ABSTRACT
Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion.
ABSTRACT
The Internet of Things (IoT) refers to interconnected devices and objects that collect and transmit data via the Internet. The IoT is an evolving technology, promising to revolutionise industries, but also demanding far-reaching policy changes (e.g. in terms of data security and privacy), which involve significant resources. This paper reviews the evidence on uptake and the economic impact of the IoT during its early years of inception. It uses a growth accounting framework to evaluate the likely impact of the IoT on productivity. Estimating the effect of new technologies on productivity is an essential step in evaluating the 'economic value-added', justifying resources dedicated to facilitating the adoption of innovations. We find a positive impact of the IoT on productivity, however relatively small, given that the IoT is still at an early stage of development. We present projections on the impact of the IoT under a number of scenarios.
ABSTRACT
This paper examines the stylized facts, correlation and interaction between volatility and returns at the 5-minute frequency for gold, silver, platinum and palladium from May 2000 to April 2015. We study the full sample period, as well as three subsamples to determine how high-frequency data of precious metals have developed over time. We find that over the full sample, the number of trades has increased substantially over time for each precious metal, while the bid-ask spread has narrowed over time, indicating an increase in liquidity and price efficiency. We also find strong evidence of periodicity in returns, volatility, volume and bid-ask spread. Returns and volume both experience strong intraday periodicity linked to the opening and closing of major markets around the world while the bid-ask spread is at its lowest when European markets are open. We also show a bilateral Granger causality between returns and volatility of each precious metal, which holds for the vast majority subsamples.