This study analyzes patent trends and volatilities for three heterogeneous quality patents in the Taiwan patent system from January 1973 to June 2006. The estimated models are symmetric GARCH and asymmetric EGARCH, providing full sample, rolling sample, and out-of-sample evidence. Three different patent types exhibit increasing trends, using monthly time series data from our samples. ”New design” patents also show time-varying volatility but other types of patents fail to reject the ARCH LM test. Findings show the asymmetric EGARCH model suitable for “new design” patent type through out of sample forecasts.This study analyzes patent trends and volatilities for three heterogeneous quality patents in the Taiwan patent system from January 1973 to June 2006. The estimated models are symmetric GARCH and asymmetric EGARCH, providing full sample, rolling sample, and out-of-sample evidence. Three different patent types exhibit increasing trends, using monthly time series data from our samples. ”New design” patents also show time-varying volatility but other types of patents fail to reject the ARCH LM test. Findings show the asymmetric EGARCH model suitable for “new design” patent type through out of sample forecasts.This study analyzes patent trends and volatilities for three heterogeneous quality patents in the Taiwan patent system from January 1973 to June 2006. The estimated models are symmetric GARCH and asymmetric EGARCH, providing full sample, rolling sample, and out-of-sample evidence. Three different patent types exhibit increasing trends, using monthly time series data from our samples. ”New design” patents also show time-varying volatility but other types of patents fail to reject the ARCH LM test. Findings show the asymmetric EGARCH model suitable for “new design” patent type through out of sample forecasts.