The cyber risk insurance market is at a nascent stage of its development, even as the magnitude of cyber losses is significant and the rate of cyber risk events is increasing. Existing cyber risk insurance products as well as academic studies have been focusing on classifying cyber risk events and developing models of these events, but little attention has been paid to proposing insurance risk transfer strategies that incentivize mitigation of cyber loss through adjusting the premium of the risk transfer product. To address this important gap, we develop a Bonus-Malus model for cyber risk insurance. Specifically, we propose a mathematical model of cyber risk insurance and cybersecurity provisioning supported with an efficient numerical algorithm based on dynamic programming. Through a numerical experiment, we demonstrate how a properly designed cyber risk insurance contract with a Bonus-Malus system can resolve the issue of moral hazard and benefit the insurer.

Remittances provide an essential connection between people working abroad and their home countries. This paper considers these transfers as a measure of preferences revealed by the workers, underlying a ranking of countries around the world. In particular, we use the World Bank bilateral remittances data of international salaries and interpersonal transfers between 2010 and 2015 to compare European countries. The suggested least squares method has favourable axiomatic properties. Our ranking reveals a crucial aspect of quality of life and may become an alternative to various composite indices.

Uniswap -- and other constant product markets -- appear to work well in practice despite their simplicity. In this paper, we give a simple formal analysis of constant product markets and their generalizations, showing that, under some common conditions, these markets must closely track the reference market price. We also show that Uniswap satisfies many other desirable properties and numerically demonstrate, via a large-scale agent-based simulation, that Uniswap is stable under a wide range of market conditions.

The Hartman-Watson distribution with density $f_r(t)$ is a probability distribution defined on $t \geq 0$ which appears in several problems of applied probability. The density of this distribution is expressed in terms of an integral $\theta(r,t)$ which is difficult to evaluate numerically for small $t\to 0$. Using saddle point methods, we obtain the first two terms of the $t\to 0$ expansion of $\theta(\rho/t,t)$ at fixed $\rho >0$. An error bound is obtained by numerical estimates of the integrand, which is furthermore uniform in $\rho$. As an application we obtain the leading asymptotics of the density of the time average of the geometric Brownian motion as $t\to 0$. This has the form $\mathbb{P}(\frac{1}{t} \int_0^t e^{2(B_s+\mu s)} ds \in da) = (2\pi t)^{-1/2} g(a,\mu) e^{-\frac{1}{t} J(a)} (1 + O(t))$, with an exponent $J(a)$ which reproduces the known result obtained previously using Large Deviations theory.

We propose a novel approach to the statistical analysis of simulation models and, especially, agent-based models (ABMs). Our main goal is to provide a fully automated and model-independent tool-kit to inspect simulations and perform counterfactual analysis. Our approach: (i) is easy-to-use by the modeller, (ii) improves reproducibility of results, (iii) optimizes running time given the modeller's machine, (iv) automatically chooses the number of required simulations and simulation steps to reach user-specified statistical confidence, and (v) automatically performs a variety of statistical tests. In particular, our framework is designed to distinguish the transient dynamics of the model from its steady-state behaviour (if any), estimate properties of the model in both "phases", and provide indications on the ergodic (or non-ergodic) nature of the simulated processes -- which, in turns allows one to gauge the reliability of a steady-state analysis. Estimates are equipped with statistical guarantees, allowing for robust comparisons across computational experiments. To demonstrate the effectiveness of our approach, we apply it to two models from the literature: a large scale macro-financial ABM and a small scale prediction market model. Compared to prior analyses of these models, we obtain new insights and we are able to identify and fix some erroneous conclusions.

The present study aims to establish the model of the cryptocurrency price trend based on financial theory using the LSTM model with multiple combinations between the window length and the predicting horizons, the random walk model is also applied with different parameter settings.

This study is about public-private research collaboration. In particular, we want to measure how the propensity of academics to collaborate with their colleagues from private firms varies over time and whether the typical profile of such academics change. Furthermore, we investigate the change of the weights of main drivers underlying the academics' propensity to collaborate with industry. In order to achieve such goals, we apply an inferential model on a dataset of professors working in Italian universities in two subsequent periods, 2010-2013 and 2014-2017. Results can be useful for supporting the definition of policies aimed at fostering public-private research collaborations, and should be taken into account when assessing their effectiveness afterwards.

COVID-19 has impacted the economy of almost every country in the world. Of particular interest are the responses of the economic indicators of developing nations (such as BRICS) to the COVID-19 shock. As an extension to our earlier work on the dynamic associations of pandemic growth, exchange rate, and stock market indices in the context of India, we look at the same question with respect to the BRICS nations. We use structural variable autoregression (SVAR) to identify the dynamic underlying associations across the normalized growth measurements of the COVID-19 cumulative case, recovery, and death counts, and those of the exchange rate, and stock market indices, using data over 203 days (March 12 - September 30, 2020). Using impulse response analyses, the COVID-19 shock to the growth of exchange rate was seen to persist for around 10+ days, and that for stock exchange was seen to be around 15 days. The models capture the contemporaneous nature of these shocks and the subsequent responses, potentially guiding to inform policy decisions at a national level. Further, causal inference-based analyses would allow us to infer relationships that are stronger than mere associations.

