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on Network Economics |
| By: | Saleem Bahaj (University College London); Marie Fuchs (London School of Economics (LSE)); Ricardo Reis (London School of Economics (LSE)) |
| Abstract: | At the end of 2023, there were 175 cross-border connections between central banks in a global network of liquidity lines that gave access to foreign currency for countries accounting for 79% of world GDP. This paper presents a comprehensive dataset of this network and its characteristics between 2000 and 2023. While the Federal Reserve drove growth in 2007-09, the network expanded as much between 2010 and 2015 through bilateral arrangements involving the ECB and the People’s Bank of China. The network structure means that banks without direct access to a source central bank can still have indirect access to its currency. The central intermediaries in the network for all major currencies are the PBoC and the ECB. We find support using cross-country data that the lines reduce CIP deviations at the tails. Liquidity lines are often signed to substitute for a bleeding of FX reserves, but once in place they complement reserves. |
| Keywords: | swap lines, capital flows, financial crises, IMF, cross-currency basis |
| JEL: | E44 F33 G15 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:cfm:wpaper:2423 |
| By: | Dimitar Kitanovski; Igor Mishkovski; Viktor Stojkoski; Miroslav Mirchev |
| Abstract: | Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets' co-occurrence networks, where communities are detected for each month throughout a whole year and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a maximal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S\&P 500 and the Top 203 cryptocurrencies with a market cap above 2M USD in the period from Jan 2019 to Sep 2022. Moreover, we study into more details the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11739 |
| By: | Krantz, Sebastian |
| Abstract: | This paper characterizes economically optimal investments into Africa's road network in partial and general equilibrium - based on a detailed topography of the network, road construction costs, frictions in cross-border trading, and economic geography. Drawing from data on 144 million trans-continental routes, it first assesses local and global network efficiency and market access. It then derives a large network connecting 447 cities and 52 ports along the fastest routes, devises an algorithm to propose new links, analyzes the quality of existing links, and estimates link-level construction/upgrading costs. Subsequently, it computes market-access-maximizing investments in partial equilibrium and conducts cost-benefit analysis for individual links and several investment packages. Using a spatial economic model and global optimization over the space of networks, it finally elicits welfare-maximizing investments in spatial equilibrium. Findings imply that cross-border frictions and trade elasticities significantly shape optimal road investments. Reducing frictions yields the greatest benefits, followed by road upgrades and new construction. Sequencing matters, as reduced frictions generally increase investment returns. Returns to upgrading key links are large, even under frictions. |
| Keywords: | African roads, spatially optimal investments, big data, PE and GE analysis |
| JEL: | O18 R42 R10 O10 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkwp:302186 |
| By: | Theros, Marika |
| Abstract: | Corruption remains a persistent feature in most transitional and fragile countries, raising questions around the processes and outcomes of international development and economic reforms. In the case of Afghanistan, conventional wisdom tends to blame domestic factors, including corruption, in the collapse of the internationally-backed Islamic Republic of Afghanistan, while largely neglecting the co-constitutive nexus between economic reconstruction, criminality, and political authority. Combining the political marketplace framework with a network analysis, this paper traces how a corrupt network formed around the Kabul Bank, grew and metastasised by leveraging neo-liberal and technocratic economic reform policies, and thus, gravely undermined the country’s governance and stability. By doing so, it argues that international reconstruction practices and resources reconfigured power in Afghanistan, and helped create a governance system governed by the logic of a criminalised political marketplace. The paper also demonstrates the utility of a political marketplace lens in explaining evolving political dynamics, with a network analysis to generate deeper insights into the complex interactions between the local and global dynamics that produce criminality, corruption, and state capture. |
| Keywords: | Afghanistan; corruption; criminal networks; political economy; post-conflict reconstruction; Taylor & Francis deal |
| JEL: | N0 |
| Date: | 2024–08–12 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:124263 |
| By: | Cameron Cornell; Lewis Mitchell; Matthew Roughan |
| Abstract: | Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, don't fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme - allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer - a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.12210 |
| By: | Jianqing Fan (Princeton University); Weining Wang (University of Groningen); Yue Zhao (University of York) |
