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Portfolio construction seeks an optimal tradeoff between a portfolio's mean return and its associated risk. Since risk may not be properly described by return volatility, portfolios are optimized in this chapter with respect to various measures of downside risk in an empirical out-of-sample setting. These optimizations are successful for most of the investigated measures when assuming perfect foresight of expected returns. Moreover, some of these findings continue to hold when using more naïve return estimates. The reductions in downside risk are most convincing for semivariance, semideviation, and conditional value at risk (VaR) while VaR and skewness appear useless for portfolio construction purposes.

A number of economists have wanted to measure downside risk, the risk of prices falling, just using information based on negative returns. This has been operationalized by quantities such as semivariance, value at risk and expected shortfall, which are typically estimated using daily returns. This chapter introduces a new measure of the variation of asset prices based on high frequency data, called realized semivariance (RS). Its limiting properties are derived, relating it to quadratic variation and, in particular, negative jumps. It is shown that it has some useful properties in empirical work, enriching the standard ARCH models pioneered by Rob Engle over the last 25 years and building on the recent econometric literature on realized volatility.

The 2007 to 2008 financial crisis has sparked a renewed skepticism of portfolio theory and financial engineering. As a result, key changes are taking place in how investors manage risk from the top down. Asset allocators have become increasingly aware of the pitfalls of a naïve approach to portfolio engineering that relies on the normal distribution and that fails to address downside risk. Additionally, asset classes are no longer the optimal way to look at diversification; instead, risk factor diversification is becoming the focus. This chapter focuses on these key changes. It presents the theoretical foundations behind the risk factor approach to asset allocation, demonstrates how risk concentration leads to tail risk, and analyzes the costs and benefits of tail risk hedging in practice.

Monthly return distributions of alternative assets are generally not normally distributed and typically show significantly smoothed returns, which can lead to an underestimation of risk. Furthermore, portfolio optimization in the mean-variance framework that includes alternative assets is suboptimal. This is because the variance of the return distributions for these investments fails to adequately capture all risks. This chapter provides an estimate of the efficient frontier for portfolios consisting of numerous alternative assets as well as traditional asset classes such as equities and bonds. This process enables incorporating the special characteristics of alternative investments, especially downside risk, in the optimization procedure for mixed-asset portfolios. Within this approach, mixed-asset portfolios containing the majority of alternative investments can be used to illustrate the previously unknown effects of skewness and excess kurtosis on the efficient frontier. The evidence shows that alternative investments are ideally suited to reduce portfolio risk and enhance risk-adjusted performance.

Technologies for the Integration of Manufacturing Applications was a focused research program funded by the Advanced Technology Program. The goals of the publicly-funded privately-performed research funded through this focused program are discussed. The net social benefits of research are quantified using the spillover evaluation method.

Risk associated with a particular macroeconomic variable depends not only on the firm's exposure to it, but also on the degree of uncertainty about the variable, and its relation with other variables causing exposures. The method for risk analysis used here to analyze macroeconomic risk and hedging is Cash Flow at Risk (CFaR). This method is a variation of Value at Risk (VaR). The objective with respect to CFaR is discussed in terms of four factors: (i) the size of the cash flow exposure to an exposure variable; (ii) the degree of uncertainty about the variable; (iii) the relationship between the variable and other risk variables; and (iv) the distribution of costs and benefits associated with particular cash flow outcomes. The last factor could be, for example, potential costs associated with lack of liquidity. The CFaR method is illustrated using actual cash flow data from Norsk Hydro. Effects on CFaR of hedging macroeconomic exposures are estimated.

This chapter argues that the Small Business Innovation Development Act of 1982, and more specifically the Small Business Innovation Research (SBIR) program portion of that Act, is a prime example of public sector entrepreneurship. The chapter begins with a legislative history of the SBIR program and a detailed description of its structure. The chapter then presents an economic analysis of the program using a downside risk model of the SBIR program, before turning to an analysis of the program as an example of public sector entrepreneurship using the conceptual structure in Chapter 3. Finally, an overview of the literature related to the effectiveness of the program is reviewed.