Publications - Kim Christensen https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Bcontroller%5D=Publications&cHash=600ffeb84c19ae683492db6876b1c508 en-us PURE Extension typo3support@science.au.dk (Web Department) 30 <![CDATA[High-dimensional estimation of quadratic variation based on penalized realized variance]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=2f9ba48b-154a-4a46-9afc-b08710879236&tx_pure_pure5%5BshowType%5D=pub&cHash=8e36910726758b36c377c37e0ebd3d2c Christensen, K., Nielsen, M. S., Podolskij, M. In this paper, we develop a penalized realized variance (PRV) estimator of the quadratic variation (QV) of a high-dimensional continuous Itô semimartingale. We adapt the principle idea of regularization from linear regression to covariance estimation in a continuous-time high-frequency setting. We show that under a nuclear norm penalization, the PRV is computed by soft-thresholding the eigenvalues of realized variance (RV). It therefore encourages sparsity of singular values or, equivalently, low rank of the solution. We prove our estimator is minimax optimal up to a logarithmic factor. We derive a concentration inequality, which reveals that the rank of PRV is—with a high probability—the number of non-negligible eigenvalues of the QV. Moreover, we also provide the associated non-asymptotic analysis for the spot variance. We suggest an intuitive data-driven subsampling procedure to select the shrinkage parameter. Our theory is supplemented by a simulation study and an empirical application. The PRV detects about three–five factors in the equity market, with a notable rank decrease during times of distress in financial markets. This is consistent with most standard asset pricing models, where a limited amount of systematic factors driving the cross-section of stock returns are perturbed by idiosyncratic errors, rendering the QV—and also RV—of full rank.

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Research Sat, 01 Jul 2023 10:45:49 +0200 2f9ba48b-154a-4a46-9afc-b08710879236
<![CDATA[A GMM approach to estimate the roughness of stochastic volatility]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=4a0bb988-ea61-4287-9a7c-0e82cdb47f5d&tx_pure_pure5%5BshowType%5D=pub&cHash=34349dae7f7bd9f07181a73a13d18016 Bolko, A. E., Christensen, K., Pakkanen, M. S., Veliyev, B. We develop a GMM approach for estimation of log-normal stochastic volatility models driven by a fractional Brownian motion with unrestricted Hurst exponent. We show that a parameter estimator based on the integrated variance is consistent and, under stronger conditions, asymptotically normally distributed. We inspect the behavior of our procedure when integrated variance is replaced with a noisy measure of volatility calculated from discrete high-frequency data. The realized estimator contains sampling error, which skews the fractal coefficient toward “illusive roughness.” We construct an analytical approach to control the impact of measurement error without introducing nuisance parameters. In a simulation study, we demonstrate convincing small sample properties of our approach based both on integrated and realized variance over the entire memory spectrum. We show the bias correction attenuates any systematic deviance in the parameter estimates. Our procedure is applied to empirical high-frequency data from numerous leading equity indexes. With our robust approach the Hurst index is estimated around 0.05, confirming roughness in stochastic volatility.

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Research Tue, 01 Aug 2023 10:45:49 +0200 4a0bb988-ea61-4287-9a7c-0e82cdb47f5d
<![CDATA[The drift burst hypothesis]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=c6540e7e-0af3-4d7c-8a4c-57badc401105&tx_pure_pure5%5BshowType%5D=pub&cHash=932dc3f0fdf8395e79b5319ba4d13973 Christensen, K., Oomen, R., Renò, R. The drift burst hypothesis postulates the existence of short-lived locally explosive trends in the price paths of financial assets. The recent U.S. equity and treasury flash crashes can be viewed as two high-profile manifestations of such dynamics, but we argue that drift bursts of varying magnitude are an expected and regular occurrence in financial markets that can arise through established mechanisms of liquidity provision. We show how to build drift bursts into the continuous-time Itô semimartingale model, elaborate on the conditions required for the process to remain arbitrage-free, and propose a nonparametric test statistic that identifies drift bursts from noisy high-frequency data. We apply the test and demonstrate that drift bursts are a stylized fact of the price dynamics across equities, fixed income, currencies and commodities. Drift bursts occur once a week on average, and the majority of them are accompanied by subsequent price reversion and can thus be regarded as “flash crashes.” The reversal is found to be stronger for negative drift bursts with large trading volume, which is consistent with endogenous demand for immediacy during market crashes.

