Modeling and predicting oil VIX: Internet search volume versus traditional mariables

Campos, Ismael Cañete; Cortázar, Gonzalo; Reyes, Tomás

doi:10.1016/j.eneco.2017.06.009

Cited by 45 publications

(16 citation statements)

References 55 publications

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“…Da et al () use search volume to measure investor sentiment and show that decreases in search volume are correlated with price increases, which then reverse in the short term. Campos et al () use search volume to model and predict the oil's VIX, finding that search data significantly increases the returns of volatility‐exposed portfolios.…”

Section: Introductionmentioning

confidence: 99%

Negativity Bias in Attention Allocation: Retail Investors’ Reaction to Stock Returns

Reyes

2018

Int Rev Finance

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We argue that negative stock market performance attracts more attention from retail investors than comparable positive performance. Specifically, we test and confirm the hypothesis that retail investors pay more attention to negative extreme returns than positive ones. We present a measure of attention at the aggregate and company-specific levels using Google's internet search volume indexes. These measures correlate with, but are different from, existing proxies of attention. Our empirical results strongly support the position that investors display a negativity bias in attention allocation with respect to extreme stock returns. Across all specifications, lagged negative extreme returns are stronger predictors of high attention at the individualstock and stock market levels than positive ones.

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Section: Introductionmentioning

confidence: 99%

Negativity Bias in Attention Allocation: Retail Investors’ Reaction to Stock Returns

Reyes

2018

Int Rev Finance

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“…We need data to identify bankruptcy filings, measure attention with search volume from Google, obtain news stories related to each bankruptcy filing, and identify stock prices and 4 See, among others, Ginsberg et al (2009), Vosen and Schmidt (2011), Shoi and Varian (2012), and Reyes et al (2018). 5 See, among others, Kristoufek (2013), Campos et al (2017) and Reyes (2018). accounting variables for each case in our sample. Then, this section describes the sample construction, its characteristics, and bivariate correlations between the key variables.…”

Section: Data Sample Construction Characteristics and Bivariamentioning

confidence: 99%

Saddled with Attention: Overreaction to Bankruptcy Filings

Reyes

Waissbluth

2018

Int Rev Finance

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We analyze the effect of investor attention on stock prices around Chapter 11 bankruptcy filings. We measure investor attention as abnormal search volume from Google, and find that attention‐grabbing companies have more negative abnormal stock returns in the days before and during bankruptcy filings and more positive abnormal returns immediately thereafter. That is, for companies receiving high attention, investors overreact to a bankruptcy filing; for companies receiving low attention, they underreact. This pattern is more pronounced for companies with low institutional ownership and holds after controlling for standard predictors of stock performance during bankruptcy.

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“…Although the literature on OVX is scarce, some studies have already reported that the index has long memory; see Chen, He, and Yu (2015) and Campos, Cortazar, and Reyes (2017). Traditionally, long memory in volatility has been modeled either by autoregressive fractional integrated moving average (ARFIMA) or generalized autoregressive conditional heteroskedasticity (GARCH)-type models, such as the FIGARCH of Baillie, Bollerslev, and Mikkelsen (1996) and the FIEGARCH of Bollerslev and Mikkelsen (1996).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Campos et al (2017) model the oil volatility with the basic heterogeneous autoregressive (HAR) model of Corsi (2009) and find that it fits the oil volatility index well. They also include in the basic HAR model financial and macroeconomic variables, as in Fernandes, Medeiros, and Scharth (2014).…”

Section: Introductionmentioning

confidence: 99%

“…They also include in the basic HAR model financial and macroeconomic variables, as in Fernandes, Medeiros, and Scharth (2014). Contrary to Campos et al (2017), the existence of an asymmetric response of the index is tested in this paper by fitting the asymmetric extension of the HAR model proposed by Corsi, Audrino, and Reno (2012) named HARL and new proposals based on alternative oil price asymmetric measures instead of negative oil returns. HAR models with leverage have been successfully used for forecasting the volatility of stock market returns; see, among others, Liu and Maheu (2009), Scharth and Medeiros (2009), Asai, McAleer, and Medeiros (2012, Corsi and Renò (2012), Byun and Kim (2013), Patton and Sheppard (2015) and Shin (2018, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Modeling and forecasting the oil volatility index

Mazzeu

Veiga

Mariti

2019

Journal of Forecasting

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The increase in oil price volatility in recent years has raised the importance of forecasting it accurately for valuing and hedging investments. The paper models and forecasts the crude oil exchange‐traded funds (ETF) volatility index, which has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. Analysis of the oil volatility index suggests that it presents features similar to those of the daily market volatility index, such as long memory, which is modeled using well‐known heterogeneous autoregressive (HAR) specifications and new extensions that are based on net and scaled measures of oil price changes. The aim is to improve the forecasting performance of the traditional HAR models by including predictors that capture the impact of oil price changes on the economy. The performance of the new proposals and benchmarks is evaluated with the model confidence set (MCS) and the Generalized‐AutoContouR (G‐ACR) tests in terms of point forecasts and density forecasting, respectively. We find that including the leverage in the conditional mean or variance of the basic HAR model increases its predictive ability. Furthermore, when considering density forecasting, the best models are a conditional heteroskedastic HAR model that includes a scaled measure of oil price changes, and a HAR model with errors following an exponential generalized autoregressive conditional heteroskedasticity specification. In both cases, we consider a flexible distribution for the errors of the conditional heteroskedastic process.

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Modeling and predicting oil VIX: Internet search volume versus traditional mariables

Cited by 45 publications

References 55 publications

Negativity Bias in Attention Allocation: Retail Investors’ Reaction to Stock Returns

Negativity Bias in Attention Allocation: Retail Investors’ Reaction to Stock Returns

Saddled with Attention: Overreaction to Bankruptcy Filings

Modeling and forecasting the oil volatility index

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