For digital assets (cryptocurrencies, DeFi tokens and NFTs) and traditional financial assets
such as stock indices and commodities, this paper established a dynamic downside risk …

Using monthly data for the G7 countries from 1973 to 2020, we study whether stock market
bubbles help to forecast out-of-sample the realized volatility of oil price returns. We use the …

This paper investigates the presence of financial bubbles in the environmentally friendly
investments captured by the ESG markets. By using the log-periodic power law singularity …

This paper examines the predictability of bubbles across global stock markets and whether
or not synchronicity in bubble formation can be predicted via metrics of market risk that are …

This paper presents a novel approach to identifying potential bubbles in the US stock market
by employing alternative time series methods based on long memory, including fractional …

The aim of this work is to test for duration dependence to examine rational speculative
behaviour in the US housing market. Unlike other works that also test for duration …

We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-
LPPLS-CI) approach to identify positive and negative bubbles in the short-, medium, and …

We study whether booms and busts in the stock market of the United States (US) drives its
volatility. Given this, first, we employ the Multi-Scale Log-Periodic Power Law Singularity …

While there is a large body of literature on oil uncertainty-equity prices and/or returns nexus,
an associated important question of how oil market uncertainty affects stock market bubbles …

This paper presents a novel approach to identifying potential bubbles in the US stock market
by employing alternative time series methods based on long memory, including fractional …