This dissertation aims at improving econometric methodologies used in the field of economics of alternative assets, with a specific focus on heterogeneous goods, more particularly goods exchanged in the fine art market and fine wines. This thesis covers new methods to track market prices through time, as well as volatility of heterogeneous assets. Statistically, this requires a methodology able to extract a common trend in prices from prices of goods that exhibit different characteristics. A challenge of this problem is to design an estimator that is relevant and coherent with applications of economists and business practitioners. Such applications include the use of estimated price indices to compute returns and volatility of an investment in art and comparing it with other financial indices. Two estimators are suggested: first, a generalized Nadaraya-Watson estimator allows estimating prices as a continuous function of time. Second, a Kalman-filter based estimator provides a consistent estimator of marginal impact of time on prices as well as an unbiased estimator of volatility of the underlying market. It is shown that these estimators have better statistical properties than estimators currently used in the applied literature, while presenting less constraining assumptions. Finally, the methodology is applied to tackle a regulatory requirement in the alternative funds industry.