Research articles for the 2020-08-30

Complexity science approach to economic crime
János Kertész,Johannes Wachs
arXiv

In this comment we discuss how complexity science and network science are particularly useful for identifying and describing the hidden traces of economic misbehaviour such as fraud and corruption.



Creation of knowledge through exchanges of knowledge: Evidence from Japanese patent data
Tomoya Mori,Shosei Sakaguchi
arXiv

This study shows evidence for collaborative knowledge creation among individual researchers through direct exchanges of their mutual differentiated knowledge. Using patent application data from Japan, the collaborative output is evaluated according to the quality and novelty of the developed patents, which are measured in terms of forward citations and the order of application within their primary technological category, respectively. Knowledge exchange is shown to raise collaborative productivity more through the extensive margin (i.e., the number of patents developed) in the quality dimension, whereas it does so more through the intensive margin in the novelty dimension (i.e., novelty of each patent).



Elicitation Complexity of Statistical Properties
Rafael Frongillo,Ian A. Kash
arXiv

A property, or statistical functional, is said to be elicitable if it minimizes expected loss for some loss function. The study of which properties are elicitable sheds light on the capabilities and limitations of point estimation and empirical risk minimization. While recent work asks which properties are elicitable, we instead advocate for a more nuanced question: how many dimensions are required to indirectly elicit a given property? This number is called the elicitation complexity of the property. We lay the foundation for a general theory of elicitation complexity, including several basic results about how elicitation complexity behaves, and the complexity of standard properties of interest. Building on this foundation, our main result gives tight complexity bounds for the broad class of Bayes risks. We apply these results to several properties of interest, including variance, entropy, norms, and several classes of financial risk measures. We conclude with discussion and open directions.



Filtration shrinkage, the structure of deflators, and failure of market completeness
Constantinos Kardaras,Johannes Ruf
arXiv

We analyse the structure of local martingale deflators projected on smaller filtrations. In a general continuous-path setting, we show that the local martingale part in the multiplicative Doob-Meyer decomposition of projected local martingale deflators are themselves local martingale deflators in the smaller information market. Via use of a Bayesian filtering approach, we demonstrate the exact mechanism of how updates on the possible class of models under less information result in the strict supermartingale property of projections of such deflators. Finally, we demonstrate that these projections are unable to span all possible local martingale deflators in the smaller information market, by investigating a situation where market completeness is not retained under filtration shrinkage.



Forecast Encompassing Tests for the Expected Shortfall
Timo Dimitriadis,Julie Schnaitmann
arXiv

We introduce new forecast encompassing tests for the risk measure Expected Shortfall (ES). The ES currently receives much attention through its introduction into the Basel III Accords, which stipulate its use as the primary market risk measure for the international banking regulation. We utilize joint loss functions for the pair ES and Value at Risk to set up three ES encompassing test variants. The tests are built on misspecification robust asymptotic theory and we investigate the finite sample properties of the tests in an extensive simulation study. We use the encompassing tests to illustrate the potential of forecast combination methods for different financial assets.



Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020
Nik Dawson,Sacha Molitorisz,Marian-Andrei Rizoiu,Peter Fray
arXiv

In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3,698 journalist job ads from a corpus of over 6.7 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We can also tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that the skills sought by journalism job ads increasingly include social media and generalist communications.



Pricing and Capital Allocation for Multiline Insurance Firms With Finite Assets in an Imperfect Market
John A. Major,Stephen J. Mildenhall
arXiv

We analyze multiline pricing and capital allocation in equilibrium no-arbitrage markets. Existing theories often assume a perfect complete market, but when pricing is linear, there is no diversification benefit from risk pooling and therefore no role for insurance companies. Instead of a perfect market, we assume a non-additive distortion pricing functional and the principle of equal priority of payments in default. Under these assumptions, we derive a canonical allocation of premium and margin, with properties that merit the name the natural allocation. The natural allocation gives non-negative margins to all independent lines for default-free insurance but can exhibit negative margins for low-risk lines under limited liability. We introduce novel conditional expectation measures of relative risk within a portfolio and use them to derive simple, intuitively appealing expressions for risk margins and capital allocations. We give a unique capital allocation consistent with our law invariant pricing functional. Such allocations produce returns that vary by line, in contrast to many other approaches. Our model provides a bridge between the theoretical perspective that there should be no compensation for bearing diversifiable risk and the empirical observation that more risky lines fetch higher margins relative to subjective expected values.



Reduction of Qubits in Quantum Algorithm for Monte Carlo Simulation by Pseudo-random Number Generator
Koichi Miyamoto,Kenji Shiohara
arXiv

It is known that quantum computers can speed up Monte Carlo simulation compared to classical counterparts. There are already some proposals of application of the quantum algorithm to practical problems, including quantitative finance. In many problems in finance to which Monte Carlo simulation is applied, many random numbers are required to obtain one sample value of the integrand, since those problems are extremely high-dimensional integrations, for example, risk measurement of credit portfolio. This leads to the situation that the required qubit number is too large in the naive implementation where a quantum register is allocated per random number. In this paper, we point out that we can reduce qubits keeping quantum speed up if we perform calculation similar to classical one, that is, estimate the average of integrand values sampled by a pseudo-random number generator (PRNG) implemented on a quantum circuit. We present not only the overview of the idea but also concrete implementation of PRNG and application to credit risk measurement. Actually, reduction of qubits is a trade-off against increase of circuit depth. Therefore full reduction might be impractical, but such a trade-off between speed and memory space will be important in adjustment of calculation setting considering machine specs, if large-scale Monte Carlo simulation by quantum computer is in operation in the future.



SHIFT: A Highly Realistic Financial Market Simulation Platform
Thiago W. Alves,Ionut Florescu,George Calhoun,Dragos Bozdog
arXiv

This paper presents a new financial market simulator that may be used as a tool in both industry and academia for research in market microstructure. It allows multiple automated traders and/or researchers to simultaneously connect to an exchange-like environment, where they are able to asynchronously trade several financial assets at the same time. In its current iteration, this order-driven market implements the basic rules of U.S. equity markets, supporting both market and limit orders, and executing them in a first-in-first-out fashion. We overview the system architecture and we present possible use cases. We demonstrate how a set of automated agents is capable of producing a price process with characteristics similar to the statistics of real price from financial markets. Finally, we detail a market stress scenario and we draw, what we believe to be, interesting conclusions about crash events.



The Long Shadow beyond Lockdown: Board Chairs’ Professional Epidemic Experience and Corporate Investment
He, Shan,Li, Jianjun,Ni, Xiaoran,Peng, Yuchao
SSRN
Based on the evidence after the outbreak of SARS in 2003, which is caused by the same family of viruses as COVID-19, we show that due to the “probability weighting” phenomenon, i.e., decision makers tend to overweight the probability of extreme tail events, the epidemic experience induces entrepreneurs to undervalue profitable investment projects. In particular, we show that firms with board chairs experienced the outbreak of SARS during their tenure of high executives invest less in subsequent periods. Among those firms, this effect matter more for board chairs that actually experienced operating distress during the outbreak of SARS. In addition, those firms have lower investment-Q sensitivities and worse performances, implying that the reduction in corporate investment is inefficient. Our paper reveals a specific channel through which epidemic disease can distort the real economy, which has useful implications for assessing the long-run economic impacts of COVID-19.