A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high-frequency returns coupled with relatively simple reduced-form time series modeling procedures. Building on...

This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution...

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning...

Demonstration of the omnipresence of noise in financial correlation/covariance matrices revealed by means of random matrix theory, a branch of probability theory.Introduction of the Shannon entropy as a measure of noise in correlation matrices. Demonstration of substantial entropy decrease as a...

Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics) of Market Memory Trading, L.L.C. present, in a series of nine (9) white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the...

Instead of relying solely on data of a single time series it is possible to use information of parallel, similar time series to improve prediction quality. Our data set consists of microeconomic data of daily store deposits from a large number of different stores. We analyze how prediction...

Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing...

This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies...

In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...