"Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets"

Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets.

Essays in the economics of innovation : an application to the case of Taiwan.

This research is an in-depth empirical investigation in the causes and consequences of the innovation activiy of Taiwan's manufacturing firms.The analysis relies on the exploitation of a panel of more than 27000 manufacturing firms observed between 1992 and 1995, 5000 of which can be classified as "innovation firms". The dissertation is organized in three chapters. The first chapter examines the determinants of firms' decision to innovate. We find an overall positive effect (variable across industries) of market concentration. We also observe a non-linear relationship between firms' size and the probability to innovate. Moreover, Taiwan's firms tend to innovate less toward the end of their life cycle. Finally, the growth rate of exportations (at the industry level) significantly increases the probability to innovate. In the second chapter, we estimate the effect of market structure and firm characteristics on the choice of innovation strategy. Four possible strategies are considered: to do R&D only, to import (disembodied) technology only, to mix both, and to forego innovation. We find positive effects of firm size and market structure on the probability of doing R&D (with or without importing technology). We also find that younger firms are more likely to innovate. Finally, we point out significant evidence of complementarity between doing R&D and importing technology. In the final chapter, we focus on the 5000 innovation firms to test the respective effects of importing technology and doing R&D on the growth of total factor and labor productivity. We check for complementarities by including an interaction effect into the regression model of productivity growth. We find that both activities contribute to the growth of labor productivity, whereas total factor productivity growth is mainly driven by R&D. Our results are consistent with possible complementarities between both activities, especially in high-tech industries.Productivity; Stratégies d'innovation; Productivité des entreprises; Technological complementarities; Complémentarités des sources technologiques; Innovation strategy;

Support for Distributed Dynamic Data Structures in C++

Traditionally, applications executed on distributed memory architectures in single-program multiple-data (SPMD) mode use distributed (multi-dimensional) data arrays. Good performance has been achieved by applying runtime techniques to such applications executing in a loosely synchronous manner. However, many applications utilize language constructs such as pointers to synthesize dynamic complex data structures, such as linked lists, trees and graphs, with elements consisting of complex composite data types. Existing runtime systems that rely on global indices cannot be used for these applications, as no global names or indices are imposed upon the elements of these data structures. A portable object-oriented runtime library is presented to support applications that use dynamic distributed data structures, including both arrays and pointer-based data structures. In particular, CHAOS++ deals with complex data types and pointer-based data structures by providing {\em mobile objects} and {\em globally addressable objects}. Preprocessing techniques are used to analyze communication patterns, and data exchange primitives are provided to carry out efficient data transfer. Performance results for applications taken from three distinct classes are also included to demonstrate the wide applicability of the runtime library. (Also cross-referenced as UMIACS-TR-95-19

T2: A Customizable Parallel Database For Multi-dimensional Data

As computational power and storage capacity increase, processing and analyzing large volumes of multi-dimensional datasets play an increasingly important part in many domains of scientific research. Several database research groups and vendors have developed object-relational database systems to provide some support for managing and/or visualizing multi-dimensional datasets. These systems, however, provide little or no support for analyzing or processing these datasets -- the assumption is that this is too application-specific to warrant common support. As a result, applications that process these datasets are analyzing large volumes of multi-dimensional datasets play an increasingly important part in many domains of scientific research. Several database research groups and vendors have developed object-relational database systems to provide some support for managing and/or visualizing multi-dimensional datasets. These systems, however, provide little or no support for analyzing or processing these datasets -- the assumption is that this is too application-specific to warrant common support. As a result, applications that process these datasets are usually decoupled from data storage and management, resulting in inefficiency due to copying and loss of locality. Furthermore, every application developer has to implement complex support for managing and scheduling the processing. Our study of a large set of scientific applications over the past three years indicates that the processing for such datasets is often highly stylized and shares several important characteristics. Usually, both the input dataset as well as the result being computed have underlying multi-dimensional grids. The basic processing step usually consists of transforming individual input items, mapping the transformed items to the output grid and computing output items by aggregating, in some way, all the transformed input items mapped to the corresponding grid point. In this paper, we present the design of T2, a customizable parallel database that integrates storage, retrieval and processing of multi-dimensional datasets. T2 provides support for common operations including index generation, data retrieval, memory management, scheduling of processing across a parallel machine and user interaction. It achieves its primary advantage from the ability to seamlessly integrate data retrieval and processing for a wide variety of applications and from the ability to maintain and jointly process multiple datasets with different underlying grids. (Also cross-referenced as UMIACS-TR-98-04

Querying Very Large Multi-dimensional Datasets in ADR - Extended Abstract

This paper addresses optimizing the execution of range queries into multi-dimensional datasets on distributed memory parallel machines within the Active Data Repository framework. ADR is an infrastructure that integrates storage, retrieval and processing of large multi-dimensional datasets on distributed memory parallel architectures with multiple disks attached to each node. We describe three potential strategies for efficient execution of such queries that employ different tiling and workload partitioning approaches. We evaluate scalability of these strategies for different application scenarios, varying both the number of processors and the input dataset size on a 128 processor IBM SP multicomputer. Also cross-referenced as UMIACS-TR-99-2

