The 2006–2008 oil bubble: Evidence of speculation, and prediction

Sornette, Didier; Woodard, Ryan; Zhou, Wei‐Xing

doi:10.1016/j.physa.2009.01.011

Cited by 198 publications

(95 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The evidence on the effects of speculation is mixed. In some studies, speculation is shown to have a statistically significant effect on price and volatility in oil markets, particularly in the lead up to the historically high oil prices achieved pre-crisis (Sornette et al, 2009, Kaufmann and Ullman, 2009, Cifarelli and Paladino, 2010, Du et al, 2011. In contrast, Büyüksahin and Harris (2011) find no evidence that non-commercial positions, including hedge fund positions, have a causal effect on oil prices.…”

Section: Introductionmentioning

confidence: 99%

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Cummins

Dowling

Kearney

2016

International Review of Financial Analysis

View full text Add to dashboard Cite

We formally compare fundamental factor and latent factor approaches to oil price modelling. Fundamental modelling has a long history in seeking to understand oil price movements, while latent factor modelling has a more recent and limited history, but has gained popularity in other financial markets. The two approaches, though competing, have not formally been compared as to effectiveness. For a range of short-medium-and long-dated WTI oil futures we test a recently proposed five-factor fundamental model and a Principal Component Analysis latent factor model. Our findings demonstrate that there is no discernible difference between the two techniques in a dynamic setting. We conclude that this infers some advantages in adopting the latent factor approach due to the difficulty in determining a well specified fundamental model.

show abstract

Section: Introductionmentioning

confidence: 99%

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Cummins

Dowling

Kearney

2016

International Review of Financial Analysis

View full text Add to dashboard Cite

show abstract

“…nonlinear least-square fits of price and log-price time series , Zhou and Sornette, 2008, Sornette et al, 2009 3. Bayesian methods applied to the time series of returns [Chang and Feigenbaum, 2006].…”

Section: Introductionmentioning

confidence: 99%

A Consistent Model of 'Explosive' Financial Bubbles with Mean-Reversing Residuals

Sornette

2009

SSRN Journal

Self Cite

View full text Add to dashboard Cite

We present a self-consistent model for explosive financial bubbles, which combines a mean-reverting volatility process and a stochastic conditional return which reflects nonlinear positive feedbacks and continuous updates of the investors' beliefs and sentiments. The conditional expected returns exhibit faster-than-exponential acceleration decorated by accelerating oscillations, called "log-periodic power law." Tests on residuals show a remarkable low rate (0.2%) of false positives when applied to a GARCH benchmark. When tested on the S&P500 US index from Jan. 3, 1950 to Nov. 21, 2008, the model correctly identifies the bubbles ending in Oct. 1987, in Oct. 1997, in Aug. 1998 and the ITC bubble ending on the first quarter of 2000. Different unit-root tests confirm the high relevance of the model specification. Our model also provides a diagnostic for the duration of bubbles: applied to the period before Oct. 1987 crash, there is clear evidence that the bubble started at least 4 years earlier. We confirm the validity and universality of the volatility-confined LPPL model on seven other major bubbles that have occurred in the World in the last two decades. Using Bayesian inference, we find a very strong statistical preference for our model compared with a standard benchmark, in contradiction with Chang and Feigenbaum [2006] which used a unit-root model for residuals.

show abstract

“…The same conclusion applies to the price time series of sugar shown in Fig 21, to the Brent Oil bubbles (see Ref. [43] for the analysis of the 2008 bubble) shown in Fig 22 and shown in Fig 25, the main difference between the indicators provided by the quantile regressions compared with the L 2 fitting method is that the former provides earlier warnings of the peak of the bubble that occurred in June 2015 as well as signatures of a previous large peak and correction in early 2015. We refer to Ref.…”

Section: Empirical Analysis Of 16 Historical Bubbles With the Consolimentioning

confidence: 53%

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

Zhang

Sornette

2015

SSRN Journal

Self Cite

View full text Add to dashboard Cite

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the "S&P 500 1987" bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.

show abstract

The 2006–2008 oil bubble: Evidence of speculation, and prediction

Cited by 198 publications

References 12 publications

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

A Consistent Model of 'Explosive' Financial Bubbles with Mean-Reversing Residuals

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

Contact Info

Product

Resources

About