Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence

We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale 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

The merit of high-frequency data in portfolio allocation

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

Limit Order Flow, Market Impact and Optimal Order Sizes: Evidence from NASDAQ TotalView-ITCH Data

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. Moreover, only very few market orders walk through the book, i.e., directly move the best ask or bid quote. Estimates of impulse-response functions on the basis of a cointegrated VAR model for quotes and market depth allow us to quantify the market impact of incoming limit orders. We propose a method to predict the optimal size of a limit order conditional on its position in the book and a given fixed level of expected market impact

Time Varying Quantile Lasso

In the present paper we study the dynamics of penalization parameter λ of the least absolute shrinkage and selection operator (Lasso) method proposed by Tibshirani (1996) and extended into quantile regression context by Li and Zhu (2008). The dynamic behaviour of the parameter λ can be observed when the model is assumed to vary over time and therefore the fitting is performed with the use of moving windows. The proposal of investigating time series of λ and its dependency on model characteristics was brought into focus by Hardle et al. (2016), which was a foundation of FinancialRiskMeter (http://frm.wiwi.hu-berlin.de). Following the ideas behind the two aforementioned projects, we use the derivation of the formula for the penalization parameter λ as a result of the optimization problem. This reveals three possible effects driving λ; variance of the error term, correlation structure of the covariates and number of nonzero coefficients of the model. Our aim is to disentangle these three effect and investigate their relationship with the tuning parameter λ, which is conducted by a simulation study. After dealing with the theoretical impact of the three model characteristics on λ, empirical application is performed and the idea of implementing the parameter λ into a systemic risk measure is presented. The codes used to obtain the results included in this work are available on http://quantlet.de/d3/ia/

The Microstructure of a U.S. Treasury ECN: The Brokertec Platform

This paper assesses the microstructure of the U.S. Treasury securities market, using newly available tick data from the BrokerTec electronic trading platform. Examining trading activity, bid-ask spreads, and depth for on-the-run two-, three-, five-, ten-, and thirty-year Treasury securities, we find that market liquidity is greater than that found in earlier studies that use data only from voice-assisted brokers. We find that the price effect of trades on BrokerTec is quite small and is even smaller once order-book information is considered. Moreover, order-book information itself is shown to affect prices. We also explore a novel feature of BrokerTec - the ability to enter hidden ('iceberg') orders - and find that, as predicted by theory, such orders are more common when price volatility is higher

Estimating Term Structure Changes Using Principal Component Analysis in Indian Sovereign Bond Market

This paper analyses the India sovereign yield to find out the principal factors affecting the term structure of interest rate changes. We apply Principal Component Analysis (PCA) on our data consisting of zero coupon interest rates derived from government bond trading using Nelson-Siegel functional form. This decomposition of the yield curve highlights important relationship between identified factors and metrics of the term structure shape. The empirical findings support statistical similarities between the Indian yield curve and term structure studies of major countries