Why is it so Hard to Value Intangibles? Evidence from Investments in High-Technology Start-Ups

The paper uses a range of primary-source empirical evidence to address the question: ‘why is it to hard to value intangible assets?’ The setting is venture capital investment in high technology companies. While the investors are risk specialists and financial experts, the entrepreneurs are more knowledgeable about product innovation. Thus the context lends itself to analysis within a principal-agent framework, in which information asymmetry may give rise to adverse selection, pre-contract, and moral hazard, post-contract. We examine how the investor might attenuate such problems and attach a value to such high-tech investments in what are often merely intangible assets, through expert due diligence, monitoring and control. Qualitative evidence is used to qualify the more clear cut picture provided by a principal-agent approach to a more mixed picture in which the ‘art and science’ of investment appraisal are utilised by both parties alike

Distributional and Poverty Consequences of Globalization: A Dynamic Comparative Analysis for Developing Countries

This study examines the impact of globalization on cross-country inequality and poverty using a panel data set for 65 developing counties, over the period 1970-2008. With separate modelling for poverty and inequality, explicit control for financial intermediation, and comparative analysis for developing countries, the study attempts to provide a deeper understanding of cross country variations in income inequality and poverty. The major findings of the study are five fold. First, a non-monotonic relationship between income distribution and the level of economic development holds in all samples of countries. Second, both openness to trade and FDI do not have a favourable effect on income distribution in developing countries. Third, high financial liberalization exerts a negative and significant influence on income distribution in developing countries. Fourth, inflation seems to distort income distribution in all sets of countries. Finally, the government emerges as a major player in impacting income distribution in developing countries.

Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas

We study the asymmetric and dynamic dependence between financial assets and demonstrate, from the perspective of risk management, the economic significance of dynamic copula models. First, we construct stock and currency portfolios sorted on different characteristics (ex ante beta, coskewness, cokurtosis and order flows), and find substantial evidence of dynamic evolution between the high beta (respectively, coskewness, cokurtosis and order flow) portfolios and the low beta (coskewness, cokurtosis and order flow) portfolios. Second, using three different dependence measures, we show the presence of asymmetric dependence between these characteristic-sorted portfolios. Third, we use a dynamic copula framework based on Creal et al. (2013) and Patton (2012) to forecast the portfolio Value-at-Risk of long-short (high minus low) equity and FX portfolios. We use several widely used univariate and multivariate VaR models for the purpose of comparison. Backtesting our methodology, we find that the asymmetric dynamic copula models provide more accurate forecasts, in general, and, in particular, perform much better during the recent financial crises, indicating the economic significance of incorporating dynamic and asymmetric dependence in risk management.

Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula

We investigate the dynamic and asymmetric dependence structure between equity portfolios from the US and UK. We demonstrate the statistical significance of dynamic asymmetric copula models in modelling and forecasting market risk. First, we construct “high-minus-low" equity portfolios sorted on beta, coskewness, and cokurtosis. We find substantial evidence of dynamic and asymmetric dependence between characteristic-sorted portfolios. Second, we consider a dynamic asymmetric copula model by combining the generalized hyperbolic skewed t copula with the generalized autoregressive score (GAS) model to capture both the multivariate non-normality and the dynamic and asymmetric dependence between equity portfolios. We demonstrate its usefulness by evaluating the forecasting performance of Value-at-Risk and Expected Shortfall for the high-minus-low portfolios. From back-testing, e find consistent and robust evidence that our dynamic asymmetric copula model provides the most accurate forecasts, indicating the importance of incorporating the dynamic and asymmetric dependence structure in risk management.