1,571 research outputs found
efficiency and evolution of R&D Networks.
This work introduces a new model to investigate the efficiency and evolution of networks of firms exchanging knowledge in R&D partnerships. We first examine the efficiency of a given network structure from the point of view of maximizing total profits in the industry. We show that the efficient network structure depends on the marginal cost of collaboration. When the marginal cost is low, the complete graph is efficient. However, a high marginal cost implies that the efficient network is sparser and has a core-periphery structure. Next, we examine the evolution of the network structure when the decision on collaborating partners is decentralized. We show the existence of multiple equilibrium structures which are in general inefficient. This is due to (i) the path dependent character of the partner selection process, (ii) the presence of knowledge externalities and (iii) the presence of severance costs involved in link deletion. Finally, we study the properties of the emerging equilibrium networks and we show that they are coherent with the stylized facts on R&D networks.R&D networks;technology spillovers;network efficiency;network formation;
The Efficiency and Evolution of R&D Networks
This work introduces a new model to investigate the efficiency and evolution of networks of firms exchanging knowledge in R&D partnerships. We first examine the efficiency of a given network structure in terms of the maximization of total profits in the industry. We show that the efficient network structure depends on the marginal cost of collaboration. When the marginal cost is low, the complete graph is efficient. However, a high marginal cost implies that the efficient network is sparser and has a core-periphery structure. Next, we examine the evolution of the network struc- ture when the decision on collaborating partners is decentralized. We show the existence of mul- tiple equilibrium structures which are in general inefficient. This is due to (i) the path dependent character of the partner selection process, (ii) the presence of knowledge externalities and (iii) the presence of severance costs involved in link deletion. Finally, we study the properties of the emerg- ing equilibrium networks and we show that they are coherent with the stylized facts of R&D net- works.R&D networks, technology spillovers, network efficiency, network formation
The scale-free topology of market investments
We propose a network description of large market investments, where both
stocks and shareholders are represented as vertices connected by weighted links
corresponding to shareholdings. In this framework, the in-degree () and
the sum of incoming link weights () of an investor correspond to the number
of assets held (\emph{portfolio diversification}) and to the invested wealth
(\emph{portfolio volume}) respectively. An empirical analysis of three
different real markets reveals that the distributions of both and
display power-law tails with exponents and . Moreover, we find
that scales as a power-law function of with an exponent .
Remarkably, despite the values of , and differ across
the three markets, they are always governed by the scaling relation
. We show that these empirical findings can be
reproduced by a recent model relating the emergence of scale-free networks to
an underlying Paretian distribution of `hidden' vertex properties.Comment: Final version accepted for publication on Physica
Systemic risk from investment similarities
Network theory proved recently to be useful in the quantification of many properties of financial systems. The analysis of the structure of investment portfolios is a major application since their eventual correlation and overlap impact the actual risk by individual investors. We investigate the bipartite network of US mutual fund portfolios and their assets. We follow its evolution during the Global Financial Crisis and study the diversification, as understood in modern portfolio theory, and the similarity of the investments of different funds. We show that, on average, portfolios have become more diversified and less similar during the crisis. However, we also find that large overlap is far more likely than expected from benchmark models of random allocation of investments. This indicates the existence of strong correlations between fund investment strategies. We exploit a deliberately simplified model of shock propagation to identify a systemic risk component stemming from the similarity of portfolios. The network is still partially vulnerable after the crisis because of this effect, despite the increase in the diversification of multi asset portfolios. Diversification and similarity should be taken into account jointly to properly assess systemic risk
Credit Default Swaps Drawup Networks: Too Tied To Be Stable?
We analyse time series of CDS spreads for a set of major US and European
institutions on a pe- riod overlapping the recent financial crisis. We extend
the existing methodology of {\epsilon}-drawdowns to the one of joint
{\epsilon}-drawups, in order to estimate the conditional probabilities of
abrupt co-movements among spreads. We correct for randomness and for finite
size effects and we find significant prob- ability of joint drawups for certain
pairs of CDS. We also find significant probability of trend rein- forcement,
i.e. drawups in a given CDS followed by drawups in the same CDS. Finally, we
take the matrix of probability of joint drawups as an estimate of the network
of financial dependencies among institutions. We then carry out a network
analysis that provides insights into the role of systemically important
financial institutions.Comment: 15 pages, 5 figures, Supplementary informatio
Evolution equations for slowly rotating stars
We present a hyperbolic formulation of the evolution equations describing
non-radial perturbations of slowly rotating relativistic stars in the
Regge--Wheeler gauge. We demonstrate the stability preperties of the new
evolution set of equations and compute the polar w-modes for slowly rotating
stars.Comment: 27 pages, 2 figure
Evolution of controllability in interbank networks
The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected "hub" institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies
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