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Research Fri, 01 Apr 2022 10:45:49 +0200 c6540e7e-0af3-4d7c-8a4c-57badc401105
<![CDATA[A machine learning approach to volatility forecasting]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=889c873d-4d03-4aa0-bc6f-0f54864a8131&tx_pure_pure5%5BshowType%5D=pub&cHash=30ec6c499d75d939f2e7aad6a437a0e3 Christensen, K., Siggaard, M. V., Veliyev, B. Research Mon, 18 Jan 2021 10:45:49 +0100 889c873d-4d03-4aa0-bc6f-0f54864a8131 <![CDATA[Roughness in spot variance?]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=5e327f89-995b-4fb1-be94-6ee1c9b857db&tx_pure_pure5%5BshowType%5D=pub&cHash=44abe77dc6b5d9c016d227472875fdfd Bolko, A. E., Christensen, K., Pakkanen, M., Veliyev, B. Research Thu, 01 Oct 2020 10:45:49 +0200 5e327f89-995b-4fb1-be94-6ee1c9b857db <![CDATA[The economic value of VIX ETPs]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=8066fe87-d270-4346-a931-753b096ce09b&tx_pure_pure5%5BshowType%5D=pub&cHash=65af87547153b7d0767973192c309fcd Christensen, K., Christiansen, C., Posselt, A. M. The fairly new VIX ETPs have been promoted for providing effective and easily accessible diversification, while at the same time having large negative returns. We examine the economic value of using VIX ETPs for diversification of stock–bond portfolios. Our analysis begins in 2009, when the first VIX ETPs are introduced, and therefore only considers the period after the recent financial crisis. For investors with a constant allocation strategy, the diversification benefits of the VIX ETPs do not offset their negative returns. This implies negative economic value of a constant allocation. For a dynamic allocation strategy, including short VIX ETPs in the investment opportunity set can have substantial positive economic value.

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Research Wed, 01 Jan 2020 10:45:49 +0100 8066fe87-d270-4346-a931-753b096ce09b
<![CDATA[The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=dbeb28d7-8b15-4a53-becd-88884c33481e&tx_pure_pure5%5BshowType%5D=pub&cHash=6a1bfcd37254b208507a709ae8fcfaa9 Christensen, K., Thyrsgaard, M., Veliyev, B. We propose a nonparametric estimator of the empirical distribution function (EDF) of the latent spot variance of the log-price of a financial asset. We show that over a fixed time span our realized EDF (or REDF) – inferred from noisy high-frequency data – is consistent as the mesh of the observation grid goes to zero. In a double-asymptotic framework, with time also increasing to infinity, the REDF converges to the cumulative distribution function of volatility, if it exists. We exploit these results to construct some new goodness-of-fit tests for stochastic volatility models. In a Monte Carlo study, the REDF is found to be accurate over the entire support of volatility. This leads to goodness-of-fit tests that are both correctly sized and relatively powerful against common alternatives. In an empirical application, we recover the REDF from stock market high-frequency data. We inspect the goodness-of-fit of several two-parameter marginal distributions that are inherent in standard stochastic volatility models. The inverse Gaussian offers the best overall description of random equity variation, but the fit is less than perfect. This suggests an extra parameter (as available in, e.g., the generalized inverse Gaussian) is required to model stochastic variance.

