The Genesis of Venture Capital - Lessons from the German Experience

Why does venture capital work in some countries but not in others? This clinical study of the first German venture capital firm examines the difficulties of creating a venture capital market in a bank-based financial system. The analysis identifies the problem of creating appropriate governance structures to protect investor returns. It exposes the difficulties of established banks - not to mention government - to devise venture investment strategies. It identifies the availability of high quality entrepreneurs as a critical complement. And it provides a reinterpretation of the hypothesis of Black and Gilson (1997), arguing that the existence of an active stock market is a necessary, but by no means sufficient condition for the development of venture capital.

Volatility and the role of order book structure

There is much literature that deals with modeling and forecasting asset return volatility. However, much of this research does not attempt to explain variations in the level of volatility. Movements in volatility are often linked to trading volume or frequency, as a reflection of underlying information flow. This paper considers whether the state of an open limit order book influences volatility. It is found that market depth and order imbalance do influence volatility, even in the presence of the traditional volume related variables.Realized volatility, bi-power variation, limit order book, market microstructure, order imbalance

Are combination forecasts of S&P 500 volatility statistically superior?

Forecasting volatility has received a great deal of research attention. Many articles have considered the relative performance of econometric model based and option implied volatility forecasts. While many studies have found that implied volatility is the preferred approach, a number of issues remain unresolved. One issue being the relative merit of combination forecasts. By utilising recent econometric advances, this paper considers whether combination forecasts of S&P 500 volatility are statistically superior to a wide range of model based forecasts and implied volatility. It is found that combination forecasts are the dominant approach, indicating that the VIX cannot simply be viewed as a combination of various model based forecasts.Implied volatility, volatility forecasts, volatility models, realized volatility, combination forecasts.

Forecasting stock market volatility conditional on macroeconomic conditions.

This paper presents a GARCH type volatility model with a time-varying unconditional volatility which is a function of macroeconomic information. It is an extension of the SPLINE GARCH model proposed by Engle and Rangel (2005). The advantage of the model proposed in this paper is that the macroeconomic information available (and/or forecasts)is used in the parameter estimation process. Based on an application of this model to S&P500 share index returns, it is demonstrated that forecasts of macroeconomic variables can be easily incorporated into volatility forecasts for share index returns. It transpires that the model proposed here can lead to significantly improved volatility forecasts compared to traditional GARCH type volatility models.Volatility, macroeconomic data, forecast, spline, GARCH.

A nonparametric approach to forecasting realized volatility

A well developed literature exists in relation to modeling and forecasting asset return volatility. Much of this relate to the development of time series models of volatility. This paper proposes an alternative method for forecasting volatility that does not involve such a model. Under this approach a forecast is a weighted average of historical volatility. The greatest weight is given to periods that exhibit the most similar market conditions to the time at which the forecast is being formed. Weighting occurs by comparing short-term trends in volatility across time (as a measure of market conditions) by the application of a multivariate kernel scheme. It is found that at a 1 day forecast horizon, the proposed method produces forecasts that are significantly more accurate than competing approaches.Volatility, forecasts, forecast evaluation, model confidence set, nonparametric

Testing for nonlinearity in mean in the presence of heteroskedasticity. Working paper #8

This paper considers an important practical problem in testing time-series data for nonlinearity in mean. Most popular tests reject the null hypothesis of linearity too frequently if the the data are heteroskedastic. Two approaches to redressing this size distortion are considered, both of which have been proposed previously in the literature although not in relation to this particular problem. These are the heteroskedasticity-robust-auxiliary-regression approach and the wild bootstrap. Simulation results indicate that both approaches are effective in reducing the size distortion and that the wild bootstrap others better performance in smaller samples. Two practical examples are then used to illustrate the procedures and demonstrate the potential pitfalls encountered when using non-robust tests.nonlinearity in mean, heteroskedasticity, wild bootstrap, empirical size and power

Real-space renormalization group flow in quantum impurity systems: local moment formation and the Kondo screening cloud

The existence of a length-scale $\xi_K\sim 1/T_K$ (with $T_K$ the Kondo temperature) has long been predicted in quantum impurity systems. At low temperatures $T\ll T_K$, the standard interpretation is that a spin-$\tfrac{1}{2}$ impurity is screened by a surrounding `Kondo cloud' of spatial extent $\xi_K$. We argue that renormalization group (RG) flow between any two fixed points (FPs) results in a characteristic length-scale, observed in real-space as a crossover between physical behaviour typical of each FP. In the simplest example of the Anderson impurity model, three FPs arise; and we show that `free orbital', `local moment' and `strong coupling' regions of space can be identified at zero temperature. These regions are separated by two crossover length-scales $\xi_{\text{LM}}$ and $\xi_K$, with the latter diverging as the Kondo effect is destroyed on increasing temperature through $T_K$. One implication is that moment formation occurs inside the `Kondo cloud', while the screening process itself occurs on flowing to the strong coupling FP at distances $\sim \xi_K$. Generic aspects of the real-space physics are exemplified by the two-channel Kondo model, where $\xi_K$ now separates `local moment' and `overscreening' clouds.Comment: 6 pages; 5 figure

A Cholesky-MIDAS model for predicting stock portfolio volatility

This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is flexible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.Cholesky, Midas, volatility forecasts