On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors

Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved. In this paper, we follow a causal perspective to systematically look into these three demons in finance. Specifically, we reexamine these issues in the context of causality, which gives rise to a novel and inspiring understanding of how the issues can be addressed. Following this perspective, we provide systematic solutions to these problems, which hopefully would serve as a foundation for future research in the area

THE TRANSMISSION OF PRICE VOLATILITY IN THE BEEF MARKETS

This paper reconsiders the implications of efficient markets for transmission of price volatility across markets. Tests of volatility transmission are based on conditional variances. Results are reported for key grain and beef markets. Transmission across cash, futures, and options is considered.Cointegration, GARCH, Market Efficiency, Beef Markets, Demand and Price Analysis, Livestock Production/Industries,

FED CATTLE SPATIAL TRANSACTIONS PRICE RELATIONSHIPS

Delineation of geographic markets for fed cattle is essential in monitoring price behavior and determining geographic markets. This study uses transactions data from 28 U.S. fed cattle slaughter plants to determine the extent of the geographic market for fed cattle. Results indicate a national market for fed cattle with prices across most plants cointegrated. In addition, price discovery originates predominantly at plants located in Nebraska, and typically one-third of the total price adjustment to spatial integration occurs in one day.Cointegration, Relevant market, Spatial prices, Demand and Price Analysis, Livestock Production/Industries,

Automated Discovery in Econometrics

Our subject is the notion of automated discovery in econometrics. Advances in computer power, electronic communication, and data collection processes have all changed the way econometrics is conducted. These advances have helped to elevate the status of empirical research within the economics profession in recent years and they now open up new possibilities for empirical econometric practice. Of particular significance is the ability to build econometric models in an automated way according to an algorithm of decision rules that allow for (what we call here) heteroskedastic and autocorrelation robust (HAR) inference. Computerized search algorithms may be implemented to seek out suitable models, thousands of regressions and model evaluations may be performed in seconds, statistical inference may be automated according to the properties of the data, and policy decisions can be made and adjusted in real time with the arrival of new data. We discuss some aspects and implications of these exciting, emergent trends in econometrics.Automation, discovery, HAC estimation, HAR inference, model building, online econometrics, policy analysis, prediction, trends

Evaluating temporal observation-based causal discovery techniques applied to road driver behaviour

Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity typically seen during online usage in autonomous agent domains. Meanwhile, interventional techniques are not always feasible due to domain restrictions. In order to better explore the issues facing observational techniques and promote further discussion of these topics we carry out a benchmark across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets in addition to those generated synthetically we highlight where improvements need to be made in order to facilitate the application of causal discovery techniques to the aforementioned use-cases. Finally, we discuss potential directions for future work that could help better tackle the difficulties currently experienced by state of the art techniques