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Articles 1 - 13 of 13
Full-Text Articles in Social and Behavioral Sciences
Permutation-Based Tests For Discontinuities In Event Studies, Federico Bugni, Jia Li
Permutation-Based Tests For Discontinuities In Event Studies, Federico Bugni, Jia Li
Research Collection School Of Economics
We propose using a permutation test to detect discontinuities in an underlying economic model at a cutoff point. Relative to the existing literature, we show that this test is well suited for event studies based on time-series data. The test statistic measures the distance between the empirical distribution functions of observed data in two local subsamples on the two sides of the cutoff. Critical values are computed via a standard permutation algorithm. Under a high-level condition that the observed data can be coupled by a collection of conditionally independent variables, we establish the asymptotic validity of the permutation test, allowing …
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 …
Fixed-K Inference For Volatility, Tim Bollerslev, Jia Li, Zhipeng Liao
Fixed-K Inference For Volatility, Tim Bollerslev, Jia Li, Zhipeng Liao
Research Collection School Of Economics
We present a new theory for the conduct of nonparametric inference about the latent spot volatility of a semimartingale asset price process. In contrast to existing theories based on the asymptotic notion of an increasing number of observations in local estimation blocks, our theory treats the estimation block size k as fixed. While the resulting spot volatility estimator is no longer consistent, the new theory permits the construction of asymptotically valid and easy-to-calculate pointwise confidence intervals for the volatility at any given point in time. Extending the theory to a high-dimensional inference setting with a growing number of estimation blocks …
Generalized Jump Regressions For Local Moments, Tim Bollerslev, Jia Li, Leonardo Salim Saker Chaves
Generalized Jump Regressions For Local Moments, Tim Bollerslev, Jia Li, Leonardo Salim Saker Chaves
Research Collection School Of Economics
We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as special cases. The resulting asymptotic distribution of the estimator, derived under an infill asymptotic setting, is highly nonstandard and generally not mixed normal. In addition, we establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction. …
Jump Factor Models In Large Cross-Sections, Jia Li, Viktor Todorov, George. Tauchen
Jump Factor Models In Large Cross-Sections, Jia Li, Viktor Todorov, George. Tauchen
Research Collection School Of Economics
We develop tests for deciding whether a large cross-section of asset prices obey an exact factor structure at the times of factor jumps. Such jump dependence is implied by standard linear factor models. Our inference is based on a panel of asset returns with asymptotically increasing cross-sectional dimension and sampling frequency, and essentially no restriction on the relative magnitude of these two dimensions of the panel. The test is formed from the high-frequency returns at the times when the risk factors are detected to have a jump. The test statistic is a cross-sectional average of a measure of discrepancy in …
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Research Collection School Of Economics
We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer product of the cross-section of increments at the identified jump events. The test for rank r is based on the asymptotic behavior of the …
Efficient Estimation Of Integrated Volatility Functionals Via Multi-Scale Jackknife, Jia Li, Yunxiao Liu, Dacheng. Xiu
Efficient Estimation Of Integrated Volatility Functionals Via Multi-Scale Jackknife, Jia Li, Yunxiao Liu, Dacheng. Xiu
Research Collection School Of Economics
We propose semiparametrically efficient estimators for general integrated volatility functionals of multivariate semimartingale processes. A plug-in method that uses nonparametric estimates of spot volatilities is known to induce high-order biases that need to be corrected to obey a central limit theorem. Such bias terms arise from boundary effects, the diffusive and jump movements of stochastic volatility and the sampling error from the nonparametric spot volatility estimation. We propose a novel jackknife method for bias correction. The jackknife estimator is simply formed as a linear combination of a few uncorrected estimators associated with different local window sizes used in the estimation …
Asymptotic Inference About Predictive Accuracy Using High Frequency Data, Jia Li, Andrew J. Patton
Asymptotic Inference About Predictive Accuracy Using High Frequency Data, Jia Li, Andrew J. Patton
Research Collection School Of Economics
This paper provides a general framework that enables many existing inference methods for predictive accuracy to be used in applications that involve forecasts of latent target variables. Such applications include the forecasting of volatility, correlation, beta, quadratic variation, jump variation, and other functionals of an underlying continuous-time process. We provide primitive conditions under which a “negligibility” result holds, and thus the asymptotic size of standard predictive accuracy tests, implemented using a high-frequency proxy for the latent variable, is controlled. An extensive simulation study verifies that the asymptotic results apply in a range of empirically relevant applications, and an empirical application …
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the …
Weak Convergence To Stochastic Integrals For Econometric Applications, Hanying Liang, Peter C. B. Phillips, Hanchao Wang, Qiying Wang
Weak Convergence To Stochastic Integrals For Econometric Applications, Hanying Liang, Peter C. B. Phillips, Hanchao Wang, Qiying Wang
Research Collection School Of Economics
Limit theory involving stochastic integrals is now widespread in time series econometrics and relies on a few key results on functional weak convergence. In establishing such convergence, the literature commonly uses martingale and semimartingale structures. While these structures have wide relevance, many applications involve a cointegration framework where endogeneity and nonlinearity play major roles and complicate the limit theory. This paper explores weak convergence limit theory to stochastic integral functionals in such settings. We use a novel decomposition of sample covariances of functions of I (1) and I (0) time series that simplifies the asymptotics and our limit results for …
Estimating The Volatility Occupation Time Via Regularized Laplace Inversion, Jia Li, Viktor Todorov, Tauchen
Estimating The Volatility Occupation Time Via Regularized Laplace Inversion, Jia Li, Viktor Todorov, Tauchen
Research Collection School Of Economics
We propose a consistent functional estimator for the occupation time of the spot variance of an asset price observed at discrete times on a finite interval with the mesh of the observation grid shrinking to zero. The asset price is modeled nonparametrically as a continuous-time Itô semimartingale with nonvanishing diffusion coefficient. The estimation procedure contains two steps. In the first step we estimate the Laplace transform of the volatility occupation time and, in the second step, we conduct a regularized Laplace inversion. Monte Carlo evidence suggests that the proposed estimator has good small-sample performance and in particular it is far …
Generalized Method Of Integrated Moments For High-Frequency Data, Jia Li, Dacheng Xiu
Generalized Method Of Integrated Moments For High-Frequency Data, Jia Li, Dacheng Xiu
Research Collection School Of Economics
We propose a semiparametric two‐step inference procedure for a finite‐dimensional parameter based on moment conditions constructed from high‐frequency data. The population moment conditions take the form of temporally integrated functionals of state‐variable processes that include the latent stochastic volatility process of an asset. In the first step, we nonparametrically recover the volatility path from high‐frequency asset returns. The nonparametric volatility estimator is then used to form sample moment functions in the second‐step GMM estimation, which requires the correction of a high‐order nonlinearity bias from the first step. We show that the proposed estimator is consistent and asymptotically mixed Gaussian and …
Robust Estimation And Inference For Jumps In Noisy High Frequency Data: A Local-To-Continuity Theory For The Pre-Averaging Method, Jia Li
Research Collection School Of Economics
We develop an asymptotic theory for the pre-averaging estimator when asset price jumps are weakly identified, here modeled as local to zero. The theory unifies the conventional asymptotic theory for continuous and discontinuous semimartingales as two polar cases with a continuum of local asymptotics, and explains the breakdown of the conventional procedures under weak identification. We propose simple bias-corrected estimators for jump power variations, and construct robust confidence sets with valid asymptotic size in a uniform sense. The method is also robust to certain forms of microstructure noise.