The Virtual Microscope

We present the design and implementation of the Virtual Microscope, a software system employing a client/server architecture to provide a realistic emulation of a high power light microscope. The system provides a form of completely digital telepathology, allowing simultaneous access to archived digital slide images by multiple clients. The main problem the system targets is storing and processing the extremely large quantities of data required to represent a collection of slides. The Virtual Microscope client software runs on the end user's PC or workstation, while database software for storing, retrieving and processing the microscope image data runs on a parallel computer or on a set of workstations at one or more potentially remote sites. We have designed and implemented two versions of the data server software. One implementation is a customization of a database system framework that is optimized for a tightly coupled parallel machine with attached local disks. The second implementation is component-based, and has been designed to accommodate access to and processing of data in a distributed, heterogeneous environment. We also have developed caching client software, implemented in Java, to achieve good response time and portability across different computer platforms. The performance results presented show that the Virtual Microscope systems scales well, so that many clients can be adequately serviced by an appropriately configured data server. (Also UMIACS-TR-2002-85

Titan A High-Performance Remote-Sensing Database

There are two major challenges for a high-performance remote-sensing database. First, it must provide low-latency retrieval of very large volumes of spatio-temporal data. This requires effective declustering and placement of a multi-dimensional dataset onto a large disk farm. Second, the order of magnitude reduction in data-size due to post-processing makes it imperative, from a performance perspective, that the postprocessing be done on the machine that holds the data. This requires careful coordination of computation and data retrieval. This paper describes the design, implementation and evaluation of {\em Titan}, a parallel shared-nothing database designed for handling remote-sensing data. The computational platform for Titan is a 16-processor IBM SP-2 with four fast disks attached to each processor. Titan is currently operational and contains about 24~GB of data from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-7 satellite. The experimental results show that Titan provides good performance for global queries, and interactive response times for local queries. (Also cross-referenced as UMIACS-TR-96-67

Cost Models for Query Processing Strategies in the Active Data Repository

Exploring and analyzing large volumes of data plays an increasingly important role in many domains of scientific research. We have been developing the Active Data Repository (ADR), an infrastructure that integrates storage, retrieval, and processing of large multi-dimensional scientific datasets on distributed memory parallel machines with multiple disks attached to each node. In earlier work, we proposed three strategies for processing range queries within the ADR framework. Our experimental results show that the relative performance of the strategies changes under varying application characteristics and machine configurations. In this work we describe analytical models to predict the average computation, I/O and communication operation counts of the strategies when input data elements are uniformly distributed in the attribute space of the output dataset, restricting the output dataset to be a regular d-dimensional array. We validate these models for various synthetic datasets and fo..

Essays in the economics of innovation : an application to the case of Taiwan

This research is an in-depth empirical investigation in the causes and consequences of the innovation activiy of Taiwan's manufacturing firms.The analysis relies on the exploitation of a panel of more than 27000 manufacturing firms observed between 1992 and 1995, 5000 of which can be classified as "innovation firms". The dissertation is organized in three chapters. The first chapter examines the determinants of firms' decision to innovate. We find an overall positive effect (variable across industries) of market concentration. We also observe a non-linear relationship between firms' size and the probability to innovate. Moreover, Taiwan's firms tend to innovate less toward the end of their life cycle. Finally, the growth rate of exportations (at the industry level) significantly increases the probability to innovate. In the second chapter, we estimate the effect of market structure and firm characteristics on the choice of innovation strategy. Four possible strategies are considered: to do R&D only, to import (disembodied) technology only, to mix both, and to forego innovation. We find positive effects of firm size and market structure on the probability of doing R&D (with or without importing technology). We also find that younger firms are more likely to innovate. Finally, we point out significant evidence of complementarity between doing R&D and importing technology. In the final chapter, we focus on the 5000 innovation firms to test the respective effects of importing technology and doing R&D on the growth of total factor and labor productivity. We check for complementarities by including an interaction effect into the regression model of productivity growth. We find that both activities contribute to the growth of labor productivity, whereas total factor productivity growth is mainly driven by R&D. Our results are consistent with possible complementarities between both activities, especially in high-tech industries.(ECON 3)--UCL, 200

Doing R&D and/or Importing Technologies: The Critical Importance of Firm Size in Taiwan’s Manufacturing Industries

International audienceWe analyze the relationship between firm size and innovation inputs in Taiwan, Two inputs are considered: R&D and technology imports. Building on an existing theoretical framework, we test this relationship by estimating bivariate Tobit models in twenty 2-digit industries, using a panel of 27,754 firms observed from 1992 to 1995. We find that, in all industries, R&D intensity and/or technology imports intensity depend strongly on firm size, following an "inverted-U" pattern. Moreover, we find that most industries are only "mildly Schumpeterian". Finally, our results provide some empirical evidence for complementarity between R&D and technology imports in the innovation process