Residual Value Risk Management in Automotive Financing: A Comparative Perspective Inspired by Ferrari Financial Services

The automotive industry is changing rapidly because consumers’ tastes are shifting due to technology and rules are changing. Residual value risk (RVR) is one of the key financial issues in the sector and occurs because a vehicle’s predicted market value at the end of its lease or finance terms does not match the actual amount. This thesis investigates the key determinants of RVR in automotive financing, with a particular focus on the luxury vehicle sector, Ferrari Financial Services (FFS). Luxury automobile residual values are quite often influenced by the financial status of consumers who purchase them. This study used a mixture of qualitative and quantitative data to analyze the industry and forecast RVR. A multiple linear regression model is used to analyze how vehicle-specific factors (brand, mileage, accident experience) and macroeconomic factors (inflation, interest rates, and supply chain disruptions) impact residual value. Also, other predictive models with time-series and MLs (Random Forest, XGBoost, Artificial Neural Network) are evaluated for their effectiveness in improving RVR predictions. The results show that the depreciation of luxury cars is not linear and demand sensitivity as well as limited production issues. Thus, standard RVR models are not enough. Ferrari Financial Services buys back models after an agreed period to mitigate RVR. It also offers guaranteed residual value contracts and selective leases. So, they are doing it all being a bit risk-prone yet smart. Residual values are becoming more sensitive to market volatility, including macroeconomic shocks, EV adoption and regulatory pressure. This dissertation offers a revised risk strategy that automotive financiers can use. The strategy utilizes predictive analytics and leasing policy regarding the RVR risk associated with the industry. This research has implications for financial institutions, manufacturers, and policymakers to enhance residual value estimation, reduce financial exposure, and improve competitiveness of leasing operations in an evolving automotive market.