Predicting Crashes in Oil Prices During The Covid-19 Pandemic with Mixed Causal-Noncausal Models

Hecq, Alain; Voisin, Elisa

doi:10.1108/s0731-90532023000045b010

Cited by 5 publications

(1 citation statement)

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“…Pedersen (2001) argues that the HP filter is adequate if the cyclical component of seasonally adjusted timeseries is defined as cycles with durations of less than 32 quarters. Further, Hecq and Voisin (2021) also support the use of the HP filter in cases where the fluctuations in fundamental values are unknown. Given the unique nature of box office earnings, we make no assumptions about its fluctuations and use the HP filter.…”

Section: Cyclical Component Of Box Office Earningsmentioning

confidence: 57%

Is it time for popcorn? Daily box office earnings and aggregate stock returns

Fortin

2022

Financial Management

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We quantitatively measure the interactions between daily consumption and the stock market. We find that daily consumption, proxied by the cyclical component of theatrical box office earnings, can significantly and positively predict stock returns for up to 5 days. We also demonstrate a trading strategy using our consumption measures that yield nontrivial excess returns with little risk. These findings suggest that the box office effect is an economically important factor for equities. The framework implies that daily consumption carries value‐relevant public information, which leads to price reaction at a daily frequency.

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Section: Cyclical Component Of Box Office Earningsmentioning

confidence: 57%

Is it time for popcorn? Daily box office earnings and aggregate stock returns

Fortin

2022

Financial Management

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Forecast performance of noncausal autoregressions and the importance of unit root pretesting

Bec,

Bohn Nielsen

2024

Journal of Forecasting

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Based on a large simulation study, this paper investigates which strategy to adopt in order to choose the most accurate forecasting model for mixed causal‐noncausal autoregressions (MAR) data generating processes: always differencing (D), never differencing (L), or unit root pretesting (P). Relying on recent econometric developments regarding forecasting and unit root testing in the MAR framework, the main results suggest that from a practitioner's point of view, the P strategy at the 10% level is a good compromise. In fact, it never departs too much from the best model in terms of forecast accuracy, unlike the L (respectively, D) strategy when the DGP becomes very persistent (respectively, less persistent). This approach is illustrated using recent monthly Brent crude oil price data.

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Modelling common bubbles in cryptocurrency prices

Hall,

Jasiak

2024

Economic Modelling

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Predicting Crashes in Oil Prices During The Covid-19 Pandemic with Mixed Causal-Noncausal Models

Cited by 5 publications

References 0 publications

Is it time for popcorn? Daily box office earnings and aggregate stock returns

Is it time for popcorn? Daily box office earnings and aggregate stock returns

Forecast performance of noncausal autoregressions and the importance of unit root pretesting

Modelling common bubbles in cryptocurrency prices

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