Volatility term structures in commodity markets

Abstract

In this study, we comprehensively examine the volatility term structures in commodity markets. We model state‐dependent spillovers in principal components (PCs) of the volatility term structures of different commodities, as well as that of the equity market. We detect strong economic links and a substantial interconnectedness of the volatility term structures of commodities. Accounting for intra‐commodity‐market spillovers significantly improves out‐of‐sample forecasts of the components of the volatility term structure. Spillovers following macroeconomic news announcements account for a large proportion of this forecast power. There thus seems to be substantial information transmission between different commodity markets.

A Study of Stock Market Predictability Based on Financial Time Series Models

“…is literature used nearly 10 years of stock data from the market as a dataset to construct a long and shortterm memory network with a multiclass feature system, which overcomes the common drawback of local minimum rather than the global minimum in neural network models and compares it with commonly used models such as CNN, RNN, and multilayer perceptron (MLP), and the empirical findings show that LSTM achieves superior forecasting performance with prediction, high accuracy, and fast convergence and has a wide range of application prospects. e authors in [18] quantified investor sentiment indices by BiLSTM used CLSTM to classify the sentiment of word features of news and constructed a hybrid LSTM to predict stock market trend changes. e authors in [19] applied machine learning algorithms to time series analysis, based on the improved XGBoost algorithm, phase space reconstruction optimization method, and improved SVR model for stock index regression prediction, and the experimental results showed that the machine learning algorithm can significantly achieve the classification prediction in the stock index, but the numerical prediction effect is not significant.…”

Anomalies in Commodity Futures Markets

“…25 Further, K and S denote the strike and spot prices, respectively, where C(K) and P (K) represent the call and put prices at strike price K, respectively. 26 In the next step, we follow Hollstein et al (2020b) and compute the corresponding option prices, using the Black (1976) 25 We follow the literature and use the Ivy curve from OptionMetrics to proxy for the interest rate. 26 We focus on out-of-the-money (OTM) option prices.…”

Economic determinants of oil futures volatility: A term structure perspective

“…The short-end factor captures the short-term variations, 5 while the shape and long-end factors have the ability to capture medium-and long-term variations in a flexible way. The second strand studies the relation between commodity spot or future prices, and spot volatility with economic variables by using equilibrium models (Casassus et al (2013), Chiang et al (2015), Gao, Hitzemann, Shaliastovich, and Xu (2017), Heath (2019)), or VaR models (Alquist and Kilian (2010), Symeonidis, Prokopczuk, Brooks, and Lazar (2012), Silvennoinen and Thorp (2013), Kilian and Murphy (2014), Cheng and Xiong (2014), Anzuini, Pagano, and Pisani (2015), Kilian (2016), Prokopczuk, Stancu, and Symeonidis (2019), Hollstein, Prokopczuk, and Wuersig (2019)). Typically, short-term price variations are associated with demand variables in the physical markets and trading variables in the futures markets.…”