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We show that if a lead-lag relation exists between the option and spot markets, the implied volatility in option prices can be biased depending on the level of the true volatility; that is, the higher the true volatility, the more upward biased the implied volatility will be. We then test the theoretical conjecture, using intraday transactions data of the S&P 500 stock index and its options. The empirical results show that the S&P 500 index option market leads the cash index, and that the bias of the implied volatility due to the lead-lag relation is statistically significant, confirming our theoretical conjecture.

This paper provides a structural model to value startup companies and determine the optimal level of research and development (R&D) spending by these companies.

This paper describes a new variant of float-the-money options, which can act as a financial instrument for financing R&D expenses for a specific time horizon or development stage, allowing the investor to share in the startup's value appreciation over that duration. Another innovation of this paper is that it develops a structural model for evaluating optimal level of R&D spending over a given time horizon. The paper deploys the Gompertz-Cox model for the R&D project outcomes, which facilitates investigation of how increased level of R&D input can enhance the company's value growth.

The author first introduces a time-varying drift term into standard Black-Scholes model to account for the varying growth rates of the startup at different stages, and the author interprets venture capital's investment in the startup as a “float-the-money” option. The author then incorporates the probabilities of startup failures at multiple stages into their financial valuation. The author gets a closed-form pricing formula for the contingent option of value appreciation. Finally, the author utilizes Cox proportional hazards model to analyze the optimal level of R&D input that maximizes the return on investment.

The integrated contingent claims model links the change in the financial valuation of startups with the incremental R&D spending. The Gompertz-Cox contingency model for R&D success rate is used to quantify the optimal level of R&D input. This model assumption may be simplistic, but nevertheless illustrative.

Once supplemented with actual transaction data, the model can serve as a reference benchmark valuation of new project deals and previously invested projects seeking exit.

The integrated structural model can potentially have much wider applications beyond valuation of startup companies. For instance, in valuing a company's risk management, the level of R&D spending in the model can be replaced by the company's budget for risk management. As another promising application, in evaluating a country's economic growth rate in the face of rising climate risks, the level of R&D spending in this paper can be replaced by a country's investment in addressing climate risks.

This paper is the first to develop an integrated valuation model for startups by combining the real-world R&D project contingencies with risk-neutral valuation of the potential payoffs.

Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information operations. With Information Options, the underlying assets are information assets and the rules governing exercise are based on the realities of the information realm (infosphere). Information Options can be modeled as options to “purchase” information assets by paying the cost of the information operations involved. Information Options arise at several stages of value creation. The initial stage involves observation of physical phenomena with accompanying data capture. The next refinement is to organize the data into structured databases. Then bits of information are selected from storage and synthesized into an information product (such as a management report). Next, the information product is presented to the user via an efficient interface that does not require the user to be a field expert. Information Options are similar in concept to real options but substantially different in their details, since real options have physical objects as the underlying assets and the rules governing exercise are based on the realities of the physical world. Also, while exercising a financial option typically kills the option, Information Options may include multiple exercises. Information Options may involve high volatility or jump processes as well, further enhancing their value. This chapter extends several important real option applications into the information realm, including jump process models and models for valuing options to synthesize any of n information items into any of m output assets.

Option models have provided insight into the value of flexibility to switch from one state to another (such as switching a mine or refinery from operating to closed status). More complex flexible processes offer multiple possibilities for switching states. A fabrication facility, for example, may offer options to shift from the current status to any of several alternatives (reflecting reconfiguration of basic facilities to accommodate different operating processes with different outputs). New algorithms enable practical application of complex option pricing models to flexible facilities, improving analysts’ ability to draw sound conclusions about the effects of flexibility and innovativeness on share value. Such models also apply for options with information items as the underlying assets. Information organizations such as oil exploration and development companies may include options to shift from the current capability to any of several alternatives reflecting added abilities to handle new information sources or apply the organization’s talents in new ways. In the case of either physical or information processing, careful attention to estimating the matrix of correlations among the values of potential alternative states allows explicit integration of financial analysis and strategic analysis – especially the influence of substitutes and the anticipated reactions of competitors, suppliers, and potential new entrants.

While asset/liability management (A/L M) has been applied widely by insurers for 15 years, it has had mixed results. This article describes how initial efforts were unsuccessful, due to the focus on accounting values rather than economic values. The author asserts that insurers must rectify this misstep before A/L M can become a useful tool for them. Several forces are combining to ensure that this takes place in the near future.

In early January 2008, a senior VP with LAAMCO, a fund of hedge funds known for alternative investments, was conducting due diligence on an equity market-neutral hedge fund. The hedge fund used an option strategy known as a collar (also known as a bull spread or split-strike conversion). The track record of the hedge fund had been stellar. The fund's performance had not only beaten that of the S&P 500 Index over the same period but had done so with much lower monthly return volatility. As part of the due diligence, it was necessary to backtest the collar strategy and try to quantify how much value the manager, BLM Investment Securities, LLC, (BLM) had added. The case is a disguised representation of an actual hedge fund—the true identity of BLM is revealed to students at the end of the case discussion.