Research articles for the 2020-01-19

Asset Price Bubbles in market models with proportional transaction costs
Francesca Biagini,Thomas Reitsam
arXiv

We study asset price bubbles in market models with proportional transaction costs $\lambda\in (0,1)$ and finite time horizon $T$ in the setting of [49]. By following [28], we define the fundamental value $F$ of a risky asset $S$ as the price of a super-replicating portfolio for a position terminating in one unit of the asset and zero cash. We then obtain a dual representation for the fundamental value by using the super-replication theorem of [50]. We say that an asset price has a bubble if its fundamental value differs from the ask-price $(1+\lambda)S$. We investigate the impact of transaction costs on asset price bubbles and show that our model intrinsically includes the birth of a bubble.



Communicability in the World Trade Network -- A new perspective for community detection
Paolo Bartesaghi,Gian Paolo Clemente,Rosanna Grassi
arXiv

Community detection in a network plays a crucial role in the economic and financial contexts, specifically when applied to the World Trade Network. We provide a new perspective in which clusters of strongly interacting countries are identified by means of a specific distance criterion. We refer to the Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The methodology is based on a varying distance threshold and it is effective from a computational point of view. It also allows an inspection of the intercluster and intracluster properties of the resulting communities. The numerical analyses highlight peculiar relationships between countries and provide a rich set of information that can hardly be achieved within alternative clustering approaches.



Comparing School Choice and College Admission Mechanisms By Their Immunity to Strategic Admissions
Somouaoga Bonkoungou,Alexander S. Nesterov
arXiv

Recently dozens of school districts and college admissions systems around the world have reformed their admission rules. As a main motivation for these reforms the policymakers cited strategic flaws of the rules: students had strong incentives to game the system, which caused dramatic consequences for non-strategic students. However, almost none of the new rules were strategy-proof. We explain this puzzle. We show that after the reforms the rules became more immune to strategic admissions: each student received a smaller set of schools that he can get in using a strategy, weakening incentives to manipulate. Simultaneously, the admission to each school became strategy-proof to a larger set of students, making the schools more available for non-strategic students. We also show that the existing explanation of the puzzle due to Pathak and S\"onmez (2013) is incomplete.



Corporate Governance, Noise Trading and Liquidity of Stocks
Jianhao Su
arXiv

Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mechanisms by which corporate governance improves liquidity of the corporate stocks. In this process we found that the existence of noise traders who are semi-informed in the market is an important condition for corporate governance to have the effect of improving liquidity of the stocks. We further find that the strength of this effect is affected by the degree of noise traders' participation in market transactions. Our model reveals that corporate governance and the degree of noise traders' participation in transactions have a synergistic effect on improving the liquidity of the stocks.



Examining the correlation of the level of wage inequality with labor market institutions
Virginia Tsoukatou
arXiv

Technological change is responsible for major changes in the labor market. One of the offspring of technological change is the SBTC, which is for many economists the leading cause of the increasing wage inequality. However, despite that the technological change affected similarly the majority of the developed countries, nevertheless, the level of the increase of wage inequality wasn't similar. Following the predictions of the SBTC theory, the different levels of inequality could be due to varying degrees of skill inequality between economies, possibly caused by variations in the number of skilled workers available. However, recent research shows that the difference mentioned above can explain a small percentage of the difference between countries. Therefore, most of the resulting inequality could be due to the different ways in which the higher level of skills is valued in each labor market. The position advocated in this article is that technological change is largely given for all countries without much scope to reverse. Therefore, in order to illustrate the changes in the structure of wage distribution that cause wage inequality, we need to understand how technology affects labor market institutions.In this sense, the pay inequality caused by technological progress is not a phenomenon we passively accept. On the contrary, recognizing that the structure and the way labor market institutions function is largely influenced by the way institutions respond to technological change, we can understand and maybe reverse this underlying wage inequality.



Neglecting Uncertainties Leads to Suboptimal Decisions About Home-Owners Flood Risk Management
Mahkameh Zarekarizi,Vivek Srikrishnan,Klaus Keller
arXiv

Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate the house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating a house to the Base Flood Elevation (the elevation of the 100-yr flood) plus a freeboard. This recommendation neglects many uncertainties. Here we use a multi-objective robust decision-making framework to analyze this decision in the face of deep uncertainties. We find strong interactions between the economic, engineering, and Earth science uncertainties, illustrating the need for an integrated analysis. We show that considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility increases the economically optimal house elevation to values well above the recommendation by FEMA. An improved decision-support for home-owners has the potential to drastically improve decisions and outcomes.



Nonparametric Hedging of Volatility Swaps with Variance Swaps in Stochastic Volatility Models
Frido Rolloos
arXiv

In this paper the zero vanna implied volatility approximation for the price of freshly minted volatility swaps is generalised to seasoned volatility swaps. We also derive how volatility swaps can be hedged using only variance swaps without making use of a specific stochastic volatility model. As dynamically trading variance swaps is in general cheaper and operationally less cumbersome compared to dynamically rebalancing a continuous strip of options, our result makes the hedging of volatility swaps both practically feasible and robust. Within the class of stochastic volatility models our pricing and hedging results are model-independent and can be implemented at almost no computational cost.



Recovering Network Structure from Aggregated Relational Data using Penalized Regression
Hossein Alidaee,Eric Auerbach,Michael P. Leung
arXiv

Social network data can be expensive to collect. Breza et al. (2017) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of a latent social network when it is generated by a specific parametric random effects model. Our main observation is that many economic network formation models produce networks that are effectively low-rank. As a consequence, network recovery from ARD is generally possible without parametric assumptions using a nuclear-norm penalized regression. We demonstrate how to implement this method and provide finite-sample bounds on the mean squared error of the resulting estimator for the distribution of network links. Computation takes seconds for samples with hundreds of observations. Easy-to-use code in R and Python can be found at https://github.com/mpleung/ARD.



The sub-fractional CEV model
Axel A. Araneda,Nils Bertschinger
arXiv

The sub-fractional Brownian motion (sfBm) could be considered as the intermediate step between the standard Brownian motion (Bm) and the fractional Brownian motion (fBm). By the way, subfractional diffusion is a candidate to describe stochastic processes with long-range dependence and non-stationarity in their increments. In this note, we use sfBm for financial modeling. In particular, we extend the results provided by Araneda [Axel A. Araneda. The fractional and mixed-fractional CEV model. Journal of Computational and Applied Mathematics, 363:106-123, 2020] arriving at the option pricing under the sub-fractional CEV model.



Trading on the Floor after Sweeping the Book
Vassilis Polimenis
arXiv

Informed traders need to trade fast in order to profit from their private information before it becomes public. Fast electronic markets provide such liquidity. Slow markets provide execution in an auction based trading floor. Hybrid markets combine both execution venues. In its main result, the paper shows that to compensate for their slow and risky executions, trading floors need to be at least twice as deep as the sweeping facility. Furthermore, when a stand-alone trading floor is enhanced with the addition of a sweeping facility, overall informed trading will decline because it is easier for informed traders to extract the full value of their private info.