Implied volatility directional forecasting: a machine learning approach

Vrontos, Spyridon D.; Galakis, John; Vrontos, Ioannis D.

doi:10.1080/14697688.2021.1905869

Cited by 34 publications

(13 citation statements)

References 42 publications

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“…Second, when sampling frequency enhances, one may need to model volatility of the variable of interest. To do so, the state-of-art studies may suggest using machine learning approach (Vrontos et al, 2021). Finally, one should recognize that the macroeconomic data suffer from a small sample size.…”

Section: Discussionmentioning

confidence: 99%

Electricity generation and China's GDP growth under dual control policy

Xia,

Liu

2024

Energy Exploration & Exploitation

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To achieve the carbon neutralization plan by 2060, China implemented the “Dual Control” ( Neng Hao Shuang Kong in Chinese) policy in 2015 by limiting the energy intensity and consumption, leading to forced slowdown of energy-related production and consumptions, which in turn hampered China's economic growth. The economic impact of energy shortage has been discussed in such studies as Cheng et al., the economic consequences of the new policy remain unanswered. In this article, we select China's provincial GDP and electricity generation data from 2001 to 2021 to perform causality tests and a double log model which delineates the nexus between GDP growth and electricity generation. We find that (a) electricity generation growth Granger causes GDP growth but not vice versa, and (b) a 1% increase in electricity generation growth will lead to 0.17% increase in GDP growth. The significance of the finding is: as coal still primarily fuels electricity generation, dual control will further hinder China's economic development sans significant enhancement of electric system or expansion of renewable energy, our findings thus support an upgrade of current grid network by China Power International Development; an expansion of renewable capacity such as a proposal of 400 GW new energy project; and construction of energy storage devices such as the CGD Group thermal storage System and Yingcheng Compressed Air Energy Storage System.

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Section: Discussionmentioning

confidence: 99%

Electricity generation and China's GDP growth under dual control policy

Xia,

Liu

2024

Energy Exploration & Exploitation

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“…Hirsa et al (2021) apply neural network models (random forest, support vector machines, feed-forward neural networks, and LSTM) to investigate approaches that could replicate the VIX index and VIX futures with fewer options than the original CBOE methodology [31]. Vrontos (2021) utilizes a variety of machine learning models and finds that these models are statistically and economically more effective than traditional econometric models for forecasting directional changes in the VIX [14]. Finally, Avellaneda et al (2021) generate VIX futures trading signals by including the functional dependence of VIX term structures, VIX futures position, and expected utility in neural networks and find that this approach shows better portfolio performance in out-of-sample backtesting [15].…”

Section: Applications Of Machine Learning Approachesmentioning

confidence: 99%

“…However, few studies focus on the predictability of tradable VIX derivatives, such as VIX futures, and the construction of trading strategies. For example, early studies [13][14][15]. However, rather than providing precise numerical predictions, previous studies focused on either interval or directional forecasting of the VIX.…”

Section: Introductionmentioning

confidence: 99%

VIX constant maturity futures trading strategy: A walk-forward machine learning study

Wang,

Li,

Liu

et al. 2024

PLoS ONE

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This study employs seven advanced machine learning approaches to conduct numerical predictions of the next-day returns of VIX constant-maturity futures (VIX CMFs) using the term structure information derived from VIX CMFs. Based on precise numerical predictions, this study proposes a new Constrained-Mean-Variance Portfolio Optimization (C-MVO) trading strategy and tests it against a benchmark long-short trading strategy to evaluate the profitability of the machine learning numerical predictions. This study applies three unique feature sets, each incrementally incorporating the VIX CMFs’ term structure features, to individually examine the predictive ability of the seven machine learning models and their backtesting performance. Over a comprehensive 11-year period, the experiment adheres to a strict walk-forward expanding-window methodology for both training and backtesting. The predictive and backtesting results show that four of the seven machine learning models attain a prediction information ratio greater than 0.02, with an average prediction information ratio of 0.037. This result suggests that the VIX CMFs term structure features have predictive power for the next-day returns of VIX CMFs. Moreover, the average C-MVO information ratio is 0.623, and the long-short strategy information ratio is 0.404. This increase in the information ratio under the C-MVO strategy validates the effectiveness of the machine learning models and the C-MVO strategy.

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“…In the field of finance, research on volatility predictions primarily focuses on two aspects. Firstly, works in this aspect aim to construct forecasting methods based on widely used financial models such as GARCH and ARIMA (Dai et al, 2022;Spyridon D. Vrontos and Vrontos, 2021;Wang et al, 2016;Engle and Patton, 2007). The second aspect is innovation on the data side (None-jad, 2017;Audrino et al, 2020;Chen et al, 2020;Wang et al, 2021b).…”

Section: Stock Market Volatility Predictionmentioning

confidence: 99%

KeFVP: Knowledge-enhanced Financial Volatility Prediction

Niu,

Xiong,

Wang

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Financial volatility prediction is vital for characterizing a company's risk profile. Transcripts of companies' earnings calls serve as valuable, yet unstructured, data sources to be utilized to access companies' performance and risk profiles. Despite their importance, current works ignore the role of financial metrics knowledge (such as EBIT, EPS, and ROI) in transcripts, which is crucial for understanding companies' performance, and little consideration is given to integrating text and price information. In this work, we statistically analyze common financial metrics and create a special dataset centered on these metrics. Then, we propose a knowledge-enhanced financial volatility prediction method (KeFVP) to inject knowledge of financial metrics into text comprehension by knowledge-enhanced adaptive pre-training (KePt) and effectively integrating text and price information by introducing a conditional time series prediction module. Extensive experiments are conducted on three realworld public datasets, and the results indicate that KeFVP is effective and outperforms all the state-of-the-art methods. 1

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Implied volatility directional forecasting: a machine learning approach

Cited by 34 publications

References 42 publications

Electricity generation and China's GDP growth under dual control policy

Electricity generation and China's GDP growth under dual control policy

VIX constant maturity futures trading strategy: A walk-forward machine learning study

KeFVP: Knowledge-enhanced Financial Volatility Prediction

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