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Full-Text Articles in Social and Behavioral Sciences
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi. Zhang
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi. Zhang
Research Collection School Of Economics
This paper provides a nonparametric test for deciding the dimensionality of a policy shock as manifest in the abnormal change in asset returns' stochastic covariance matrix, following the release of a macroeconomic announcement. We use high-frequency data in local windows before and after the event to estimate the covariance jump matrix, and then test its rank. We find a one-factor structure in the covariance jump matrix of the yield curve resulting from the Federal Reserve's monetary policy shocks prior to the 2007-2009 financial crisis. The dimensionality of policy shocks increased afterwards due to the use of unconventional monetary policy tools.
Occupation Density Estimation For Noisy High-Frequency Data, Congshan Zhang, Jia Li, Tim Bollerslev
Occupation Density Estimation For Noisy High-Frequency Data, Congshan Zhang, Jia Li, Tim Bollerslev
Research Collection School Of Economics
This paper studies the nonparametric estimation of occupation densities for semimartingale processes observed with noise. As leading examples we consider the stochastic volatility of a latent efficient price process, the volatility of the latent noise that separates the efficient price from the actually observed price, and nonlinear transformations of these processes. Our estimation methods are decidedly nonparametric and consist of two steps: the estimation of the spot price and noise volatility processes based on pre-averaging techniques and in-fill asymptotic arguments, followed by a kernel-type estimation of the occupation densities. Our spot volatility estimates attain the optimal rate of convergence, and …
Reading The Candlesticks: An Ok Estimator For Volatility, Jia Li, Dishen Wang, Qiushi. Zhang
Reading The Candlesticks: An Ok Estimator For Volatility, Jia Li, Dishen Wang, Qiushi. Zhang
Research Collection School Of Economics
Academic research on nonparametric “spot” volatility inference often relies on high-quality transaction data that are not available to an average investor. Most investors, however, have free access to intraday candlestick charts through their online trading applications. Based on such data, we propose an Optimal candlesticK (OK) estimator for the spot volatility at a given time point. Under a standard infill asymptotic setting for Itˆo semimartingale price process, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. In addition, its estimation error can be coupled by a Brownian functional, whose …