With today's technological advancements, mobile phones and wearable devices have become extensions of an increasingly diffused and smart digital infrastructure. In this paper, we examine mobile health (mHealth) platforms and their health and economic impacts on the outcomes of chronic disease patients. To do so, we partnered with a major mHealth firm that provides one of the largest mobile health app platforms in Asia specializing in diabetes care. We designed and implemented a randomized field experiment based on detailed patient health activities (e.g., steps, exercises, sleep, food intake) and blood glucose values from 1,070 diabetes patients over several months. Our main findings show that the adoption of the mHealth app leads to an improvement in both short term metrics (such as reduction in patients' blood glucose and glycated hemoglobin levels) and longer-term metrics (such as hospital visits, and medical expenses). Patients who adopted the mHealth app undertook higher levels of exercise, consumed healthier food with lower calories, walked more steps and slept for longer times on a daily basis. A comparison of mobile vs. PC enabled version of the same app demonstrates that the mobile has a stronger effect than PCs in helping patients make behavioral modifications with respect to diet, exercise and life style, which ultimately leads to an improvement in their healthcare outcomes. We also compared outcomes when the platform facilitates personalized health reminders to patients vs. generic reminders. We found that personalized mobile message with patient-specific guidance can have an inadvertent effect on patient app engagement, life style changes, and health improvement. Overall, our findings indicate the potential value of mHealth technologies, as well as the importance of mHealth platform design in achieving better healthcare outcomes.

The fast-growing Emerging Market (EM) economies and their improved transparency and liquidity have attracted international investors. However, the external price shocks can result in a higher level of volatility as well as domestic policy instability. Therefore, an efficient risk measure and hedging strategies are needed to help investors protect their investments against this risk. In this paper, a daily systemic risk measure, called FRM (Financial Risk Meter) is proposed. The FRM-EM is applied to capture systemic risk behavior embedded in the returns of the 25 largest EMs FIs, covering the BRIMST (Brazil, Russia, India, Mexico, South Africa, and Turkey), and thereby reflects the financial linkages between these economies. Concerning the Macro factors, in addition to the Adrian and Brunnermeier (2016) Macro, we include the EM sovereign yield spread over respective US Treasuries and the above-mentioned countries currencies. The results indicated that the FRM of EMs FIs reached its maximum during the US financial crisis following by COVID 19 crisis and the Macro factors explain the BRIMST FIs with various degrees of sensibility. We then study the relationship between those factors and the tail event network behavior to build our policy recommendations to help the investors to choose the suitable market for in-vestment and tail-event optimized portfolios. For that purpose, an overlapping region between portfolio optimization strategies and FRM network centrality is developed. We propose a robust and well-diversified tail-event and cluster risk-sensitive portfolio allocation model and compare it to more classical approaches

The massive shock of the COVID-19 pandemic is already showing its negative effects on economies around the world, unprecedented in recent history. COVID-19 infections and containment measures have caused a general slowdown in research and new knowledge production. Because of the link between R&D spending and economic growth, it is to be expected then that a slowdown in research activities will slow in turn the global recovery from the pandemic. Many recent studies also claim an uneven impact on scientific production across gender. In this paper, we investigate the phenomenon across countries, analysing preprint depositions. Differently from other works, that compare the number of preprint depositions before and after the pandemic outbreak, we analyse the depositions trends across geographical areas, and contrast after-pandemic depositions with expected ones. Differently from common belief and initial evidence, in few countries female scientists increased their scientific output while males plunged.

The Group Quantization formalism is a scheme for constructing a functional space that is an irreducible infinite dimensional representation of the Lie algebra belonging to a dynamical symmetry group. We apply this formalism to the construction of functional space and operators for quadratic potentials -- gaussian pricing kernels in finance. We describe the Black-Scholes theory, the Ho-Lee interest rate model and the Euclidean repulsive and attractive oscillators. The symmetry group used in this work has the structure of a principal bundle with base (dynamical) group a semi-direct extension of the Heisenberg-Weyl group by SL(2,R), and structure group (fiber) the positive real line.

My study would follow two phases of analysis i.e. the first phase/preliminary view would be developed through the widest range of available and applicable methodologies followed by a second phase/in-depth assessment and discussion of the identified challenges, opportunities, and policy options.

