Real Asset Valuation: A Back‐to‐basics Approach

Different valuation methods can lead to different corporate investment decisions, and the conventional “static, single discount rate” DCF approach in particular is biased against many of the kinds of decisions that corporate managers tend to view as “strategic.” Reducing the bias from valuations involves two main tasks: treating risk in a way that is consistent with observed market pricing, and accounting for the ability of companies to make decisions “dynamically” over time. The authors propose two separate tools, market‐based valuation and complete decision tree analysis, for accomplishing these two improvements in valuation. The authors also suggest working with the full distribution of future cash flows, one possible realization at a time, rather than working with the aggregate measure of expected cash flow. From a technical perspective, it is necessary to work with the full distribution to value real options properly. Valuing the cash flows one realization at a time also leads to a much better understanding of the interaction between economy‐level, systematic risks and local asset‐level, technical risks. Just as important, the proposed approaches support an effective division of labor between local asset managers, who are better positioned to model technical considerations and other asset specifics, and the central finance staff, who can ensure the consistent treatment of economy‐wide risk and to create the rules of engagement for evaluating opportunities. After presenting an overview of both the valuation and the organizational issues, the authors present a case involving a corporate investment in carbon capture and storage that illustrates both the application of the proposed methods and the various sources of bias in the typical DCF analysis.     

Hedging volatility risk

Volatility risk plays an important role in the management of portfolios of derivative assets as well as portfolios of basic assets. This risk is currently managed by volatility “swaps” or futures. However, this risk could be managed more efficiently using options on volatility that were proposed in the past but were never introduced mainly due to the lack of a cost efficient tradable underlying asset.The objective of this paper is to introduce a new volatility instrument, an option on a straddle, which can be used to hedge volatility risk. The design and valuation of such an instrument are the basic ingredients of a successful financial product. In order to value these options, we combine the approaches of compound options and stochastic volatility. Our numerical results show that the straddle option is a powerful instrument to hedge volatility risk. An additional benefit of such an innovation is that it will provide a direct estimate of the market price for volatility risk.     

Real Options: Meeting the Georgetown Challange

In response to the demand for a single, generally accepted real options methodology, this article proposes a four‐step process leading to a practical solution to most applications of real option analysis. The first step is familiar: calculate the standard net present value of the project assuming no managerial flexibility, which results in a value estimate (and a “branch” of a decision tree) for each year of the project's life. The second step estimates the volatility of the value of the project and produces a value tree designed to capture the main sources of uncertainty. Note that the authors focus on the uncertainty about overall project value, which is driven by uncertainty in revenue growth, operating margins, operating leverage, input costs, and technology. The key point here is that, in contrast to many real options approaches, none of these variables taken alone is assumed to be a reliable surrogate for the uncertainty of the project itself. For example, in assessing the option value of a proven oil reserve, the relevant measure of volatility is the volatility not of oil prices, but of the value of the operating entity—that is, the project value without leverage. The third step attempts to capture managerial flexibility using a decision “tree” that illustrates the decisions to be made, their possible outcomes, and their corresponding probabilities. The article illustrate various kinds of applications, including a phased investment in a chemical plant (which is treated as a compound option) and an investment in a peak‐load power plant (a switching option with changing variance, which precludes the use of constant risk‐neutral probabilities as in standard decision tree analysis). The fourth and final step uses a “no‐arbitrage” approach to form a replicating portfolio with the same payouts as the real option. For most corporate investment projects, it is impossible to locate a “twin security” that trades in the market. In the absence of such a security, the conventional NPV of a project (again, without flexibility) is the best candidate for a perfectly correlated underlying asset because it represents management's best estimate of value based on the expected cash flows of the project.     

Valuation of a Pharmaceutical Licensing Contract

It is quite common for boutique pharma companies, biotech firms, and university labs to license their intellectual property (a new compound) to large pharmaceutical companies. In such cases, the big pharma companies finance and manage the R&D process and, if successful, market the drugs. In exchange for use of their intellectual property, the outlicensing organizations receive licensing contracts that promise some combination of fixed payments, event‐specific milestone payments, and revenue‐ or profit‐based royalties. The authors show how to estimate the expected present value of the future cash flows promised by such licensing deals. The complicating factor in such valuations is that the licensing contracts are essentially derivative contracts written on drugs in development, which as the authors showed in a paper published in this journal a year ago, are themselves compound options on marketed drugs. The authors use the valuation example from the earlier paper, along with a proposed licensing deal, and walk the reader through the process of valuing the licensing deal's cash flows.     

