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Full-Text Articles in Social and Behavioral Sciences
Cross-Exchange Crypto Risk: A High-Frequency Dynamic Network Perspective, Yifu Wang, Wanbo Lu, Min-Bin Liu, Rui Ren, Wolfgang Karl Hardle
Cross-Exchange Crypto Risk: A High-Frequency Dynamic Network Perspective, Yifu Wang, Wanbo Lu, Min-Bin Liu, Rui Ren, Wolfgang Karl Hardle
Sim Kee Boon Institute for Financial Economics
Cross-exchange crypto trading presents inherent risks, particularly for centralized exchanges. Investors observe exacerbating crypto volatility and counterparty risk and would like to quantify these elements of crypto trades. The multiple exchanges require a multivariate view on the structures of risk spillover across exchanges. Here, a Multivariate Heterogeneous AutoRegression (MHAR) model is designed and analyzed, accommodating the stylized facts of crypto markets, including 24/7 trading and the long-memory effect on return variations. The proposed MHAR approach clearly reveals the intensity of interconnectedness among exchanges during extreme events, e.g., the Bitcoin market. Additionally, one observes extremely volatile eigenvector centralities of Futures Exchange …
Optimal Inference For Spot Regressions, Tim Bollerslev, Jia Li, Yuexuan Ren
Optimal Inference For Spot Regressions, Tim Bollerslev, Jia Li, Yuexuan Ren
Research Collection School Of Economics
Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The "local Gaussian" property of the generic continuous-time benchmark model enables optimal "finite-sample" inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures.
Optimal Nonparametric Range-Based Volatility Estimation, Tim Bollerslev, Jia Li, Qiyuan Li
Optimal Nonparametric Range-Based Volatility Estimation, Tim Bollerslev, Jia Li, Qiyuan Li
Research Collection School Of Economics
We present a general framework for optimal nonparametric spot volatility estimation based on intraday range data, comprised of the first, highest, lowest, and last price over a given time-interval. We rely on a decision-theoretic approach together with a coupling-type argument to directly tailor the form of the nonparametric estimator to the specific volatility measure of interest and relevant loss function. The resulting new optimal estimators offer substantial efficiency gains compared to existing commonly used range-based procedures.
Disagreement In Market Index Options, Guilherme Salome, George Tauchen, Jia Li
Disagreement In Market Index Options, Guilherme Salome, George Tauchen, Jia Li
Research Collection School Of Economics
We generate new evidence on disagreement among traders in the S&P 500 options market from high-frequency intraday price and volume data. Inference on disagreement is based on a model where investors observe public information but agree to disagree on its interpretation; disagreement among investors is captured by the volume–volatility elasticity. For options, there are two natural variables related to disagreement: moneyness and tenor, which we relate to disagreement about the distribution of the market index at different quantiles and times. The estimated volume–volatility elasticity equals unity for options near the money and close to expiration, which is consistent with the …
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 …
Glivenko-Cantelli Theorems For Integrated Functionals Of Stochastic Processes, Jia Li, Congshan Zhang, Yunxiao Liu
Glivenko-Cantelli Theorems For Integrated Functionals Of Stochastic Processes, Jia Li, Congshan Zhang, Yunxiao Liu
Research Collection School Of Economics
We prove a Glivenko-Cantelli theorem for integrated functionals of latent continuous-time stochastic processes. Based on a bracketing condition via random brackets, the theorem establishes the uniform convergence of a sequence of empirical occupation measures towards the occupation measure induced by underlying processes over large classes of test functions, including indicator functions, bounded monotone functions, Lipschitz-in-parameter functions, and Hölder classes as special cases. The general Glivenko-Cantelli theorem is then applied in more concrete high-frequency statistical settings to establish uniform convergence results for general integrated functionals of the volatility of efficient price and local moments of microstructure noise.
