The success of real option valuation (hereafter ROV) can be attributed to a

few key strengths. First, ROV methods generate dynamic models that allow

us to make quantitative predictions. For example, a structural real options

model of the firm (such as Leland (1994)) generates estimates for the firm's

value, default probability, credit spread and many other variables of interest.

The model allows us to predict what happens to those variables if, say, the

volatility of the firm's assets increases from 20% to 30%. Static models only

generate qualitative predictions that may not necessarily hold in a dynamic

context (Pennings (2017) in this special issue provides such an example).

Second, structural real option models can (unlike their static counterparts)

be brought to the data and tested. Structural estimations can be used to obtain

parameter estimates of unobservables. This is useful in areas such as capital

structure research where the relative costs and benefits of leverage have been

central to the debate. Dynamic structural real options models allow estimates

for expected bankruptcy costs, issuance costs and managerial preferences to

be inferred. These estimates may shed light on the plausibility of a particular capital structure theory. We refer to Strebulaev and Whited (2012) for an excellent review on dynamic structural models in corporate finance and structural estimation.

Third, ROV methods allow us to calculate values for investment projects

and contingent claims on these investments. The seminal paper by Brennan

and Schwartz (1985) shows how in the spirit of Black and Scholes (1973) and Merton (1973) real options can be priced by finding a risk-free self-financing

portfolio whose cash cows replicate those which are to be valued. The present

value of the cash ow stream is then equal to the current price of this replicating portfolio. Merton (1998) argues that even if the underlying asset is not

traded or its price not observed, its value can often be tracked by a portfolio

of traded securities. To derive the option valuation formula one pretends \as

if" the underlying asset is traded. Having a pricing framework is useful for

cases where a significant fraction of a firm's value is attributed to real options

(e.g. growth options). Of course, given that real options can only add value,

one has to be careful not to inflate valuations.

Fourth, ROV encourages managers to think strategically about investments.

ROV requires managers to identify the options at their disposal and pro-

actively to determine under which circumstances or conditions they will be

exercised. This leads to a pro-active and flexible management style in which

managers act optimally as economic uncertainty unfolds. ROV provides a

framework that bridges the gap between finance and strategy. In practice,

first order mistakes are not made because of a second order mispricing of a

real option, but because a valuable real option has been overlooked altogether.

ROV is still useful as a conceptual framework even when those real options

are (too) hard to value. To quote Amram and Kulatilaka (2000), ROV is \a

way of thinking" that helps managers formulate their strategic options.

Finally, ROV provides a paradigm within which capital budgeting research

can consolidate. Before Modigliani and Miller (1958) capital budgeting (and _finance more generally) consisted of a collection of disparate theories, anecdotes

and rules of thumb that were in no way based on a common set of principles

and axioms. The papers by Modigliani and Miller (1958), Black and Scholes

(1973) and Merton (1973) introduced core principles such as value additivity

and arbitrage, allowing finance theories to be built on a common foundation

and to consolidate into a coherent framework. Those theories can be tested,

and anomalies that are encountered can spur research into new directions.

The dynamic nature of ROV comes at a cost of increased complexity and

decreased transparency. The mathematical complexity may partially explain

why ROV is not (yet) the workhorse capital budgeting method in finance

textbooks. Core finance courses to undergraduate and MBA students still

rely on the static Net Present Value (NPV) method, with a proper study

of ROV left for more advanced elective courses. In a survey by Graham and

Harvey (2001), 75% of CFOs respond that they \always or almost always" use

NPV, but only 25% claim to use real option methods. Block (2007) surveys

the Fortune 1000 largest companies in the US and reports that only 14.3% of respondents use real options in the capital budgeting process. The users

come primarily from industries where sophisticated analysis is the norm such

as technology, energy and utilities. The primary reasons given for non-use of

real options in order of importance are \(a) lack of top management support;

(b) discounted cash ow is already a proven method; (c) real options require

too much sophistication and (d) real options encourage excessive risk-taking

because CFOs believe that ROV overestimates the value of uncertain projects

encouraging companies to invest in them".