Options Arbitrage in Imperfect Markets

Option valuation models are based on an arbitrage strategy—hedging the option against the underlying asset and rebalancing continuously until expiration—that is only possible in a frictionless market. This paper simulates the impact of market imperfections and other problems with the “standard” arbitrage trade, including uncertain volatility, transactions costs, indivisibilities, and rebalancing only at discrete intervals. We find that, in an actual market such as that for stock index options, the standard arbitrage is exposed to such large risk and transactions costs that it can only establish very wide bounds on equilibrium options prices. This has important implications for price determination in options markets, as well as for testing of valuation models.     

Assessing Project Risk

Finding the appropriate discount rate, or cost of capital, for evaluating investment projects requires an accurate estimate of project risk. This can be challenging because project risk cannot be estimated directly using the CAPM, but must instead be inferred from a set of traded securities, typically the equity betas of comparable firms in the same industry. These equity betas are then unlevered to undo the effect of comparable companies' financial leverage and obtain estimates of “asset” betas, which are then used to estimate project risk. The authors show that asset betas estimated in this way are likely to overestimate project risk. The equity returns of companies are risky not only because of their existing projects but also because of their growth opportunities. Such growth opportunities often include embedded “real options,” such as the option to delay, expand, or abandon a project. Because such real options are similar to leveraged positions in the underlying project, a company's growth opportunities are typically riskier than its existing projects. Therefore, to properly assess project risk, analysts must also unlever the asset betas derived from comparable company stock returns for the leverage contributed by their growth options. The authors derive a simple method for unlevering asset betas for growth options leverage in order to properly assess project risk. They then show that standard methods for assessing project risk significantly overestimate project costs of capital—by as much as 2–3% in industries such as healthcare, pharmaceuticals, communications, medical equipment, and entertainment. Their method should also be applied to stock return volatility to derive project volatility, an important input for determining the value of a firm's growth opportunities and the appropriate time for investing in these opportunities.     

The Effect of “Private” and “Public” Risks on Oilfield Asset Pricing: Empirical Insights into the Georgetown Real Option Debate

The Spring 2005 issue of this journal featured a “debate” over the best way of applying real options. In “Real Options Analysis: Where Are the Emperor's Clothes?,” Adam B orison criticized most practices that go under the name real options and recommended an “integrated” approach that combines real options techniques with a traditional approach known as “decision tree analysis.” This approach breaks valuation problems into two components—“market” risks (say, oil price changes) and “private” risks (the possibility that actual reserves fall well short of estimated) — and then uses option pricing models to evaluate the market risks and decision trees for the private risks. In response to Borison's article, Tom Copeland and Vladimir Antikarov argued that these two components can be evaluated in a single analysis that uses both DCF (to calculate the value of the “underlying asset”) and Monte Carlo simulation (to estimate the volatility of the underlying), thereby expanding the range of real options applications. In this article, the authors attempt to shed light on this debate with the findings of their extensive empirical analysis of U.K. oilfield expansion options. The bottom line of their study is that size matters in the context of oilfields, presumably because it offers a reliable guide to the kind and size of risks associated with the project. In the case of the larger oilfields, where market risks are likely to outweigh the private risks, the author's findings suggest that both approaches are reasonably effective and provide roughly the same degree of accuracy. In the case of smaller fields, however, where private risks are proportionally larger, the authors conclude that Borison's approach is likely to be more reliable.     

Valuing qualitative options with stochastic volatility

We find a closed-form formula for valuing a time-switch option where its underlying asset is affected by a stochastically changing market environment, and apply it to the valuation of other qualitative options such as corridor options and options in foreign exchange markets. The stochastic market environment is modeled as a Markov regime-switching process. This analytic formula provides us with a rapid and accurate scheme for valuing qualitative options with stochastic volatility.     

Valuing Pharma R&D: The Catch-22 of DCF

There are two principal methods for valuing pharmaceutical R&D projects—discounted cash flow (DCF) and real options valuation (ROV). As typically practiced, DCF valuations tend to be lower than the estimates produced by ROV techniques. Part of the difference, as many have recognized, stems from DCF s limited ability to take account of managers' real option to cut its losses when new information reveals a drug candidate's lack of profit potential. Another reason for the difference, however, is the widespread use in DCF valuations of established success rates that do not distinguish between projects that fail to pass safety or efficacy trials and those that are abandoned for lack of economic viability. If the appropriate success rates are used, the two methods should yield identical project values because they assume the same scenarios. The practical reality, however, is that the two methods deal in a completely different way with the possibility of abandonment for economic reasons. Because ROV accounts for this possibility directly in the model itself, it is much better suited than DCF to this task—indeed, that is the uncertainty that it is designed to deal with. And the fact that 30% of all pharma R&D abandonments are for economic reasons is a strong argument for using ROV rather than DCF to evaluate new drug development.     

