No. Conditional autocorrelation in realized shocks due to misspecification in expected return process affects the relative performance of longer-horizon volatility predictions of models using different frequencies of data. This is because, for multi-step forecasts of volatility, small violations...

Based on a unique high-frequency dataset for more than fifty commodities, currencies, equity indices, and fixed income instruments spanning more than two decades, we document strong similarities in realized volatilities patterns across assets and asset classes. Exploiting these similarities...

This paper proposes an ex post volatility estimator, called mixed interval realized variance (MIRV), that uses high-frequency data to provide measurements robust to the idiosyncratic noise of stock markets caused by market microstructures. The theoretical properties of the new volatility...

Can the degree of predictability found in the data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R-squares of predictive regressions. Using data on the market and component portfolios, we find that the empirical R-squares are significantly greater...

This paper proposes a Near Explosive Random-Coefficient autoregressive model for asset pricing which accommodates both the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be stationary with or without fat tails, unit-root nonstationary...

This paper studies in some details the joint-use of high-frequency data and economic variables to model financial returns and volatility. We extend the Realized LGARCH model by allowing for a timevarying intercept, which responds to changes in macroeconomic variables in a MIDAS framework and...

In this paper we provide a mathematical derivation that links traditional time-value-of-money concepts to Metcalfe value, and use Bitcoin, Facebook as numerical examples of the proof. There is compelling evidence that suggests that the growth and price of bitcoin and other cryptocurrencies are...

We propose the use of a risk measure built on flight-to-safety (FTS) episodes into a volatility forecasting model. We assign to each day in the sample a probability of being a FTS day after observing (ab)normal movements in the US equity, US bond and gold markets. By allowing each FTS day to be...

This paper shows that generalizing the heterogeneous autoregressive model (HAR) with realized (co)variances and semi-(co)variances from the index leads to more accurate volatility forecasts. To circumvent the effects of the market microstructure noise arising from using high sampling...

In this paper stock market development as proxied by market capitalisation is examined. The study period is January 2010 to May 2019. The data frequency is monthly. The paper concentrates on the Zimbabwe Stock Market, but briefly walks through Stock Markets in Africa. Examining stock market...