Correlation, price discovery and co-movement of ABS and equity

Asset-backed securitization (ABS) has become a viable and increasingly attractive risk management and refinancing method either as a standalone form of structured finance or as securitized debt in Collateralized Debt Obligations (CDO). However, the absence of industry standardization has prevented rising investment demand from translating into market liquidity comparable to traditional fixed income instruments, in all but a few selected market segments. Particularly low financial transparency and complex security designs inhibits profound analysis of secondary market pricing and how it relates to established forms of external finance. This paper represents the first attempt to measure the intertemporal, bivariate causal relationship between matched price series of equity and ABS issued by the same entity. In a two-dimensional linear system of simultaneous equations we investigate the short-term dynamics and long-term consistency of daily secondary market data from the U.K. Sterling ABS/MBS market and exchange traded shares between 1998 and 2004 with and without the presence of cointegration. Our causality framework delivers compelling empirical support for a strong co-movement between matched price series of ABS-equity pairs, where ABS markets seem to contribute more to price discovery over the long run. Controlling for cointegration, risk-free interest and average market risk of corporate debt hardly alters our results. However, once we qualify the magnitude and direction of price discovery on various security characteristics, such as the ABS asset class, we find that ABS-equity pairs with large-scale CMBS/RMBS and credit card/student loan ABS reveal stronger lead-lag relationships and joint price dynamics than whole business ABS. JEL Classifications: G10, G12, G2

Cross-listing, price discovery and the informativeness of the trading process

This paper analyzes the price discovery process of a set of Spanish stocks cross-listed at the NYSE. Our methodology distinguishes between two sources of information asymmetries. Market-specific information that is revealed through the trading process and public disclosures simultaneously revealed to both markets but subject to informed judgments. We compute the information share of the Spanish and U.S. trading activity during the daily 2-hour overlapping interval. Empirical results show that the NYSE contribution to the price discovery process is not negligible. But the NYSE information is basically trade-unrelated

Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure

Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

On the timeliness of price discovery

Price discovery is the process whereby value-relevant, private information becomes impounded or reflected in a stock's publicly-observable market price. The timeliness of price discovery refers to how quickly that process takes effect. There is no reason to believe either that all private information is discovered equally quickly or that price discovery is equally speedy for all firms. The latter observation suggests it would be worthwhile knowing why the timeliness of price discovery differs across firms, even the more so in an environment where all listed companies by law must disclose most material price-sensitive information as soon as they become aware of it. The other observation, that not all private information is discovered equally quickly, implies we should focus on a material, periodic event when we compare timeliness across firms. A good candidate is the announcement of the company's annual results, since for many years is has been known that annual earnings alone captures at least half the value-relevant information released by the average firm over the 12 months leading up to this date. We use various approaches to explore measures of timeliness and what they can tell us. We review a number of studies that have considered various aspects of timeliness in different countries and extend and contrast their findings. We also examine the relationship between the timeliness of price discovery and analogous measures based upon firms' formal disclosures to the share market and upon analysts' consensus earnings forecasts. Finally, we report on an issue of major concern to regulators and market operators, namely the influence of corporate governance on the timeliness of price discovery

Anti-correlated Soft Lags in the Intermediate State of Black Hole Source GX 339-4

We report the few hundred second anti-correlated soft lags between soft and hard energy bands in the source GX 339-4 using RXTE observations. In one observation, anti-correlated soft lags were observed using the ISGRI/INTEGRAL hard energy band and the PCA/RXTE soft energy band light curves. The lags were observed when the source was in hard and soft intermediate states, i.e., in a steep power-law state.We found that the temporal and spectral properties were changed during the lag timescale. The anti-correlated soft lags are associated with spectral variability during which the geometry of the accretion disk is changed. The observed temporal and spectral variations are explained using the framework of truncated disk geometry. We found that during the lag timescale, the centroid frequency of quasi-periodic oscillation is decreased, the soft flux is decreased along with an increase in the hard flux, and the power-law index steepens together with a decrease in the disk normalization parameter. We argue that these changes could be explained if we assume that the hot corona condenses and forms a disk in the inner region of the accretion disk. The overall spectral and temporal changes support the truncated geometry of the accretion disk in the steep power-law state or in the intermediate state.Comment: published in ApJ, 9 pages, 8 figure

Clustering Time Series from Mixture Polynomial Models with Discretised Data

Clustering time series is an active research area with applications in many fields. One common feature of time series is the likely presence of outliers. These uncharacteristic data can significantly effect the quality of clusters formed. This paper evaluates a method of over-coming the detrimental effects of outliers. We describe some of the alternative approaches to clustering time series, then specify a particular class of model for experimentation with k-means clustering and a correlation based distance metric. For data derived from this class of model we demonstrate that discretising the data into a binary series of above and below the median improves the clustering when the data has outliers. More specifically, we show that firstly discretisation does not significantly effect the accuracy of the clusters when there are no outliers and secondly it significantly increases the accuracy in the presence of outliers, even when the probability of outlier is very low

Gamma Ray Burst Prompt correlations

The mechanism responsible for the prompt emission of gamma-ray bursts (GRBs) is still a debated issue. The prompt phase-related GRB correlations can allow to discriminate among the most plausible theoretical models explaining this emission. We present an overview of the observational two-parameter correlations, their physical interpretations, their use as redshift estimators and possibly as cosmological tools. The nowadays challenge is to make GRBs, the farthest stellar-scaled objects observed (up to redshift $z=9.4$), standard candles through well established and robust correlations. However, GRBs spanning several orders of magnitude in their energetics are far from being standard candles. We describe the advances in the prompt correlation research in the past decades, with particular focus paid to the discoveries in the last 20 years