Nikolaus Hautsch

University of Vienna | UniWien · Institut für Statistik und Operations Research

We extend the classic ”martingale-plus-noise” model for high-frequency returns to accommodate an error correction mechanism and endogenous pricing errors. It is motivated by (i) novel empirical evidence documenting that microstructure noise exhibits frequently changing patterns of serial dependence which are interwoven with innovations to the effic...

This paper provides a guide to high-frequency option trade and quote data disseminated by the Options Price Reporting Authority (OPRA). We present a comprehensive overview of the U.S. option market, including details on market regulation and the trading processes for all 16 constituent option exchanges. We review the existing literature that utiliz...

A counterparty credit limit (CCL) is a limit that is imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. CCLs help institutions to mitigate counterparty credit risk via selective diversification of their exposures. In this paper, we analyse how CCLs impact the prices that institutions pay for their t...

We propose a multivariate dynamic intensity peaks‐over‐threshold model to capture extremes in multivariate return processes. The random occurrence of extremes is modeled by a multivariate dynamic intensity model, while temporal clustering of their size is captured by an autoregressive multiplicative error model. Applying the model to daily returns...

We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized versions of efficient allocati...

Distributed ledger technologies rely on consensus protocols confronting traders with random waiting times until the transfer of ownership is accomplished. This time-consuming settlement process exposes arbitrageurs to price risk and imposes limits to arbitrage. We derive theoretical arbitrage boundaries under general assumptions and show that they...

Exploiting Nasdaq order book data and difference-in-differences methodology, we identify the distinct effects of trading pause mechanisms introduced on US stock exchanges after May 2010. We show that the mere existence of such a regulation makes market participants behave differently in anticipation of a pause. Pauses enhance price discovery during...

A counterparty credit limit (CCL) is a limit imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. Although CCLs are designed to help institutions mitigate counterparty risk by selective diversification of their exposures, their implementation restricts the liquidity that institutions can access in an...

We theoretically and empirically study large-scale portfolio allocation problems when transaction costs are taken into account in the optimization problem. We show that transaction costs act on the one hand as a turnover penalization and on the other hand as a regularization, which shrinks the covariance matrix. As an empirical framework, we propos...

Measuring and modelling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency re- turn...

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 mome...

We introduce a 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 copula-type transformation. Bas...

We propose a multivariate dynamic intensity peaks-over-threshold model to capture ex- treme events in a multivariate time series of returns. The random occurrence of extreme events exceeding a threshold is modeled by means of a multivariate dynamic intensity model allowing for feedback effects between the individual processes. We propose alternativ...

We propose a multivariate dynamic intensity peaks-over-threshold model to capture extreme events in a multivariate time series of returns. The random occurrence of extreme events exceeding a threshold is modeled by means of a multivariate dynamic intensity model allowing for feedback effects between the individual processes. We propose alternative...

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 specifi...

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 mome...

We propose a new estimator for the spot covariance matrix of a multi-dimensional continu-ous 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 para-metric spectral covariance estimates. The latter originate from a local method of mo...

We develop a model of an order-driven exchange competing for order flow with off-exchange trading mechanisms. Liquidity suppliers face a trade-off between benefits and costs of order exposure. If they display trading intentions, they attract additional trade demand. We show, in equilibrium, hiding trade intentions can induce mis-coordination betwee...

We propose a framework for estimating network-driven time-varying systemic risk contributions that is applicable to a high-dimensional financial system. Tail risk de-pendencies and contributions are estimated based on a penalized two-stage fixed-effects quantile approach, which explicitly links bank interconnectedness to sys-temic risk contribution...

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevanc...

An efficient estimator is constructed for the quadratic covaria-tion or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non-synchronous observations under high-frequency asymptotics. Our approach relies on an asymptot-ically equivalent continuous-time observation model where a local generalised method of mo...

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 efficien...

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discret...

We propose a local adaptive multiplicative error model (MEM) accommodating timevarying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analyzing one-minute cumulative trading volumes of five larg...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial crisis. HF-based covariance matrix predictions are obtained...

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 mome...

