This paper describes a Bayesian nonparametric approach to volatility estimation. Volatility is assumed to follow a superposition of an infinite number of Ornstein–Uhlenbeck processes driven by a compound Poisson process with a parametric or nonparametric jump size distribution. This model allows a wide range of possible dependencies and marginal distributions for volatility. The properties of t...

A small strand of recent literature is occupied with identifying simultaneity in multiple equation systems through autoregressive conditional heteroscedasticity. Since this approach assumes that the structural innovations are uncorrelated, any contemporaneous connection of the endogenous variables needs to be exclusively explained by mutual spillover effects. In contrast, this paper allows for ...

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This paper presents a stock trading method by combining the filter rule and the decision tree technique. The filter rule, having been widely used by investors, is used to generate candidate trading points. These points are subsequently clustered and screened by the application of a decision tree algorithm C4.5. Compared to previous literature that applied such a combination technique, this rese...

As the volume and interconnectedness of corporate data grows, data quality is becoming a business competency essential to success. Existing methods for managing data quality do not scale up to large volumes of data in a way that is directly manageable by the owner of the data. For the past two years a new breed of data quality products, built on applied AI techniques, are empowering non-technic...

Recently, stock exchanges have altered their trading fees to subsidize liquidity by offering “make” rebates for providing liquidity through limit orders and charging “take” fees for consuming liquidity via marketable orders, leading to debate regarding the impact of these fees on market quality. Using an exogenous experiment performed by NASDAQ in 2015, I employ difference-in-differences analys...

The paper addresses a method for spreading messages in social networks through an initial acceleration by “spreading groups”. These groups start the spread which eventually reaches a larger portion of the network. The use of spreading groups creates a final flow which resembles the spread through the nodes with the highest level of influence (opinion leaders). While harnessing opinion leaders t...

This paper examines the intertemporal relation between downside risk and expected stock returns. Value at Risk (VaR), expected shortfall, and tail risk are used as measures of downside risk to determine the existence and significance of a risk-return tradeoff. We find a positive and significant relation between downside risk and the portfolio returns on NYSE/AMEX/Nasdaq stocks. VaR remains a su...

Bu çalışma; farklı dönemlerde Nasdaq borsasında işlem gören teknoloji şirket hisse senedi fiyatları ile kripto paraların değerleri arasındaki ilişkiyi araştırmak amacıyla yapılmıştır. Çalışmada regresyon ve korelasyon analizi kullanılmıştır. Çalışma bulgularına göre; COVID-19 dönemi sonrasında arasında pozitif yönlü kuvvetli bir ilişki olduğunu söylemek mümkündür. Ancak öncesinde anlamlı buluna...

Volatility forecasting is one of the main issues in the financial econometrics literature. Volatility measures may be derived from statistical models for conditional variance, or from option prices. In recent times, indices have been suggested which summarize the implied volatility of widely traded market index options. One such index is the so-called VXN, an average of 30-day ahead implied vol...