A Critical Review of "Automatic Patch Generation Learned from Human-Written Patches": Essay on the Problem Statement and the Evaluation of Automatic Software Repair

At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs. We strongly disagree with key points of this paper. Our critical review has two goals. First, we aim at explaining why we disagree with Kim and colleagues and why the reasons behind this disagreement are important for research on automatic software repair in general. Second, we aim at contributing to the field with a clarification of the essential ideas behind automatic software repair. In particular we discuss the main evaluation criteria of automatic software repair: understandability, correctness and completeness. We show that depending on how one sets up the repair scenario, the evaluation goals may be contradictory. Eventually, we discuss the nature of fix acceptability and its relation to the notion of software correctness.Comment: ICSE 2014, India (2014

Long Memory Dynamics for Multivariate Dependence under Heavy Tails

We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the data. In the empirical study for four Dow Jones equities, we find that the degree of memory in the volatilities of the equity return series is similar, while the degree of memory in correlations between the series varies significantly. The forecasts from our model are compared with high-frequency realised volatility and dependence measures. The forecast accuracy is overall higher compared to those from some well-known competing benchmark models

Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation

We model 1927-1997 U.S. business failure rates using a time series approach based on unobserved components. Clear evidence is found of cyclical behavior in default rates. The cycle has a period of around 10 years. We also detect longer term movements in default probabilities and default correlations. Our findings have important implications for portfolio credit risk analysis. First, a static analysis of portfolio credit risk can underestimate credit risk significantly by not accounting for the dynamic and cyclical behaviour of default probabilities. Second, estimating default correlations over long horizons without accounting for time variation may lead to misspecified risk management models. We highlight the main effects in an actual credit risk experiment, addressing the issue of pro-cyclicality in ratings and capital buffer formation. It turns out that dynamic models anticipate much better on required capital buffer increases than rating strategies based on recent historical data. In this way, dynamic credit risk models may help to alleviate part of the pro-cyclicality problem

Spot variance path estimation and its application to high frequency jump testing

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used to extend an existing high frequency jump test statistic, to detect arrival times of jumps and to obtain distributional characteristics of detected jumps. The effectiveness of our approach is explored through Monte Carlo simulations. It is shown that sparse sampling for mitigating the impact of microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach we can analyze high frequency price observations that are contaminated with microstructure noise without the need for sparse sampling, say at fifteen minute intervals. An empirical illustration is presented for the intraday EUR/USD exchange rates. Our main finding is that fewer jumps are detected when sampling intervals increase

The mechanism of the catalytic oxidation of hydrogen sulfide *1: III. An electron spin resonance study of the sulfur catalyzed oxidation of hydrogen sulfide

ESR experiments on the oxidation of hydrogen sulfide were performed in the temperature range 20–150 °C. Alumina, active carbon and molecular sieve zeolite 13X were investigated as catalysts. For zeolite 13X it was demonstrated that the reaction is autocatalytic and that sulfur radicals are the active sites for oxygen chemisorption. The intensity of the sulfur radical ESR signal, which is related to the degree of conversion of these radicals, by oxygen, fits in with an oxidation-reduction mechanism.\ud \ud The sulfur-oxygen radical species, which appear when oxygen is admitted to sulfur radicals, are assigned to sulfur chains containing one or two oxygen atoms at the end of the chain. It is very likely that these sulfur-oxygen radicals are intermediates in the proposed mechanism. The formation of the byproduct SO2 from SxO2 · − at temperatures above 175 °C is also visible in the ESR spectrum.\ud \ud On the basis of the experiments it is concluded that in the mechanism of H2S oxidation on active carbons, carbon radicals do not play an important role

Realized wishart-garch:A score-driven multi-Asset volatility model

We propose a novel multivariate GARCH model that incorporates realized measures for the covariance matrix of returns. The joint formulation of a multivariate dynamic model for outer-products of returns, realized variances, and realized covariances leads to a feasible approach for analysis and forecasting. The updating of the covariance matrix relies on the score function of the joint likelihood function based on Gaussian and Wishart densities. The dynamic model is parsimonious while the analysis relies on straightforward computations. In a Monte Carlo study, we show that parameters are estimated accurately for different small sample sizes. We illustrate the model with an empirical in-sample and out-of-sample analysis for a portfolio of 15 U.S. financial assets

Missing observations in observation-driven time series models

We argue that existing methods for the treatment of missing observations in time-varying parameter observation-driven models lead to inconsistent inference. We provide a formal proof of this inconsistency for a Gaussian model with time-varying mean. A Monte Carlo simulation study supports this theoretical result and illustrates how the inconsistency problem extends to score-driven and, more generally, to observation-driven models, which include well-known models for conditional volatility. To overcome the problem of inconsistent inference, we propose a novel estimation procedure based on indirect inference. This easy-to-implement method delivers consistent inference. The asymptotic properties of the new method are formally derived. Our proposed estimation procedure shows a promising performance in a Monte Carlo simulation exercise as well as in an empirical study concerning the measurement of conditional volatility from financial returns data