This paper investigates the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis. Using a broad sample of stock options traded at the American Stock Exchange and the Chicago Board Options Exchange (CBOE)...

We observe that daily highs and lows of stock prices do not diverge over time and, hence, adopt the cointegration concept and the related vector error correction model (VECM) to model the daily high, the daily low, and the associated daily range data. The in-sample results attest the importance...

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...

Global excess liquidity roaming the world's financial markets (or its sudden absence) is sometimes believed to limit sovereign monetary policy even in large economies such as the euro area. However, there is still discussion about what constitutes global excess liquidity and how exactly it...

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...

A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized Autoregressive Conditional Heteroskedasticity...

This paper examines whether the predictability of securitized real estate returns differs from that of stock returns. It also provides a cross-country comparison of securitized real estate return predictability. In contrast to most of the literature on this issue, the analysis is not based on a...

This paper uses the Markov-switching multifractal (MSM) model and generalized autoregressive conditional heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 and from January 03, 1977 to March 24, 2014. Based on...