We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's major cities. Building on the Log-Periodic Power Law Singular (LPPLS) model of self-reinforcing feedback loops, we use the quantile...

This paper focuses on the forecasting of market risk measures for the Russian RTS index future, and examines whether augmenting a large class of volatility models with implied volatility and Google Trends data improves the quality of the estimated risk measures. We considered a time sample of...

This paper offers an improvement to the trade-to-trade model for event studies. While the trade-to-trade model of Maynes and Rumsey (1993) addresses the problem of thin trading by eliminating periods in which no trading is recorded, the proposed improvement addresses the influence of zero-value...

This paper presents a CAPM-based threshold quantile regression model with GARCH specification to examine relations between stock excess returns and “abnormal trading volume.” By employing the Bayesian MCMC method with asymmetric Laplace distribution to six daily Dow Jones Industrial stocks,...

We show that conditional skewness and kurtosis of the momentum strategy are highly time-varying and sometimes take extreme values or may even not exist. The high negative skewness and high kurtosis arise since the winners' and losers' skewness moves in opposite directions, whereas the kurtosis...

We provide empirical evidence that the returns on US equity momentum exhibit a time-varying skewness which deepens during dramatic losses (crashes). As a result, the dynamics of the strategy expected returns reflects the time variation in both conditional volatility and skewness. This has first...

This study investigated the impact of investor sentiment impact on sectoral returns and their volatility on the Johannesburg Stock Exchange using a proxy-based composite investor sentiment index and generalised autoregressive conditional heteroscedasticity models. Overall, findings showed a...

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders' use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ,...

In this paper, we provide new empirical evidence on order submission activity and price impacts of limit orders at NASDAQ. Employing NASDAQ TotalView-ITCH data, we find that market participants dominantly submit limit orders with sizes equal to a round lot. Most limit orders are canceled almost...