In the stochastic volatility models for multivariate daily stock returns, it has been found that estimates of parameters become unstable as dimension returns increases. To solve this problem, we focus on factor structure multiple and consider two additional sources information: first, index associated with market and, second, realized covariance matrix calculated from high-frequency data. The p...

Inclusion of jump component in the price process has been a long debate finance literature. In this paper, we identify and characterize risks Canadian stock market using high-frequency data from Toronto Stock Exchange. Our results provide strong evidence clustering – about 30% jumps occur within first 30 minutes trading hours, 25% are due to overnight returns. While average intraday is negative...

Over the past few years, a growing number of contributions have addressed the problem of finding optimal ways to aggregate financial ultra high frequency information into estimates of daily volatility. The list of alternative volatility measures is long, depending on which settings they are derived under (e.g. presence/absence of jumps or microstructure noise in the log–price process). Here, we...

Research has consistently found that implied volatility is a conditionally biased predictor of realized volatility across asset markets. This paper evaluates explanations for this bias in the market for options on foreign exchange futures. No solution considered—including a model of priced volatility risk—explains the conditional bias found in implied volatility. Further, while implied volatili...

High frequency data provides a rich source of information for understanding financial markets and time series properties of returns. This paper estimates models of high frequency index futures returns using ‘around the clock’ 5-minute returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capt...

We apply the ACD-ICV method proposed by Tse and Yang (2011) for the estimation of intraday volatility to estimate monthly volatility, and empirically compare this method against the realized volatility (RV) and generalized autoregressive conditional heteroskedasticity (GARCH) methods. Our Monte Carlo results show that the ACD-ICV method performs well against the other two methods. Evidence on t...

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps...

In this paper we look at the relationship between daily realized volatility estimates using intraday data and range based estimates using daily high/low price range information. Several classical range based volatility estimators are compared with nonlinear functional forms in mapping range based information onto realized volatility measures. We find that the older range based estimators can be...

Many recent modelling advances in …nance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility predictability, some from the perspective of the usefulness of jumps in forecasting volatility. Key papers...