Volatility Clustering in High-Frequency Data: A self-fulfilling prophecy?

Clustering volatility is shown to appear in a simple market model with noise trading simply because agents use volatility forecasting models. At the core of the argument lies a feed-back mechanism linking past observed volatility to present observed volatility. Its stability properties are critical as to what kind of volatility will ultimately be observed.

Panel unit root testing and the martingale difference hypothesis for German stocks

Several panel unit root tests based on different ways to account for cross-unit dependence are reviewed. The note then illustrates the tests by checking whether the martingale difference hypothesis is appropriate for stock prices on the German stock market: according to the martingale difference hypothesis, logarithmized stock prices follow an integrated process without short-run dynamics. Compared with usual tests for no autocorrelation, unit root tests do not require strong moment conditions and can cope with stock returns series exhibiting infinite kurtosis. Evidence against the martingale difference hypothesis is found in a panel of 30 DAX stocks observed daily between 2004 and 2007.Stock price behavior, Dickey-Fuller test, fractional integration, cross-dependent panel, cross-correlation

Accuracy of Author Names in Bibliographic Data Sources: An Italian Case Study

We investigate the accuracy of how author names are reported in bibliographic records excerpted from four prominent sources: WoS, Scopus, PubMed, and CrossRef. We take as a case study 44,549 publications stored in the internal database of Sapienza University of Rome, one of the largest universities in Europe. While our results indicate generally good accuracy for all bibliographic data sources considered, we highlight a number of issues that undermine the accuracy for certain classes of author names, including compound names and names with diacritics, which are common features to Italian and other Western languages

Testing for the Cointegrating Rank of a Vector Autoregressive Process with Uncertain Deterministic Trend Term

When applying Johansen's procedure for determining the cointegrating rank to systems of variables with linear deterministic trends, there are two possible tests to choose from. One test allows for a trend in the cointegration relations and the other one restricts the trend to be orthogonal to the cointegration relations. The first test is known to have reduced power relative to the second one if there is in fact no trend in the cointegration relations, whereas the second one is based on a misspecified model if the linear trend is not orthogonal to the cointegration relations. Hence, the treatment of the linear trend term is crucial for the outcome of the rank determination procedure. We compare two alternative testing strategies which are applicable if there is uncertainty regarding the proper trend specification. In the first one a specific cointegrating rank is rejected if one of the two tests rejects and in the second one the trend term is decided upon by a pretest. The first strategy is shown to be preferable in applied work.Cointegration analysis, likelihood ratio test, vector autoregressive model, vector error correction model

Rethinking Pointer Reasoning in Symbolic Execution

Symbolic execution is a popular program analysis technique that allows seeking for bugs by reasoning over multiple alternative execution states at once. As the number of states to explore may grow exponentially, a symbolic executor may quickly run out of space. For instance, a memory access to a symbolic address may potentially reference the entire address space, leading to a combinatorial explosion of the possible resulting execution states. To cope with this issue, state-of-the-art executors concretize symbolic addresses that span memory intervals larger than some threshold. Unfortunately, this could result in missing interesting execution states, e.g., where a bug arises. In this paper we introduce MemSight, a new approach to symbolic memory that reduces the need for concretization, hence offering the opportunity for broader state explorations and more precise pointer reasoning. Rather than mapping address instances to data as previous tools do, our technique maps symbolic address expressions to data, maintaining the possible alternative states resulting from the memory referenced by a symbolic address in a compact, implicit form. A preliminary experimental investigation on prominent benchmarks from the DARPA Cyber Grand Challenge shows that MemSight enables the exploration of states unreachable by previous techniques

Distributed Approximation Algorithms for Weighted Shortest Paths

A distributed network is modeled by a graph having $n$ nodes (processors) and diameter $D$. We study the time complexity of approximating {\em weighted} (undirected) shortest paths on distributed networks with a $O(\log n)$ {\em bandwidth restriction} on edges (the standard synchronous \congest model). The question whether approximation algorithms help speed up the shortest paths (more precisely distance computation) was raised since at least 2004 by Elkin (SIGACT News 2004). The unweighted case of this problem is well-understood while its weighted counterpart is fundamental problem in the area of distributed approximation algorithms and remains widely open. We present new algorithms for computing both single-source shortest paths (\sssp) and all-pairs shortest paths (\apsp) in the weighted case. Our main result is an algorithm for \sssp. Previous results are the classic $O(n)$-time Bellman-Ford algorithm and an $\tilde O(n^{1/2+1/2k}+D)$-time $(8k\lceil \log (k+1) \rceil -1)$-approximation algorithm, for any integer $k\geq 1$, which follows from the result of Lenzen and Patt-Shamir (STOC 2013). (Note that Lenzen and Patt-Shamir in fact solve a harder problem, and we use $\tilde O(\cdot)$ to hide the O(\poly\log n) term.) We present an $\tilde O(n^{1/2}D^{1/4}+D)$-time $(1+o(1))$-approximation algorithm for \sssp. This algorithm is {\em sublinear-time} as long as $D$ is sublinear, thus yielding a sublinear-time algorithm with almost optimal solution. When $D$ is small, our running time matches the lower bound of $\tilde \Omega(n^{1/2}+D)$ by Das Sarma et al. (SICOMP 2012), which holds even when $D=\Theta(\log n)$, up to a \poly\log n factor.Comment: Full version of STOC 201

Maintenance of Strongly Connected Component in Shared-memory Graph

In this paper, we present an on-line fully dynamic algorithm for maintaining strongly connected component of a directed graph in a shared memory architecture. The edges and vertices are added or deleted concurrently by fixed number of threads. To the best of our knowledge, this is the first work to propose using linearizable concurrent directed graph and is build using both ordered and unordered list-based set. We provide an empirical comparison against sequential and coarse-grained. The results show our algorithm's throughput is increased between 3 to 6x depending on different workload distributions and applications. We believe that there are huge applications in the on-line graph. Finally, we show how the algorithm can be extended to community detection in on-line graph.Comment: 29 pages, 4 figures, Accepted in the Conference NETYS-201