Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious 'present' times $t_2$ before the crashes, we employ a clustering method to group the predicted critical times $t_c$ of the LPPLS fits over different time scales, where $t_c$ is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk

Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious 'present' times $t_2$ before the crashes, we employ a clustering method to group the predicted critical times $t_c$ of the LPPLS fits over different time scales, where $t_c$ is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk

Variáveis macroeconômicas e o comportamento do investimento estrangeiro em carteira: uma análise para o caso brasileiro de 2001 a 2011

TCC (graduação) - Universidade Federal de Santa Catarina, Centro Sócio Econômico, Curso de Ciências Econômicas.O presente estudo analisa o fluxo de investimento estrangeiro em carteira direcionado à economia brasileira em conjunto com sua variação frente ao comportamento das seguintes variáveis macroeconômicas: taxa de câmbio nominal, índice de preços ao consumidor amplo, taxa básica de juros SELIC, Índice Bovespa, Risco-País (EMBI+Br) e Treasury bills norte americanas. Para tanto, utiliza-se de ferramentas estatísticas, gráficas e econométricas visando explicitar os mecanismos determinantes do fluxo externo de capital direcionado ao mercado acionário e ao mercado de renda-fixa. Conclui-se que as variáveis determinantes para o fluxo líquido do investimento estrangeiro em carteira diferem das variáveis que determinam seu acúmulo. Enquanto para sua trajetória acumulada as variáveis SELIC, Ibovespa e Risco-País apresentam-se positivamente correlacionadas, taxa de câmbio nominal, IPCA, SELIC e Treasury bills parecem se relacionar de forma negativa. Para seu fluxo líquido foram as variáveis IPCA, SELIC e Ibovespa que se correlacionaram de forma positiva enquanto SELIC e apresentaram uma relação negativa. Para as subcontas de ações e renda-fixa, verifica-se que seus respectivos determinantes também diferem. Enquanto câmbio nominal, SELIC e Ibovespa atuam de forma positiva frente ao acúmulo do investimento estrangeiro em ações, IPCA, Risco-País e Treasury bills se comportam de forma negativa. Para seu fluxo líquido observa-se cambio, Ibovespa e EMBI atuando de forma positiva enquanto cambio e EMBI se relacionam de forma negativa. Para o investimento estrangeiro em renda-fixa, parece atuar de forma positiva sobre seu acúmulo somente o Risco-País enquanto que a variação cambial, IPCA e atuam de forma negativa. Para o fluxo líquido do investimento estrangeiro em renda-fixa foi constatado a atuação positiva das variáveis IPCA e SELIC, enquanto tcambio, SELIC e Treasury bills parecem atuar de forma negativa. Sua relação com a dinâmica econômica nacional é ambígua devido ao risco da reversão repentina de seus fluxos, frente à momentos de instabilidade dos mercados

Modelos estatísticos alternativos para os mini flash crashes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Sócio-Econômico, Programa de Pós-Graduação em Economia, Florianópolis, 2014

Some Statistical Properties of the Mini Flash Crashes

We present some properties of the data from the recent mini flash crashes occurring in individual stocks of the Dow Jones Industrial Average. The top five are: 1) Gaussianity is absent in data; 2) the tail decay of the return distributions follow power laws; 3) chaos and logperiodicity cannot be dismissed at first; 4) chaos and logperiodicity are not good models for the data on second thoughts; and 5) a threshold GARCH fit can also describe the data well, but fails to detect the power law tail decay of most distributions of returns

Some Statistical Properties of the Mini Flash Crashes

We present some properties of the data from the recent mini flash crashes occurring in individual stocks of the Dow Jones Industrial Average. The top five are: 1) Gaussianity is absent in data; 2) the tail decay of the return distributions follow power laws; 3) chaos and logperiodicity cannot be dismissed at first; 4) chaos and logperiodicity are not good models for the data on second thoughts; and 5) a threshold GARCH fit can also describe the data well, but fails to detect the power law tail decay of most distributions of returns

On the predictability of stock market bubbles : evidence from LPPLS confidence multi-scale indicators

We examine the predictability of positive and negative stock market bubbles via an application of the LPPLS Confidence Multi-scale Indicators to the S&P500, FTSE and NIKKEI indexes. We find that the LPPLS framework is able to successfully capture, ex-ante, some of the prominent bubbles across different time scales, such as the Black Monday, Dot-com, and Subprime Crisis periods. We then show that measures of short selling activity have robust predictive power over negative bubbles across both short and long time horizons, in line with the previous studies suggesting that short sellers have predictive ability over stock price crash risks. Market liquidity, on the other hand, is found to have robust predictive power over both the negative and positive bubbles, while its predictive power is largely limited to short horizons. Short selling and liquidity are thus identified as two important factors contributing to the LPPLS-based bubble indicators. The evidence overall points to the predictability of stock market bubbles using market-based proxies of trading activity and can be used as a guideline to model and monitor the occurrence of bubble conditions in financial markets.https://www.tandfonline.com/loi/rquf202020-06-01hj2019Economic