| Abstract: | High-dimensional covariates often admit linear factor structure. To effectively screen correlated covariates in high-dimension, we propose a conditional variable screening test based on non-parametric regression using neural networks due to their representation power. We ask the question whether individual covariates have additional contributions given the latent factors or more generally a set of variables. Our test statistics are based on the estimated partial derivative of the regression function of the candidate variable for screening and a observable proxy for the latent factors. Hence, our test reveals how much predictors contribute additionally to the non-parametric regression after accounting for the latent factors. Our derivative estimator is the convolution of a deep neural network regression estimator and a smoothing kernel. We demonstrate that when the neural network size diverges with the sample size, unlike estimating the regression function itself, it is necessary to smooth the partial derivative of the neural network estimator to recover the desired convergence rate for the derivative. Moreover, our screening test achieves asymptotic normality under the null after finely centering our test statistics that makes the biases negligible, as well as consistency for local alternatives under mild conditions. We demonstrate the performance of our test in a simulation study and two real world applications. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.10825 |
| By: | Sina Montazeri; Haseebullah Jumakhan; Sonia Abrasiabian; Amir Mirzaeinia |
| Abstract: | Building on our prior explorations of convolutional neural networks (CNNs) for financial data processing, this paper introduces two significant enhancements to refine our CNN model's predictive performance and robustness for financial tabular data. Firstly, we integrate a normalization layer at the input stage to ensure consistent feature scaling, addressing the issue of disparate feature magnitudes that can skew the learning process. This modification is hypothesized to aid in stabilizing the training dynamics and improving the model's generalization across diverse financial datasets. Secondly, we employ a Gradient Reduction Architecture, where earlier layers are wider and subsequent layers are progressively narrower. This enhancement is designed to enable the model to capture more complex and subtle patterns within the data, a crucial factor in accurately predicting financial outcomes. These advancements directly respond to the limitations identified in previous studies, where simpler models struggled with the complexity and variability inherent in financial applications. Initial tests confirm that these changes improve accuracy and model stability, suggesting that deeper and more nuanced network architectures can significantly benefit financial predictive tasks. This paper details the implementation of these enhancements and evaluates their impact on the model's performance in a controlled experimental setting. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11859 |
| By: | Sofoklis Goulas (Economic Studies, Brookings Institution); Bhagya N. Gunawardena (School of Economics, Finance, & Marketing, RMIT); Rigissa Megalokonomou (Department of Economics, Monash University); Yves Zenou (Department of Economics, Monash University) |
| Abstract: | Using Greek administrative data, we examine the impact of being randomly assigned to a classroom with a same-gender top-performing student on both short- and long-term educational outcomes. These top performers are tasked with keeping classroom attendance records, which positions them as role models. Both male and female students are influenced by the performance of a same-gender top performer and experience both spillover and conformist effects. However, only female students show significant positive effects from the presence of a same-gender role model. Specifically, female students improved their science test scores by 4 percent of a standard deviation, were 2.5 percentage points more likely to choose a STEM track, and were more likely to apply for and enroll in a STEM university degree 3 years later. These effects were most pronounced in lower-income neighborhoods. Our findings suggest that same-gender peer role models could reduce the underrepresentation of qualified females in STEM fields by approximately 3 percent. We further validate our findings through a lab-in-the-field experiment, in which students rated the perceived influence of randomized hypothetical top-performer profiles. The results suggest that the influence of same-gender top performers is primarily driven by exposure-related factors (increased perception of distinction feasibility and self-confidence) rather than direct interactions. |
| Keywords: | gender gap, lab-in-the-field experiment, natural experiment, random peer group formation, role models |
| JEL: | J24 J16 I24 I26 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:mos:moswps:2024-15 |
| By: | Kiet Duong (University of York.); Toan Huynh (Queen Mary University of London); Anh Phan (University of Liverpool); Nam Vu (Miami University) |
| Abstract: | We propose a novel explanation for why sanctions on Russian firms might not work as intended: these firms' ability to diversify sanction risks via partner countries friendly with Russia. Using indirect links with partner firms as a plausibly exogenous proxy for this risk-sharing channel, we show that exposed Russian firms were able to leverage these links to alleviate the negative impacts of sanctions in 2014. |
| Keywords: | International risk-sharing, sanction, Russia, firm-level |
| JEL: | F31 F41 F42 F51 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cgs:wpaper:119 |