An efficient estimator is constructed for the quadratic covaria- tion or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non-synchronous observations under high-frequency asymptotics. Our approach relies on an asymptot- ically equivalent continuous-time observation model where a local generalised method of...

We show that the excessive use of hidden orders causes artificial price pressures and abnormal asset returns. Using a simple game-theoretical setting, we demonstrate that this effect naturally arises from mis-coordination in trading schedules between traders, when suppliers of liquidity do not sufficiently disclose their trade intentions. As a resu...

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. Est...

Multiplicative error models (MEM) became a standard tool for modeling conditional durations of intraday transactions, realized volatilities, and trading volumes. The parametric estimation of the corresponding multivariate model, the so-called vector MEM (VMEM), requires a specification of the joint error term distribution, which is due to the lack...

An efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non-synchronous observations under high-frequency asymptotics. Our approach relies on an asymptotically equivalent continuous-time observation model where a local generalised method of mome...

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 stoc...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial crisis. HF-based covariance matrix predictions are obtained...

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevanc...

In this paper, we develop and apply Bayesian inference for an extended Nelson- Siegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimat...

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...

Multiplicative error models (MEM) became a standard tool for modeling conditional durations of intraday transactions, realized volatilities and trading volumes. The parametric estimation of the corresponding multivariate model, the so-called vector MEM (VMEM), requires a specification of the joint error term distribution, which is due to the lack o...

This chapter focusses on multivariate extensions of multiplicative error models. The basic multivariate (or vector) multiplicative error model is introduced in Sect. 7.1.1. We discuss specification, statistical inference and provide empirical illustrations. Section 7.2 is devoted to stochastic vector MEMs corresponding to multivariate versions of u...

The terminology multiplicative error model (MEM) has been introduced by Engle (2002b) for a general class of time series models for positive-valued random variables which are decomposed into the product of their conditional mean and a positive-valued error term. Such models might be alternatively classified as autoregressive conditional mean models...

Liquidity has been recognized as an important determinant of the efficient working of a market. Following the conventional definition of liquidity, an asset is considered as being liquid if it can be traded quickly, in large quantities and with little impact on the price. According to this concept, the measurement of liquidity requires to account f...

In this chapter, we discuss dynamic models for discrete-valued data and quote processes. As illustrated in Chap. 4, the time series of the number of events in a given time interval yields a counting process and provides an alternative way to characterize the underlying point process. Section 13.1 presents a class of univariate autoregressive models...

This chapter gives an overview of institutional and theoretical market microstructure foundations. Section 2.1 introduces to the institutional framework of trading on modern financial markets. We discuss different forms of trading, types of traders as well as types of orders. Moreover, we present fundamental types of market structures, most importa...

This chapter discusses different ways to estimate intraday volatility. Section 8.1 presents realized measures to estimate intraday quadratic variation. Here, we compactly illustrate fundamental approaches, such as the maximum likelihood estimator by Aït-Sahalia et al. (2005), the realized kernel estimator by Barndorff-Nielsen et al. (2008a) as well...

In this chapter, we present generalizations of the basic multiplicative error model as introduced in Chap. 5. Section 6.1 discusses a class of ACD models which can be presented in terms of a generalized polynomial random coefficient model according to Carrasco and Chen (2002). We illustrate various special cases, discuss the theoretical properties...

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 and balance sheet information, we define the realized systemic risk beta as the total time-v...

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. Ba...

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...

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. Ba...

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 ensur...

We propose a local adaptive multiplicative error model (MEM) accommodating time-varying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analysing 1-minute cumulative trading volumes of five large...

Bayesian learning provides a core concept of information processing in financial markets. Typically it is assumed that market participants perfectly know the quality of released news. However, in practice, news’ precision is rarely disclosed. Therefore, we extend standard Bayesian learning allowing traders to infer news’ precision from two differen...

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 infe...

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 infe...

The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity r...

This chapter provides the methodological background for the specification and estimation of financial point processes. We give a brief introduction to the fundamental statistical concepts and the basic ways to model point processes. For ease of introduction, we restrict our attention to non-dynamic point processes. In Sect.4.1, we discuss the most...

