In recent years, the simultaneous online auction (SOA) has become a popular mechanism for selling heterogeneous items such as antiques, art, furniture, and collectables. These auctions sell multiple items concurrently to a selected group of bidders who often participate in multiple auctions simultaneously. Such bidder behavior creates a unique competitive environment where bidders compete against each other both within the same auction as well as across different auctions. In this chapter, we present a novel dynamic forecasting approach for predicting price in ongoing SOAs. Our proposed model generates price forecasts from the time of prediction until auction close. It updates its forecasts in real-time as the auction progresses based on newly arriving information, price dynamics and competition intensity. Applying this method to a dataset of contemporary Indian art SOAs, we find high predictive accuracy of the dynamic model in comparison to more traditional approaches. We further investigate the source of the predictive power of our model and find that price dynamics capture bidder competition within and across auctions. The importance of this finding is both conceptual and practical: price dynamics are simple to compute at high accuracy, as they require information only from the focal auction and are therefore a parsimonious representation of different forms of within-auction and between-auction competition. Keywords: Dynamic Price Forecasting, Functional Data Analysis, Simultaneous Online Art Auctions; Within-auction bidder competition; Between-auction bidder competition.