These survey findings appear at odds with empirical studies reporting that

managerial decisions and market prices for assets with real options are consistent with real options theory. Admittedly, the number of empirical studies

is small and they focus on a few industries such as real estate (Quigg (1993),

Cunningham (2006), Bulan et al. (2009)), oil (Paddock et al. (1988), Kellogg

(2014)) and mining (Moel and Tufano (2002)). More importantly, the surveys do not necessarily contradict the empirical findings. Practitioners may

actually apply real option thinking in a heuristic way. E.g. Kairys and Valerio (1997) study option trading in New York during the 1870s and concluded

that financial markets exhibited a degree of sophistication that would easily

be recognized by investors of today. Moore and Juh (2006)) examining equity

options in early twentieth century Johannesburg concluded that \investors

appear to have been able to process relevant information and come close to

determining the fair value of derivatives." Likewise, today's managers may

be able to exercise real options in a timely fashion. It is quite plausible that

managers get valuations \approximately right". After all, competition ensures

that managers who get it wrong (too often) lose their job. McDonald (2000)

shows that commonly used ules of thumb" such as hurdle rates, profitability indexes and payback rules can proxy for the use of more sophisticated ROV

and provide close-to-optimal investment decisions for a variety of parameters.

Second, ROV being a sophisticated valuation method can sometimes lead

to a false sense of accuracy and focus the attention too much on the valuation

model, and too little on the model assumptions and inputs. If the model

assumptions and inputs are wrong, then ROV will get the valuation \precisely

wrong".

Third, the identification and valuation of real options involves a certain

amount of subjectivity. Given that real options can only add value, one has to

be careful not to inflate values. For example, ROV was used during the high

tech bubble of the late nineties in order to explain and justify the in

prices of some internet and high tech _rms that yet had to generate profits.

Also real options could be used by unscrupulous managers in order to push

through pet projects that destroy firm value.

Fourth, ROV is in essence a dynamic version of NPV in which the discount

rate gets adjusted as uncertainty unfolds, real options get exercised and the riskiness of the project changes. This insight goes back to Black Scholes (1973)

who showed that options can be valued using the CAPM framework but with

an option beta that varies over time and depends on the option elasticity.

ROV and capital budgeting methods more generally tend to focus on the dis-

counting process (i.e. the denominator) and how discounting is affected by

the _rm's capital structure and risk. Relatively little attention is paid to the

cash ow process (i.e. the numerator) and its statistical and economic properties. The usual justification is that predicting cash flows such as sales and

costs is beyond the scope of finance and falls under the remit of marketing

or operations management. But the sales and production manager may pro-

duce sales and cost predictions not with a (exclusive) view to generate precise

and unbiased valuations. Often their forecasts may be produced for different

purposes such as planning, budgeting and managerial compensation. These

forecasts may not be ideal for valuation purposes and lead to real option exercise policies and valuations that are biased. Likewise, annual accounts are

not constructed with a view to give us the most accurate valuation of a _rm's

assets in place. Figuring out how available data can best be used for valuation

purposes remains a big challenge.

Fifth, ROV gets intractable when multiple state variables are introduced

(Bellman's so-called curse of dimensionality). This poses serious challenges for

investments with multiples sources of uncertainty. The Least-Squares Monte

Carlo model (LSM model) by Longstaff and Schwartz (2001) has produced

a significant breakthrough on this front. The LSM model combines Monte

Carlo simulation, dynamic programming and statistical regression in a

procedure to value nearly all types of corporate investments. But, the use of

complex statistical techniques and software packages may turn the valuation

process into a black box. Due to its numerical nature, the LSM model is not

that useful for the development of theoretical work.

Finally, although real option methods value managerial flexibility (i.e. how

managers optimally respond to economic shocks), ROV due to its complexity is not a particularly flexible valuation framework if managers cannot in

advance identify the _rm's options but have to discover and exercise them

as uncertainty unfolds. Inserting newly discovered real options into an existing valuation framework is not always straightforward due to the complicated

interactions between real options and assets in place.

1.What are the strengths of real option valuation? Describe each.

2. What are the weaknesses and challenges of real option valuation? Describe each.