Uncertainty and R&D investment: Does product market competition matter?

In recent years, the question of how uncertainty influences corporate decisions has received greater interest among academics, researchers, and corporations. This study is an attempt to investigate how uncertainties (firm-specific uncertainty (fsu), market-based uncertainty (mu), and economic policy uncertainty (epu)) influence research and development (R&D) investment and to further examine whether this relation is moderated by product market competition across firms of different sizes. Using a sample of Chinese listed firms covering 2000–2017, this study applies a two-step system GMM model to perform estimations and control for endogeneity issues. The findings show that uncertainties (fsu, mu, and epu) negatively influence R&D investment and that this negative relationship is more prominent for firms operating in competitive industries. In concentrated industries, however, the negative impact of uncertainty is mitigated for large firms, which have more internal resources and better access to external financing. This study contributes to real options theory by illustrating how different forms of uncertainty embedded in a firm’s internal and external environment reduce R&D investment, and by indicating how this relationship is moderated by product market competition.     

Return predictability and shareholders’ real options

This study re-interprets the properties of the residual income model by highlighting the shareholders’ abandonment (liquidation or adaptation) option. We estimate the value of this real option as an explicit component of abnormal earnings in the residual income model and test the improvement in valuation after incorporating it into the model. Relative to the traditional specification of the residual income model, this real options model has a stronger predictive power for future abnormal stock returns. We also find that the superior return predictability of the real options model is pronounced in the set of firms with a high probability of exercising liquidation options (for example, those with low profitability, low growth opportunities, high underlying asset volatility, and low intangible assets), which is consistent with the importance of shareholders’ abandonment option in equity valuation. The results are robust to extensive sensitivity checks.     

STRATEGY AND SHAREHOLDER VALUE CREATION: THE REAL OPTIONS FRONTIER

The current interest in real options reflects the dramatic increase in the uncertainty of the business environment. Viewed narrowly, the real options approach is the extension of financial option pricing models to the valuation of options on real (that is, nonfinancial) assets. More broadly, the real options approach is a way of thinking that helps managers formulate their strategic options—the future opportunities that are created by today's investments—while considering their likely effect on shareholder value. But if the real options framework promises to link strategy more closely to shareholder value creation, there are some major challenges on the frontier of application. In the first part of this paper, the authors tackle the question, “What is really new about real options, and how does the approach differ from other wellestablished ways to make strategic decisions under uncertainty?” This article provides a specific definition of real options that relies on the ability to track marketpriced risk. Using examples from oil exploration and pharmaceutical drug development, the authors also show how specific features of the industry and the application itself determine the usefulness of the real options approach. The second part of the paper addresses the question: Given the many differences between real and financial options, how should a real options application be framed? The authors examine the use of real options in the valuation of Internet companies to demonstrate the required judgment and tradeoffs in the framing of real options applications. The case of Webvan, an online grocer, is used to illustrate the inter‐action between strategy, execution, and valuation.     

VALUE CREATION AT ANHEUSER-BUSCH: A REAL OPTIONS EXAMPLE

The management of Anheuser-Busch created $11.5 billion of shareholder value between 1996 and 1998, a period in which U.S. demand for beer was flat and the company's profits grew only modestly. Of that $11.5 billion, the authors estimate that nearly $10 billion can be attributed to the growth options created or expanded by the company during that period. While divesting itself of unrelated businesses, such as snack foods, Busch stadium, and the St. Louis Cardinals baseball franchise, the company began purchasing minority equity interests in brewing concerns in markets with growing demand for beer, including Mexico, Brazil, Chile, Argentina, and the Philippines.
The main undertaking of the paper is to use the real options valuation method to estimate the growth option value that Anheuser-Busch has created through its investments in joint ventures in foreign markets. The authors focus specifically on a joint venture in the Argentina/Chile market, and argue that this arrangement gives the company the flexibility to invest in a complete brewing and distribution system in that market after learning about the market's potential. In other words, the joint venture creates a call option on the Argentina/Chile market. Traditional DCF analysis, which ignores the flexibility in the strategy, assigns a negative NPV to the joint venture. But explicit recognition of the "option-ality" built into the investment results in a very different valuation—as well as a plausible explanation of the growth option value in the company's stock price.
As the Anheuser-Busch example also illustrates, valuation of the real option depends critically on the assumption about the volatility of the future value of the investment projects. The authors provide an intuitively useful way for managers to examine their own volatility assumptions—one that draws on the probability assessments that are part of the well-known Black-Scholes model.     

