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Category Archives: C53

Geo-Spatial Modeling of Online Ad Distributions

By Mitchel Gorecki

The purpose of this document is to demonstrate how spatial models can be integrated into purchasing decisions for real-time bidding on advertising exchanges to improve ad selection and performance. Historical data makes it very apparent that some neighborhoods are much more interested in some ads than others. Similarly, some neighborhoods are also much more interested in some online domains than others, meaning viewing habits across domains are not equal. Basic data analysis shows that neighborhoods behave in predictable ways that can be exploited using observed performance information. This paper demonstrates how it is possible to use spatially correlated information to better optimize advertising resources.

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Advisor: Charles Becker | JEL Codes: C3, C33, C53, M37 | Tagged: Ad Distribution, Advertising, Online, Real Time Bidding, Spatial

Forecasting Beta Using Conditional Heteroskedastic Models

By Andrew Bentley

Conventional measurements of equity return volatility rely on the asset’s previous day closing price to infer the current level of volatility and fail to incorporate information concerning intraday influntuctuations. Realized measures of volatility, such as the realized variance, are able to integrate intraday information by utilizing high-frequency data to form a very accurate measure of the asset’s return volatility. These measures can be used in parallel with the traditional definition of the Capital Asset Pricing Model (CAPM) beta to better predict the time-varying systematic risk of an asset. In this analysis, realized measures were added to the General Autoregressive Conditional Heteroskedastic (GARCH) framework to form a predictive model of beta that can quickly respond to rapid changes in the level of volatility. The ndings suggest that this predictive beta is better able to explain the stylized characteristics of beta and is a more accurate forecast of the realized beta than the GARCH model or the benchmark Autoregressive Moving-Average (ARMA) model used as a comparison.

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JEL Codes: C0, C3, C03, C32, C53, C58 | Tagged: Beta, GARCH, GARCHX, High-Frequency Data, Realized Varience

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