Backtesting: The process of using historical data to test the performance of a predictive model.
Machine learning: A branch of artificial intelligence that uses algorithms to learn from data and make predictions or decisions without explicit programming.
Predictive modeling: The process of using statistical techniques and algorithms to analyze historical data and make predictions about future events or outcomes.
Risk-adjusted return: The return on an investment after adjusting for the level of risk.
Valuation Modeling Approaches:
Black-Scholes Model: This method is used to value a financial asset portfolio using option pricing theory, taking into account the volatility of the portfolio’s cash flows.
Discounted Cash Flow (DCF) Model: This method calculates the present value of future cash flows generated by an asset portfolio using a discount rate that reflects the risk of the portfolio.
Comparable Transactions: This method compares the subject financial asset portfolio to similar portfolios that have been recently sold in the market.
Mark-to-Market: This technique compares the value of the financial asset portfolio to the current market value of similar assets.
Mark-to-Model: This method uses a proprietary model to value the financial asset portfolio, taking into account factors such as credit risk and cash flow.
Monte Carlo Simulation: This method uses computer simulations to model the potential performance of the financial asset portfolio under different economic scenarios.
Risk-Adjusted Return on Capital (RAROC) Model: This technique calculates the return on capital of the financial asset portfolio adjusted for risk.
Statistical Modeling: This technique uses historical data to predict future performance of the financial asset portfolio. It can include factors such as credit score, loan-to-value ratio, and geographic location.