In this chapter, we present financial high-frequency data and their empirical properties. We discuss data preparation issues and show the statistical properties of various high-frequency variables based on blue chip assets traded at the NYSE, NASDAQ and XETRA. Section 3.1 focuses on peculiar problems which have to be taken into account when transac...

Kurzfassung: This paper investigates the time between transactions on financial markets. It is assumed that the interval between transactions is a random variable and the relation- ship between the probability to observe a transaction at each instant of time and the type of the previous trade is investigated. To estimate these effects, a semiparame...

This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained over longer horizons than previous studies have shown. We...

This paper addresses the open debate about the effectiveness and practical relevance of high-frequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained over longer horizons than previous studies have shown. W...

In this paper, we provide new empirical evidence on order submission activity and price impacts of limit orders at NASDAQ. Employing NASDAQ TotalView-ITCH data, we find that market participants dominantly submit limit orders with sizes equal to a round lot. Most limit orders are canceled almost immediately after submission if not getting executed....

In this paper, we provide new empirical evidence on order submission activity and price impacts of limit orders at NASDAQ. Employing NASDAQ TotalView-ITCH data, we find that market participants dominantly submit limit orders with sizes equal to a round lot. Most limit orders are canceled almost immediately after submission if not getting executed....

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discret...

We examine high-frequency market reactions to an intraday stock-specific news flow. Using unique pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the indicated relevance, novelty and direction of company-specific news. Employing a high-frequency VAR model based on 20 s data o...

In this paper, we quantify financial companies' time-varying marginal contribution to sys-temic risk. We define the so-called 'systemic risk beta' as the marginal effect of a bank's Value-at-Risk (VaR) on the VaR of the entire financial system. The product of a company's systemic risk beta and its VaR yields the percentage increase of the system Va...

We propose the realized systemic risk beta as a measure of financial companies’ contribution to systemic risk, given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the realized systemic risk beta as the total time-v...

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 specifi...

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 flexib...

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 stoc...

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. We decompose bid and ask returns into a common ("efficient return") factor and two market-side-specific components capturing market microstructure effects. The corresponding var...

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. We decompose bid and ask returns into a common ("efficient return") factor and two market-side-specific components capturing market microstructure effects. The corresponding var...

In this paper, we develop and apply Bayesian inference for an extended Nelson-Siegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate...

We model the dynamics of large-dimensional covariances on the basis of a multi-scale spectral decomposition of a realized covariance. Volatilities, correlation eigenvalues and eigenvectors are allowed to evolve on different frequencies with eigenvector dynamics being the slowest-moving and volatility dynamics being the fastest-moving processes. Co-...

We examine intraday market reactions to stock-specific news. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the relevance and the direction of company-specific news. Concise news-implied reactions in returns, volatility as well as liquidity demand and supply are quant...

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 liqui...

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...

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 ensur...

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...

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 liqui...

We suggest a robust form of conditional moment test as a constructive test for func- tional misspecification in multiplicative error models. The proposed test has power solely against violations of the conditional mean restriction but is not affected by any other type of model misspecification. Monte-Carlo investigations show that an appro- priate...

We model high-frequency trading processes by a multivariate multiplicative error model that is driven by component-specific observation driven dynamics as well as a common latent autoregressive factor. The model is estimated using efficient importance sampling techniques. Applying the model to 5Â min return volatilities, trade sizes and trading int...

In this paper, we model the buy and sell arrival process in the limit order book market at the Australian Stock Exchange. Using a bivariate autoregressive intensity model we analyze the contemporaneous buy and sell intensity as a function of the state of the market. We find evidence that trading decisions are both information as well as liquidity d...

In this paper, we review the most common specifications of discrete-time stochas- tic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap- proach which is easily implemented in empirical applications and financial practice and can be straig...

In this paper, we study the dynamic interdependencies between high-frequency volatility, liquidity demand as well as trading costs in an electronic limit order book market. Using data from the Australian Stock Exchange we model 1-min squared mid-quote returns, average trade sizes, number of trades and average (excess) trading costs per time interva...

We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term struc- ture and are associated with the time-varying uncertainty of the yield curve’s level, slope and curva...