We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term structure and are associated with the time-varying...

Recently, Diebold and Li (2003) obtained good forecasting results for yield curves in a reparametrized Nelson-Siegel framework. We analyze similar modeling approaches for price curves of variance swaps that serve nowadays as hedging instruments for options on realized variance.We consider the...

This paper develops and compares several methods of forecasting the S&P 500 Index using only data based on the closing value and trained over a six-decade data set. The methodologies include a C5.0 decision tree, a neural network, and a group of forecasts based on training set patterns of...

In previous studies, high-frequency data has been used to improve portfolio allocation by estimating the full realized covariance matrix. In this paper, we show that strategies using high-frequency data for measuring and forecasting univariate realized volatility alone can already generate...

The paper by Diebold and Li (2006) has become a benchmark in the yield curve forecasting literature, mostly owing to its excellent out-of-sample results. In this note we investigate the robustness of these outcomes in two different ways: (i) in terms of the arbitrary choices in their forecasting...

This paper first evaluates the volatility modeling in the Bitcoin market in terms of its realized volatility, which is considered to be a reliable proxy of its true volatility. In addition, we also rely on the important work by Patton (2011), which shows good measures for making the forecast...

The Internet Appendix consists of three sections. Section A shows data sources and detailed data processing procedures. In Section B, we outline seven forecasting models. Last, Section C represents the empirical results

This paper explores the possibility of the potential usage of machine learning models in the field of realized volatility forecasting of crude oil with a vast variety of empirical analyses and robustness checks. Although the conventional heterogeneous autoregressive (HAR) model is widely...

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...

This paper sheds light on the differences and similarities in natural gas trading at the National Balancing Point in the UK and the Henry Hub located in the US. For this, we analyze traders' expectations and implement a mechanical forecasting model that allows traders to predict future spot...