Portfolio Risk Modelling in Venture Debt

Sammanfattning: This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. The main framework revolves around driving a Monte Carlo simulation with Copluas to predict future revenue scenarios across a portfolio of early-stage technology companies. Three models for a random Gaussian walk, a Linear Dynamic System and an Autoregressive Integrated Moving Average (ARIMA) time series are implemented and evaluated in terms of their portfolio Value-at-Risk influence. The model performance confirms that modeling portfolio risk in Venture Debt is challenging, especially due to lack of sufficient data and thus a heavy reliance on assumptions. However, the empirical results for Value-at-Risk and Expected Shortfall are in line with expectations. The evaluated portfolio is still in an early stage with a majority of assets not yet in their repayment period and consequently the spread of potential losses within one year is very tight. It should further be recognized that the scope in terms of explanatory variables for sales and model complexities has been narrowed and simplified for computational benefits, transparency and communicability. The main conclusion drawn is that alternative approaches to model Venture Debt risk is fully possible, and should improve in reliability and accuracy with more data feeding the model. For future research it is recommended to incorporate macroeconomic variables as well as similar company analysis to better capture macro, funding and sector conditions. Furthermore, it is suggested to extend the set of financial and operational explanatory variables for sales through machine learning or neural networks.