility and investment timing flexibility provides the building blocks of the option to delay theory. Although the option to delay’s concept has been extensively studied in competitive markets, 4 its implications on regulated prices and investment incentives are less well understood. Indeed, there has been much debate on this subject recently. For instance, in the context of telecommunications, the New Zealand Commerce Commission stated that “the obligation to provide interconnection services removes the option for access providers to delay investment in their fixed Public Switched Telephone Networks. 5 If this option has a value, the costs of foregoing the option are a cost that should be reflected in interconnection prices” (Commerce Commission 2002). In its latest cost of capital consultation, the telecommunications regulator in the United Kingdom proposed that “Ofcom should begin to develop a framework by which regulatory policy might reflect the value of these options (realoptions)” and “a key area identified by Ofcom as being one in which the value of wait and see options might be significant was that of next generation access networks” (Ofcom 2005).
One of the first applications of the realoptions theory to this area dates back to 2006, by Yu et al.  . They used realoptions techniques to evaluate switching tariff for different wind genera-tion assets, and to identify optimal switching policies and values, in Spanish electricity markets. Two years later, in 2008, Kum-baroglu˘ et al.  presented a policy planning model that integrates learning curve information on renewable power generation tech-nologies into a dynamic programming formulation containing realoptions theory. Note that the model was successfully applied in Turkey. One year later, Siddiqui and Fleten  examined how a staged commercialization programme for an unconventional energy technology could proceed under uncertainty. Lee and Shih  presented a policy benefit evaluation model using real option pricing techniques and considered uncertainty and others fac-tors that impact policy for developing renewable energy. Their framework allows to assess volatility, uncertainty, and managerial flexibility in policy planning.