A jump diffusion model for VIX volatility options and futures

Volatility indices are becoming increasingly popular as a measure of market uncertainty and as a new asset class for developing derivative instruments. Although jumps are widely considered as a salient feature of volatility, their implications for pricing volatility options and futures are not yet fully understood. This paper provides evidence indicating that the time series behaviour of the VIX index is well approximated by a mean reverting logarithmic diffusion with jumps. This process is capable of capturing stylized facts of VIX dynamics such as fast mean-reversion at higher levels, level effects of volatility and large upward movements during times of market stress. Based on the empirical results, we provide closed-form valuation models for European options written on the spot and forward VIX, respectively.     

Using real options theory to explain patterns in the valuation of research and development expenditures

Real options theory posits that the value of the firm is a combination of the value generated by the assets in place and the value of the option to invest in the future. It is based on the idea that many decisions are difficult to reverse, and valuing the outcome of these decisions is more complicated than estimating the present value of future cash flows. R&D activities often generate real options due to the nature of these activities, and examining the valuation of R&D expenditures through the lens of real options theory can help explain differing results documented in both the R&D and value relevance of earnings and book value literatures. Numerous studies have documented that the stock market positively values R&D expenditures; however, recent work has raised questions about whether this positive relation occurs across firms reporting both profits and losses. Consistent with real options theory, I find that the negative coefficient on the R&D expenditures of profitable firms documented by prior studies only exists for low growth firms. In addition, for all R&D firms experiencing high sales growth, the market places a lower value on assets in place and a higher value on R&D expenditures.     

A PDE approach for risk measures for derivatives with regime switching

This paper considers a partial differential equation (PDE) approach to evaluate coherent risk measures for derivative instruments when the dynamics of the risky underlying asset are governed by a Markov-modulated geometric Brownian motion (GBM); that is, the appreciation rate and the volatility of the underlying risky asset switch over time according to the state of a continuous-time hidden Markov chain model which describes the state of an economy. The PDE approach provides market practitioners with a flexible and effective way to evaluate risk measures in the Markov-modulated Black–Scholes model. We shall derive the PDEs satisfied by the risk measures for European-style options, barrier options and American-style options.      

Investment,consumption, and hedging under incomplete markets

Entrepreneurs often face undiversifiable idiosyncratic risks from their business investments. We extend the standard real options approach to an incomplete markets environment and analyze the joint decisions of business investments, consumption/savings, and portfolio selection. For a lump-sum investment payoff and an agent with a sufficiently strong precautionary savings motive, an increase in volatility can accelerate investment, contrary to the standard real options analysis. When the agent can trade the market portfolio to partially hedge against investment risk, the systematic volatility is compensated via the standard CAPM argument, and the idiosyncratic volatility generates a private equity premium. Finally, when the investment payoff is a series of flows, the agent's idiosyncratic risk exposure alters both the implied option value and the implied project value, causing a reversal of the results in the lump-sum payoff case.     

The Determinants of Investor Valuation of R&D Expenditure in the Software Industry

This paper examines the cross-sectional variability in the market valuation of R&D expenditures in the pre-packaged computer software industry. Consistent with some prior research, this paper argues that R&D spending is valued heterogeneously by the stock market, and derives hypotheses regarding the determinants of the cross-sectional heterogeneity in the market valuation of R&D. The empirical tests use an extensive database containing product level information of software firms between 1994 and 1998, along with accounting and stock price data of the same period. The test results, consistent with our hypotheses, show that R&D spending is more valuable for firms with larger market shares, higher percentage of technical employees, and those that have diversified into different product categories. The results also indicate that market valuation of R&D spending is a function of product life cycle.     

The volatility target effect in structured investment products with capital protection

Designing a structured investment product with capital protection which would be characterized by high capital protection level as well as high equity participation rate is a challenging task in the current market environment. Low interest rates and high volatility levels negatively affect the above key parameters of such investment products. One way to increase the participation rate of a structured investment product with a fixed capital protection level is to use a volatility target (VolTarget) strategy as an underlying asset for a financial option embedded in such a product. We introduce an extended VolTarget mechanism with interest rate dependent volatility target levels and provide a detailed comparative numerical study of European options linked to VolTarget strategies within a hybrid Heston–Vasi?ec model with stochastic volatility and stochastic interest rate.     

What is the correct meaning of implied volatility?

This paper presents a closed-form solution for the valuation of European options under the assumption that the excess returns of an underlying asset follow a diffusion process. In light of our model, the implied volatility computed from the Black–Scholes formula should be viewed as the volatility of excess returns rather than as the volatility of gross returns. Using the SPX and the OMX options data, we test whether implied volatility obtained from Black-Scholes option price explains the volatilities of excess returns better than gross returns, even though the result is not statistically significant.