Relationship between uncertainty in the oil and stock markets before and after the shale gas revolution: Evidence from the OVX, VIX, and VKOSPI volatility indices

Choi, Sun‐Yong; Hong, Changsoo

doi:10.1371/journal.pone.0232508

Cited by 21 publications

(15 citation statements)

References 147 publications

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“…rough this subperiod analysis, we investigate the change in the forecasting performance of all methods in both high-variance and relatively low-variance data. is kind of subperiod analysis is common in other studies [48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.

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Section: Introductionmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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“…e cross-market spillover effect we found can give some advice to correctly diversify investment and reduce risks. (1) e empirical results show that WTI and Brent oil future have more spillover to both spot price and future price. e oil future played an important role in the energy market and economic.…”

Section: Discussionmentioning

confidence: 96%

“…where y t represents the historical logarithmic return of crude oil and stock prices. Equation (1) shows the basic model with the known y t and the unknown ψ t which are unobservable variables. We combined the Ganger-MSV model and the dynamic-MSV model as Yu and Meyer [33] and replace N-distribution with T-distribution:…”

Section: Multivariate Stochastic Volatility Modelmentioning

confidence: 99%

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An Empirical Analysis of Oil and Stock Markets’ Volatility Based on the DGC-MSV-t Model

Zhang

Zhuang

Liu

2021

Journal of Mathematics

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We investigate the spillover effect between crude oil future prices, crude oil spot prices, and stock index by using the multivariate stochastic volatility model. These tests between each market show the significant Granger causes of spillover effect. More and more evidences show that the crude oil price has been affected by other financial markets. The oil future played an important role in the energy market. WTI and Brent oil future have more spillover effect than INE oil future. The result shows that S&P stock market is more sensitive to the oil price than Shanghai stock market. The cross-market spillover effect we found can give some advices for the investor of oil and stock market. DIC test shows that DGC-MSV-t is considered effective and more accurate.

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“…Testing is also performed using subperiod analysis to investigate whether data deviations and outliers affect model training. Such subperiod analysis has been commonly implemented in previous studies (Sharma et al [36], García and Kristjanpoller [37], Ramos-Pérez et al [17], and Choi and Hong [38]). Specifically, we split the entire sample period into three subperiods called Period 1 (January, 2010 to December, 2015), Period 2 (January, 2016 to December, 2016), and Period 3 (January, 2017 to December, 2019).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder‐LSTM Techniques

Jung

Choi

2021

Complexity

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Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange (FX) market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory (LSTM) and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index (FXVIX) as a measure of FX volatility. In particular, the three major FXVIX indices (EUVIX, BPVIX, and JYVIX) from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting.

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Relationship between uncertainty in the oil and stock markets before and after the shale gas revolution: Evidence from the OVX, VIX, and VKOSPI volatility indices

Cited by 21 publications

References 147 publications

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

An Empirical Analysis of Oil and Stock Markets’ Volatility Based on the DGC-MSV-t Model

Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder‐LSTM Techniques

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