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Mon, 20 Oct 2014 15:18:14 +0200Mon, 20 Oct 2014 15:18:14 +0200Estimating the spot covariation of asset prices – statistical theory and empirical evidence
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35109
We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.Markus Bibinger; Nikolaus Hautsch; Peter Malec; Markus Reißworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35109Mon, 20 Oct 2014 15:18:14 +0200Capturing the zero: a new class of zero-augmented distributions and multiplicative error processes
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22873
We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of both liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data well and is able to correctly predict future distributions.Nikolaus Hautsch; Peter Malec; Melanie Schienleworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22873Fri, 07 Oct 2011 00:00:00 +0200The merit of high-frequency data in portfolio allocation
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22871
This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown.Nikolaus Hautsch; Lada M. Kyj; Peter Malecworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22871Thu, 06 Oct 2011 00:00:00 +0200Pre-averaging based estimation of quadratic variation in the presence of noise and jumps : theory, implementation, and empirical evidence
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7936
This paper provides theory as well as empirical results for pre-averaging estimators of the daily quadratic variation of asset prices. We derive jump robust inference for pre-averaging estimators, corresponding feasible central limit theorems and an explicit test on serial dependence in microstructure noise. Using transaction data of different stocks traded at the NYSE, we analyze the estimators’ sensitivity to the choice of the pre-averaging bandwidth and suggest an optimal interval length. Moreover, we investigate the dependence of pre-averaging based inference on the sampling scheme, the sampling frequency, microstructure noise properties as well as the occurrence of jumps. As a result of a detailed empirical study we provide guidance for optimal implementation of pre-averaging estimators and discuss potential pitfalls in practice. Quadratic Variation , MarketMicrostructure Noise , Pre-averaging , Sampling Schemes , Jumps Nikolaus Hautsch; Mark Podolskijworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7936Sat, 04 Sep 2010 12:20:13 +0200A blocking and regularization approach to high dimensional realized covariance estimation
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7286
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the ’RnB’ estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results.Nikolaus Hautsch; Lada M. Kyj; Roel C. A. Oomenworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7286Wed, 02 Dec 2009 10:52:56 +0100Modelling and forecasting liquidity supply using semiparametric factor dynamics
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7061
We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are modelled using a vector autoregressive model. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model’s forecasting power can be used to improve optimal order execution strategies.Wolfgang Karl Härdle; Nikolaus Hautsch; Andrija Mihociworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7061Sun, 20 Sep 2009 13:37:34 +0200A partially linear approach to modelling the dynamics of spot and futures prices
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/85
In this paper we consider the dynamics of spot and futures prices in the presence of arbitrage. We propose a partially linear error correction model where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. We estimate our model using data on the DAX index and the DAX futures contract. We find that the adjustment is indeed nonlinear. The linear alternative is rejected. The speed of price adjustment is increasing almost monotonically with the magnitude of the price difference.Jürgen Gaul; Erik Theissenworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/85Fri, 11 Apr 2008 11:21:27 +0200Ökonometrische Modellierung von Transaktionsintensitäten auf Finanzmärkten : eine Anwendung von Autoregressive-conditional-duration-Modellen auf die IPO der Deutschen Telekom
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3627
Joachim Grammig; Reinhard Hujer; Stefan Kokot; Kai-Oliver Maurerworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3627Tue, 11 Oct 2005 13:27:03 +0200Bias-free nonparametric estimation of intra-day trade activity measures
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3626
Joachim Grammig; Reinhard Hujer; Stefan Kokotworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3626Tue, 11 Oct 2005 13:19:48 +0200Tackling boundary effects in nonparametric estimation of intra-day liquidity measures
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3625
Joachim Grammig; Reinhard Hujer; Stefan Kokotworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3625Tue, 11 Oct 2005 13:13:56 +0200