H2020Индивидуална стипендия2020–2022

NONCAUSALBubble · Noncausal time series models for the forecasting of speculative bubbles

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-07-01 → 2022-06-30
Финансиране от ЕС: 187 572 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Спекулативните балони, като например резкият скок в цените на криптовалутите, се анализират чрез специални математически модели. Това помага на финансовите регулатори да следят пазарите и да ограничат ефекта от внезапни сривове в икономиката.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Noncausal time series models for the forecasting of speculative bubbles

Exuberant increases in the price of certain goods or financial assets, far beyond what could appear to be a reasonable intrinsic value, are commonly designated as “bubbles”. Such phenomena are not new: the Tulip Mania of the 17th century and the South Sea bubble of 18th century, for instance, are well documented historical instances of such events. Sharp reversals, or “crashes”, systematically follow the exuberant increasing phase of bubbles and have dramatic impact on the broader economy and society. The United States housing bubble, which peaked in 2006, was the trigger for a global economic crisis which required unprecedented intervention by states and central banks across the world. Monitoring and forecasting the evolution of asset prices, especially the ones undergoing rapid increases, could provide the ability for financial regulators to steer the markets away from overheating, avoiding sharp collapses or allowing to contain their impact. Forecasting bubbles and their crashes remains however an open research question. A recent modelling approach at the intersection of time series econometrics, statistics and probability theory -so-called “anticipative” or “noncausal” time series models- is very promising in that regard as it allows to fit and reproduce adequately the observable and statistical characteristic of bubbles across a wide range of financial indexes, stocks, commodities, cryptocurrencies and economic indicators. A blind spot of this non-standard modelling approach is that no theory exists regarding forecasting the future evolution of bubbles or the incoming occurrence of a crash. The objective of this project is precisely to provide such theoretical results which will enable the use of anticipative time series models for forecasting bubble crashes.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Speculative bubbles on financial markets, viewed as short-term explosive deviations of prices from a typical historical level and ending in an abrupt correction, have become common events across all major asset classes. They can have a dramatic impact on portfolio performances, financial institutions solvability and can compromise the stability of the financial system. Because of their ability to reproduce stylized facts from speculative bubbles such as locally explosive trajectories, noncausal time series models -autoregressive (AR) and moving average (MA) processes with roots located inside the unit circle- have been at the center of a recent fast-emerging literature in econometrics and finance. Provided their dynamics is better understood, they will enable to formulate forecasts of future bubble trajectories. If rapid progress is being achieved on estimation and fitting problematics, prediction theory of noncausal processes remains particularly scarce and limited to special elementary cases – mostly the univariate noncausal AR(1) with independent and identically distributed Cauchy errors.The NONCAUSALBubble project aims at specifically addressing the lack of theoretical foundations for the forecasting of heavy-tailed noncausal processes. Building on recent tools from extreme value and alpha-stable distribution theories, NONCAUSALBubble will characterise the conditional distribution of future paths given the past observed trajectory during explosive episodes for 1) higher-order and 2) multivariate noncausal ARMA models. Closed-form formulations of the predictive distribution during bubble episodes will be derived alongside analytical quantification of the crash odds, and an intuitive prediction framework in terms of bubble pattern-recognition will be developed.The project is hosted by VU Amsterdam, one of the top research groups in time series econometrics and forecasting.

Оригинален текст от CORDIS (на английски).

Участници

STICHTING VU · AmsterdamКоординаторНидерландия