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Research Tue, 01 Oct 2019 10:45:49 +0200 dbeb28d7-8b15-4a53-becd-88884c33481e
<![CDATA[The Economic Value of VIX ETPs]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=ccbde89b-55de-4535-b82e-f1cc79afdc76&tx_pure_pure5%5BshowType%5D=pub&cHash=72681275576a62cfa8ba89865f017c54 Christensen, K., Christiansen, C., Posselt, A. M. Research Wed, 18 Sep 2019 10:45:49 +0200 ccbde89b-55de-4535-b82e-f1cc79afdc76 <![CDATA[The drift burst hypothesis]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=210debbc-0622-40f2-882a-111d9e5c553a&tx_pure_pure5%5BshowType%5D=pub&cHash=22b8203526ed39a293b010e6ca7ec4ec Christensen, K., Oomen, R., Renò, R. Research Mon, 20 Aug 2018 10:45:49 +0200 210debbc-0622-40f2-882a-111d9e5c553a <![CDATA[Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=4666a669-005b-4576-b47b-0bcc17f9850b&tx_pure_pure5%5BshowType%5D=pub&cHash=b3c9202bf15af6429e09a40faebd1907 Christensen, K., Hounyo, U., Podolskij, M. In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely sampled jump–diffusion process are available. The test is then based on asset returns, which are deflated by the seasonal component and therefore homoskedastic under the null. To construct our test statistic, we extend the concept of pre-averaged bipower variation to a general Itô semimartingale setting via a truncation device. We prove a central limit theorem for this statistic and construct a positive semi-definite estimator of the asymptotic covariance matrix. The t-statistic (after pre-averaging and jump-truncation) diverges in the presence of stochastic volatility and has a standard normal distribution otherwise. We show that replacing the true diurnal factor with a model-free jump- and noise-robust estimator does not affect the asymptotic theory. A Monte Carlo simulation also shows this substitution has no discernable impact in finite samples. The test is, however, distorted by small infinite-activity price jumps. To improve inference, we propose a new bootstrap approach, which leads to almost correctly sized tests of the null hypothesis. We apply the developed framework to a large cross-section of equity high-frequency data and find that the diurnal pattern accounts for a rather significant fraction of intraday variation in volatility, but important sources of heteroskedasticity remain present in the data.

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Research Mon, 01 Jan 2018 10:45:49 +0100 4666a669-005b-4576-b47b-0bcc17f9850b
<![CDATA[Inference from high-frequency data]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=9738a3a7-e7db-428c-b7fb-7171ec54ee49&tx_pure_pure5%5BshowType%5D=pub&cHash=7d6d05000081ed7f3c65ba43d75f42b6 Christensen, K., Podolskij, M., Thamrongrat, N., Veliyev, B. In this paper, we show how to estimate the asymptotic (conditional) covariance matrix, which appears in central limit theorems in high-frequency estimation of asset return volatility. We provide a recipe for the estimation of this matrix by subsampling; an approach that computes rescaled copies of the original statistic based on local stretches of high-frequency data, and then it studies the sampling variation of these. We show that our estimator is consistent both in frictionless markets and models with additive microstructure noise. We derive a rate of convergence for it and are also able to determine an optimal rate for its tuning parameters (e.g., the number of subsamples). Subsampling does not require an extra set of estimators to do inference, which renders it trivial to implement. As a variance–covariance matrix estimator, it has the attractive feature that it is positive semi-definite by construction. Moreover, the subsampler is to some extent automatic, as it does not exploit explicit knowledge about the structure of the asymptotic covariance. It therefore tends to adapt to the problem at hand and be robust against misspecification of the noise process. As such, this paper facilitates assessment of the sampling errors inherent in high-frequency estimation of volatility. We highlight the finite sample properties of the subsampler in a Monte Carlo study, while some initial empirical work demonstrates its use to draw feasible inference about volatility in financial markets.

