We consider the problem of online pricing with offline data, where the decision maker has in hand pre-existed offline data and then takes online sequential actions to maximize expected cumulative revenue as well as learning the optimal pricing. We focus on a simple and specific problem setting...

We propose estimators and analyze their large-sample distribution for two problems, data-oriented expected performance evaluation and data-oriented stochastic optimization, in presence of a specific class of non-stationarities in the observed data. The considered non-stationarities reflect...

We build a new unified modeling and analysis framework for a broad class of online matching problems. The proposed unified framework encompasses a number of classical online matching problems and accommodates three practical features: reusable resources, network resources and decaying rewards....

In real-time decision-making problems for complicated stochastic systems, a covariate that reflects the state of the system is observed in real time and a state-dependent decision needs to be made immediately to optimize some system performance. Such system performances, for complicated...

In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we...

We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e....