R
Rossen Valkanov
Researcher at University of California, San Diego
Publications - 73
Citations - 8781
Rossen Valkanov is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Stock market & Mixed-data sampling. The author has an hindex of 33, co-authored 73 publications receiving 8007 citations. Previous affiliations of Rossen Valkanov include University of California, Los Angeles.
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MIDAS regressions: Further results and new directions
TL;DR: The authors explore mixed data sampling (henceforth MIDAS) regression models, which involve time series data sampled at different frequencies, and provide empirical evidence on microstructure noise and volatility forecasting.
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There is a risk-return trade-off after all ☆
TL;DR: In this paper, a new estimator that forecasts monthly variance with past daily squared returns is introduced, the Mixed Data Sampling (or MIDAS) approach, which finds that there is a significantly positive relation between risk and return in the stock market.
Journal ArticleDOI
There is a Risk-Return Tradeoff after All
TL;DR: In this paper, a new estimator that forecasts monthly variance with past daily squared returns is introduced, the Mixed Data Sampling (or MIDAS) approach, which finds that there is a significantly positive relation between risk and return in the stock market.
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Do industries lead stock markets
TL;DR: This article investigated whether the returns of industry portfolios predict stock market movements and found that a significant number of industry returns, including retail, services, commercial real estate, metal, and petroleum, forecast the stock market by up to two months.
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Predicting volatility: getting the most out of return data sampled at different frequencies
TL;DR: In this paper, the authors consider various mixed data sampling (MIDAS) regressions to predict volatility and find that daily realized power (involving 5-min absolute returns) is the best predictor of future volatility and outperforms models based on realized volatility.