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Research Sun, 01 Jan 2017 10:45:49 +0100 9738a3a7-e7db-428c-b7fb-7171ec54ee49
<![CDATA[Fact or friction: Jumps at ultra high frequency]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=281d465c-4e44-421e-83cb-953a0cb14959&tx_pure_pure5%5BshowType%5D=pub&cHash=69197aea5712b8a13fd2961260b76726 Christensen, K., Oomen, R., Podolskij, M. Research Wed, 01 Jan 2014 10:45:49 +0100 281d465c-4e44-421e-83cb-953a0cb14959 <![CDATA[A cost-effectiveness analysis of identifying Fusobacterium necrophorum in throat swabs followed by antibiotic treatment to reduce the incidence of Lemierre's syndrome and peritonsillar abscesses]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=ce9ed473-87ca-4952-8d79-b1b77a3a6566&tx_pure_pure5%5BshowType%5D=pub&cHash=343e7e580be2bd3578d7de6fd7ec1639 Bank, S., Christensen, K., Kristensen, L. H., Prag, J. Research Tue, 01 Jan 2013 10:45:49 +0100 ce9ed473-87ca-4952-8d79-b1b77a3a6566 <![CDATA[On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=6df0d486-af57-4f67-9caa-d4aead537edc&tx_pure_pure5%5BshowType%5D=pub&cHash=decc2ed357feb28d7f5574f436686b1a Christensen, K., Podolskij, M., Vetter, M. Research Tue, 01 Jan 2013 10:45:49 +0100 6df0d486-af57-4f67-9caa-d4aead537edc <![CDATA[Asymptotic theory of range-based multipower variation]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=69b63d50-98a7-4f0a-8f07-188b5b557f30&tx_pure_pure5%5BshowType%5D=pub&cHash=dee1173d45e6dfe1f5a2b82f6930ea5d Christensen, K., Podolskij, M. Research Sun, 01 Jan 2012 10:45:49 +0100 69b63d50-98a7-4f0a-8f07-188b5b557f30 <![CDATA[Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=d6cc6df9-ef31-4b52-9591-bc047c421664&tx_pure_pure5%5BshowType%5D=pub&cHash=4bbc7beb6ccf2daef362206d0fcfb577 Christensen, K., Kinnebrock, S., Podolskij, M. Research Fri, 01 Jan 2010 10:45:49 +0100 d6cc6df9-ef31-4b52-9591-bc047c421664 <![CDATA[Realised quantile-based estimation of the integrated variance]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=6731bbf3-9a7f-4614-82dd-12efc5fec1a2&tx_pure_pure5%5BshowType%5D=pub&cHash=7bbb9ec0876c60eea109305109ce3ecb Christensen, K., Oomen, R., Podolskij, M. Research Fri, 01 Jan 2010 10:45:49 +0100 6731bbf3-9a7f-4614-82dd-12efc5fec1a2 <![CDATA[Bias-correcting the realised range-based variance in the presence of market microstructure noise]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=dc41ac90-163a-11df-b95d-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=040892361932c1dac4c754bd629763cb Christensen, K., Podolskij, M., Vetter, M. Research Thu, 01 Jan 2009 10:45:49 +0100 dc41ac90-163a-11df-b95d-000ea68e967b <![CDATA[Realized range-based estimation of integrated variance]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=adb1a7f0-82f2-11dc-bd47-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=3f16d33efe21e012f80b3c5c2f1b74b5 Christensen, K., Podolskij, M. Research Mon, 01 Jan 2007 10:45:49 +0100 adb1a7f0-82f2-11dc-bd47-000ea68e967b <![CDATA[Topics on high-frequency financial econometrics]]> https://econ.au.dk/research/researchcentres/creates/people/research-fellows/kim-christensen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=31776fa0-e0f2-11db-8476-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=397c002f4dd6670089a83f4d72a6a8e9 Christensen, K. Research Mon, 01 Jan 2007 10:45:49 +0100 31776fa0-e0f2-11db-8476-000ea68e967b