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. …
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We apply the factor approach to the correlation matrix to forecast large covariance matrix of asset returns using high-frequency data, using the principal component method to model the underlying latent factors of the correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. Our proposed method is found to perform better in reporting smaller forecast errors than some selected competitors. Empirical application to a portfolio of 100 NYSE and NASDAQ stocks shows that our method provides lower out-of-sample realized variance in selecting global minimum …
Realized Semicovariances, Tim Bollerslev, Jia Li, Andrew J. Patton, Rogier Quaedvlieg
Realized Semicovariances, Tim Bollerslev, Jia Li, Andrew J. Patton, Rogier Quaedvlieg
Research Collection School Of Economics
We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high-frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first-order asymptotic results highlight how the same-sign and mixed-sign components load differently on economic information related to stochastic correlation and jumps. The second-order asymptotic results reveal the structure underlying the same-sign semicovariances, as manifested in the form of co-drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross-section of individual stocks to empirically …
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 …
Business Time Sampling Scheme With Applications To Testing Semi-Martingale Hypothesis And Estimating Integrated Volatility, Yingjie Dong, Yiu Kuen Tse
Business Time Sampling Scheme With Applications To Testing Semi-Martingale Hypothesis And Estimating Integrated Volatility, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We propose a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data. We compute a time-transformation (TT) function using the intraday integrated volatility estimated by a jump-robust method. The BTS transactions are obtained using the inverse of the TT function. Using our sampled BTS transactions, we test the semi-martingale hypothesis of the stock log-price process and estimate the daily realized volatility. Our method improves the normality approximation of the standardized business-time return distribution. Our Monte Carlo results show that the integrated volatility estimates using our proposed sampling strategy provide smaller root mean-squared error.
Adaptive Estimation Of Continuous-Time Regression Models Using High-Frequency Data, Jia Li, Viktor Todorov, George Tauchen
Adaptive Estimation Of Continuous-Time Regression Models Using High-Frequency Data, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We derive the asymptotic efficiency bound for regular estimates of the slope coefficient in a linear continuous-time regression model for the continuous martingale parts of two Itô semimartingales observed on a fixed time interval with asymptotically shrinking mesh of the observation grid. We further construct an estimator from high-frequency data that achieves this efficiency bound and, indeed, is adaptive to the presence of infinite-dimensional nuisance components. The estimator is formed by taking optimal weighted average of local nonparametric volatility estimates that are constructed over blocks of high-frequency observations. The asymptotic efficiency bound is derived under a Markov assumption for the …
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We study the market microstructure noise-variance estimation of high-frequency stock prices. Based on the Hansen and Lunde (2006) approach, we propose estimates using subsampling method at different time scales. We conduct a Monte Carlo study to compare our method against others in the literature. Our results show that our proposed estimates have lower (absolute) mean error and root mean-squared error, and their performance is quite stable at different time scales.
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 …
Essays On High-Frequency Financial Data Analysis, Yingjie Dong
Essays On High-Frequency Financial Data Analysis, Yingjie Dong
Dissertations and Theses Collection (Open Access)
This dissertation consists of three essays on high-frequency financial data analysis. I consider intraday periodicity adjustment and its effect on intraday volatility estimation, the Business Time Sampling (BTS) scheme and the estimation of market microstructure noise using NYSE tick-by-tick transaction data. Chapter 2 studies two methods of adjusting for intraday periodicity of highfrequency financial data: the well-known Duration Adjustment (DA) method and the recently proposed Time Transformation (TT) method (Wu (2012)). I examine the effects of these adjustments on the estimation of intraday volatility using the Autoregressive Conditional Duration-Integrated Conditional Variance (ACD-ICV) method of Tse and Yang (2012). I find …
Volatility Occupation Times, Jia Li, Viktor Todorov, George Tauchen
Volatility Occupation Times, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We propose nonparametric estimators of the occupation measure and the occupation density of the diffusion coefficient (stochastic volatility) of a discretely observed Itô semimartingale on a fixed interval when the mesh of the observation grid shrinks to zero asymptotically. In a first step we estimate the volatility locally over blocks of shrinking length, and then in a second step we use these estimates to construct a sample analogue of the volatility occupation time and a kernel-based estimator of its density. We prove the consistency of our estimators and further derive bounds for their rates of convergence. We use these results …