Browsing by Author "Sizova, Natalia"

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    Approximate dynamic factor models for mixed frequency data
    (2015-10-15) Zhao, Xin; Ensor, Katherine; Kimmel, Marek; Sizova, Natalia
    Time series observed at different temporal scales cannot be simultaneously analyzed by traditional multivariate time series methods. Adjustments must be made to address issues of asynchronous observations. For example, many macroeconomic time series are published quarterly and other price series are published monthly or daily. Common solutions to the analysis of asynchronous time series include data aggregation, mixed frequency vector autoregressive models, and factor models. In this research, I set up a systematic approach to the analysis of asynchronous multivariate time series based on an approximate dynamic factor model. The methodology treats observations of various temporal frequencies as contemporaneous series. A two-step model estimation and identification scheme is proposed. This method allows explicit structural restrictions that account for appropriate temporal ordering of the mixed frequency data. The methodology consistently estimates the dynamic factors, however, no prior knowledge on the factors is required. To ensure a computationally efficient robust algorithm and model specification, I make use of modern penalized likelihood methodologies. The fitted model captures the effects of temporal relationships across the asynchronous time series in an interpretable manner. The methodology is studied through simulation and applied to several examples. The simulations and examples demonstrate good performance in model specification, estimation and out-of-sample forecasting.
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    Hotel Management in the Digital Age: Empirical Studies of Reputation Management and Dynamic Pricing
    (2016-04-01) Wang, Yang; Pazgal, Amit; Kamakura, Wagner; Kalra, Ajay; Sizova, Natalia
    Although a hotel’s basic purpose of providing a temporary place of lodging has not changed fundamentally over the course of history, the industry has continuously evolved with the newest innovations in architecture, technology, and culture. The most recent evolution is the digitization of the hotel marketplace. This thesis investigates two areas heavily influenced by the digital marketplace – online reputation management and dynamic pricing. The first study of this dissertation addresses one important facet of reputation management. How do managers’ responses to online reviews alter the opinion of subsequent reviewers? By analyzing a dataset of approximately 17 million hotel reviews, we demonstrate that managers’ responses can change the opinion of subsequent reviewers, but not always in a positive way. Responses to negative reviews generally improve subsequent opinion but responses to positive reviews can sometimes negatively influence subsequent opinion. A deep learning topic analysis of response and review texts reveals that tailored responses to positive reviews can actually negatively impact subsequent opinion. The findings in this study are shown to be consistent with the predictions of reactance theory. The second study seeks to uncover the degree to which managers’ pricing heuristics are optimal. Analyzing a year’s worth of spot prices for a focal hotel and its two competitors in the Las Vegas market, we show that managers do not price optimally in two peculiar ways. First, managers are able to set close-to-optimal average prices during off-season but dramatically underprice during peak-season. This result is consistent with agency theory that suggest the observable binary outcome of selling out the hotel may attenuate managers’ aggressiveness in setting prices. Second, managers, like untrained experimental subjects in prior literature, tend to make price changes that are too small. Furthermore, this study investigates the revenue gains due anticipating competitors’ pricing behavior and mean reversion tendencies in online reviews.
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    Risk and return: Long-run relations, fractional cointegration, and return predictability
    (Elsevier, 2013) Bollerslev, Tim; Osterrieder, Daniela; Sizova, Natalia; Tauchen, George
    Univariate dependencies in market volatility, both objective and risk neutral, are best described by long-memory fractionally integrated processes. Meanwhile, the ex post difference, or the variance swap payoff reflecting the reward for bearing volatility risk, displays far less persistent dynamics. Using intraday data for the Standard & Poor's 500 and the volatility index (VIX), coupled with frequency domain methods, we separate the series into various components. We find that the coherence between volatility and the volatility-risk reward is the strongest at long-run frequencies. Our results are consistent with generalized long-run risk models and help explain why classical efforts of establishing a naïve return-volatility relation fail. We also estimate a fractionally cointegrated vector autoregression (CFVAR). The model-implied long-run equilibrium relation between the two variance variables results in nontrivial return predictability over interdaily and monthly horizons, supporting the idea that the cointegrating relation between the two variance measures proxies for the economic uncertainty rewarded by the market.