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Fri, 28 Feb 2014 15:35:42 +0100Fri, 28 Feb 2014 15:35:42 +0100Efficient Iterative Maximum Likelihood Estimation of High-Parameterized Time Series Models
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/33146
We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields computationally tractable, consistent and asymptotic efficient estimates of all parameters. We show the asymptotic normality and derive the estimator's asymptotic covariance in dependence of the number of iteration steps. To mitigate the curse of dimensionality in high-parameterized models, we combine the procedure with a penalization approach yielding sparsity and reducing model complexity. Small sample properties of the estimator are illustrated for two time series models in a simulation study. In an empirical application, we use the proposed method to estimate the connectedness between companies by extending the approach by Diebold and Yilmaz (2014) to a high-dimensional non-Gaussian setting.Nikolaus Hautsch; Ostap Okhrin; Alexander Ristigworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/33146Fri, 28 Feb 2014 15:35:42 +0100Financial network systemic risk contributions
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32497
We propose the realized systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market as well as balance sheet information, we define the realized systemic risk beta as the total time-varying marginal effect of a firm’s Value-at-risk (VaR) on the system’s VaR. Statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential supervision.Nikolaus Hautsch; Julia Schaumburg; Melanie Schienleworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32497Mon, 16 Dec 2013 09:12:18 +0100Copula-based dynamic conditional correlation
multiplicative error processes
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32496
We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks.Taras Bodnar; Nikolaus Hautschworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32496Mon, 16 Dec 2013 09:05:56 +0100On the dark side of the market: identifying and analyzing hidden order placements
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/24085
Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders’ use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ, we propose a simple method to conduct statistical inference on the location of hidden depth and to test economic hypotheses. Analyzing a wide cross-section of stocks, we show that market conditions reflected by the (visible) bid-ask spread, (visible) depth, recent price movements and trading signals significantly affect the aggressiveness of ’dark’ liquidity supply and thus the ’hidden spread’. Our evidence suggests that traders balance hidden order placements to (i) compete for the provision of (hidden) liquidity and (ii) protect themselves against adverse selection, front-running as well as ’hidden order detection strategies’ used by high-frequency traders. Accordingly, our results show that hidden liquidity locations are predictable given the observable state of the market.Nikolaus Hautsch; Ruihong Huangworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/24085Tue, 03 Apr 2012 16:20:01 +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 +0200Capturing the zero: a new class of zero-augmented distributions and multiplicative error processes
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20474
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 on high frequencies, such as cumulated trading volumes or the time between potentially simultaneously occurring market events. We introduce a flexible pointmass 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 liquid NYSE stocks, we show that the model captures both the dynamic and distribution properties of the data very well and is able to correctly predict future distributions. Keywords: High-frequency Data , Point-mass Mixture , Multiplicative Error Model , Excess Zeros , Semiparametric Specification Test , Market Microstructure JEL Classification: C22, C25, C14, C16, C51Nikolaus Hautsch; Peter Malec; Melanie Schienleworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20474Tue, 14 Dec 2010 14:51:32 +0100Pre-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 JEL-Classification: C14, C22, G10Nikolaus Hautsch; Mark Podolskijworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7936Sat, 04 Sep 2010 12:20:13 +0200The impact of macroeconomic news on quote adjustments, noise, and informational volatility
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7520
We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. Bid and ask returns are decomposed into a common ("efficient return") factor and two market-side-specific components capturing market microstructure effects. The corresponding variance components reflect information-driven and noise-induced volatilities. We find that all volatility components reveal distinct dynamics and are positively influenced by news. The proportion of noise-induced variances is highest before announcements and significantly declines thereafter. Moreover, news-affected responses in all volatility components are influenced by order flow imbalances. JEL Classification: C32, G14, E44 Keywords: Efficient Return, Macroeconomic Announcements, Microstructure Noise, Informational Volatility.Nikolaus Hautsch; Dieter Hess; David Veredasworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7520Thu, 25 Feb 2010 10:54:47 +0100Quantifying high-frequency market reactions to real-time news sentiment announcements
