I'm working on a model to estimate CDS prices, and want to backtest it against a historical timeseries. What are some error/goodness of fit measures that I can use for this purpose outside of RMSE?
I'm generally unfamiliar with this kind of metric, and in my research I've only found comparison measures, that yield a 'best' model relative to others, such as AIC and BIC. In this case I only have the one model, and want to produce some standalone measure of 'accuracy'.
A standard approach here is to build a hedge implied by your model and evaluate its hedging performance when it comes to daily rebalancing of your CDS portfolio... I assume daily data is the highest frequency you've got.
You are doing finance, right? So in addition to regular statistical goodness-of-fit measures, you should always try PNL-based goodness-of-fit measures, whether it's Sharpe ratio or the "replication accuracy" measure I have proposed.
Having that said, in some simple situations PNL-based measures can be proven to be deterministic functions of the good old statistical goodness-of-fit measures.