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7389
We examine intra-day market reactions to news in stock-specific sentiment disclosures. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we extract information on the relevance as well as the direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model using 20 second intervals. Analyzing a cross-section of stocks traded at the London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However, this is only true if news items are classified as highly relevant. Liquidity supply reacts less distinctly due to a stronger influence of idiosyncratic noise. Furthermore, evidence for abnormal highfrequency returns after news in sentiments is shown. Keywords. Firm-specific News , News Sentiment , High-frequency Data , Volatility , Liquidity , Abnormal Returns JEL_Classification: G14, C32Axel Groß-Klußmann; Nikolaus Hautschworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7389Wed, 13 Jan 2010 15:52:54 +0100The market impact of a limit order
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7289
Despite their importance in modern electronic trading, virtually no systematic empirical evidence on the market impact of incoming orders is existing. We quantify the short-run and long-run price effect of posting a limit order by proposing a high-frequency cointegrated VAR model for ask and bid quotes and several levels of order book depth. Price impacts are estimated by means of appropriate impulse response functions. Analyzing order book data of 30 stocks traded at Euronext Amsterdam, we show that limit orders have significant market impacts and cause a dynamic (and typically asymmetric) rebalancing of the book. The strength and direction of quote and spread responses depend on the incoming orders’ aggressiveness, their size and the state of the book. We show that the effects are qualitatively quite stable across the market. Cross-sectional variations in the magnitudes of price impacts are well explained by the underlying trading frequency and relative tick size. JEL-Classifications: C32, G14, G17 Keywords: Price Impact, Limit Order, Impulse Response Function, CointegrationNikolaus Hautsch; Ruihong Huangworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7289Wed, 02 Dec 2009 11:08:22 +0100A 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. JEL-Classifications: C14, C22 Keywords: Covariance Estimation, Blocking, Realized Kernel, Regularization, Microstructure, Asynchronous TradingNikolaus 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. JEL-Classifications: C14, C32, C53, G11 Keywords: Limit Order Book, Liquidity Risk, Semiparametric Model, Factor Structure, PredictionWolfgang Karl Härdle; Nikolaus Hautsch; Andrija Mihociworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7061Sun, 20 Sep 2009 13:37:34 +0200Analyzing interest rate risk : stochastic volatility in the term structure of government bond yields
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6275
We propose a Nelson-Siegel type interest rate term structure model where the underlying yield factors follow autoregressive processes with stochastic volatility. The factor volatilities parsimoniously capture risk inherent to the term structure and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the factor volatilities follow highly persistent processes. We show that slope and curvature risk have explanatory power for bond excess returns and illustrate that the yield and volatility factors are closely related to industrial capacity utilization, inflation, monetary policy and employment growth. JEL Classification: C5, E4, G1Nikolaus Hautsch; Yangguoyi Ouworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6275Thu, 16 Apr 2009 14:50:05 +0200Price adjustment to news with uncertain precision
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/5828
Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision. In practice, however, precision is rarely dis- closed. Therefore, we extend standard Bayesian learning, suggesting traders infer news’ precision from magnitudes of surprises and from external sources. We show that interactions of the different precision signals may result in highly nonlinear price responses. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model’s predictions and show that the effects are statistically and economically significant. JEL Classification: E44, G14Nikolaus Hautsch; Dieter Hess; Christoph Müllerworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/5828Wed, 24 Sep 2008 13:26:46 +0200Capturing common components in high-frequency financial time series : a multivariate stochastic multiplicative error model
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3558
We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects in the processes of high-frequency return volatilities, trade sizes and trading intensities. The model is estimated by simulated maximum likelihood using efficient importance sampling. Analyzing five minutes data from four liquid stocks traded at the New York Stock Exchange, we find that volatilities, volumes and intensities are driven by idiosyncratic dynamics as well as a highly persistent common factor capturing most causal relations and cross-dependencies between the individual variables. This confirms economic theory and suggests more parsimonious specifications of high-dimensional trading processes. It turns out that common shocks affect the return volatility and the trading volume rather than the trading intensity. JEL Classification: C15, C32, C52Nikolaus Hautschworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3558Fri, 02 Nov 2007 08:30:53 +0100