Risk capital allocations (RCAs) are an important tool in quantitative risk management, where they are utilized to, e.g., gauge the profitability of distinct business units, determine the price of a new product, and conduct the marginal economic capital analysis. Nevertheless, the notion of RCA has been living in the shadow of another, closely related notion, of risk measure (RM) in the sense that the latter notion often shapes the fashion in which the former notion is implemented. In fact, as the majority of the RCAs known nowadays are induced by RMs, the popularity of the two are apparently very much correlated. As a result, it is the RCA that is induced by the Conditional Tail Expectation (CTE) RM that has arguably prevailed in scholarly literature and applications. Admittedly, the CTE RM is a sound mathematical object and an important regulatory RM, but its appropriateness is controversial in, e.g., profitability analysis and pricing. In this paper, we address the question as to whether or not the RCA induced by the CTE RM may concur with alternatives that arise from the context of profit maximization. More specifically, we provide exhaustive description of all those probabilistic model settings, in which the mathematical and regulatory CTE RM may also reflect the risk perception of a profit-maximizing insurer.

Motivated reasoning posits that people distort how they process new information in the direction of beliefs they find more attractive. This paper creates a novel experimental design to identify motivated reasoning from Bayesian updating when people enter into the experiment with endogenously different beliefs. It analyzes how subjects assess the veracity of information sources that tell them the median of their belief distribution is too high or too low. A Bayesian would infer nothing about the source veracity from this message, but a motivated reasoner would believe the source were more truthful when it reports the direction that he is more motivated to believe. Experimental results show novel evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, gun laws, and the performance of other subjects. Motivated reasoning from messages on these topics leads people's beliefs to become more polarized and less accurate, even though the messages are uninformative.

What role does culture play in determining institutions in a country? This paper argues that the establishment of institutions is a process originating predominantly in a nation's culture and tries to discern the role of a cultural background in the governance of countries. We use the six Hofstede's Cultural Dimensions and the six Worldwide Governance Indicators to test the strength of the relationship on 94 countries between 1996 and 2019. We find that the strongest cultural characteristics are Power Distance with negative effect on governance and Long-Term Orientation with positive effect. We also determine how well countries transform their cultural characteristics into institutions using stochastic frontier analysis.

We investigate the changes in the self-citation behavior of Italian professors following the introduction of a citation-based incentive scheme, for national accreditation to academic appointments. Previous contributions on self-citation behavior have either focused on small samples or relied on simple models, not controlling for all confounding factors. The present work adopts a complex statistics model implemented on bibliometric individual data for over 15,000 Italian professors. Controlling for a number of covariates (number of citable papers published by the author; presence of international authors; number of co-authors; degree of the professor's specialization), the average increase in self-citation rates following introduction of the ASN is of 9.5%. The increase is common to all disciplines and academic ranks, albeit with diverse magnitude. Moreover, the increase is sensitive to the relative incentive, depending on the status of the scholar with respect to the scientific accreditation. A further analysis shows that there is much heterogeneity in the individual patterns of self-citing behavior, albeit with very few outliers.

Housing markets are inherently spatial, yet many existing models fail to capture this spatial dimension. Here we introduce a new graph-based approach for incorporating a spatial component in a large-scale urban housing agent-based model (ABM). The model explicitly captures several social and economic factors that influence the agents' decision-making behaviour (such as fear of missing out, their trend following aptitude, and the strength of their submarket outreach), and interprets these factors in spatial terms. The proposed model is calibrated and validated with the housing market data for the Greater Sydney region. The ABM simulation results not only include predictions for the overall market, but also produce area-specific forecasting at the level of local government areas within Sydney as arising from individual buy and sell decisions. In addition, the simulation results elucidate agent preferences in submarkets, highlighting differences in agent behaviour, for example, between first-time home buyers and investors, and between both local and overseas investors.

Electronic platform has been increasingly popular for the execution of large orders among asset managers dealing desks. Properly monitoring each individual trade by the appropriate Transaction Cost Analysis (TCA) is the first key step towards this electronic automation. One of the challenges in TCA is to build a benchmark for the expected transaction cost and to characterize the price impact of each individual trade, with given bond characteristics and market conditions.

Taking the viewpoint of an investor, we provide an analytical methodology to conduct TCA in corporate bond trading. With limited liquidity of corporate bonds and patchy information available on existing trades, we manage to build a statistical model as a benchmark for effective cost and a non-parametric model for the price impact kernel. Our TCA analysis is conducted based on the TRACE Enhanced dataset and consists of four steps in two different time scales. The first step is to identify the initiator of a transaction and the riskless-principle-trades (RPTs). With the estimated initiator of each trade, the second step is to estimate the bid-ask spread and the mid-price movements. The third step is to estimate the expected average cost on a weekly basis via regularized regression analysis. The final step is to investigate each trade for the amplitude of its price impact and the price decay after the transaction for liquid corporate bonds. Here we apply a transient impact model (TIM) to estimate the price impact kernel via a non-parametric method.

Our benchmark model allows for identifying and improving best practices and for enhancing objective and quantitative counter-party selections. A key discovery of our study is the need to account for a price impact asymmetry between customer-buy orders and consumer-sell orders.

This work examines the economic benefits of learning a new skill from a different domain: cross-skilling. To assess this, a network of skills from the job profiles of 14,790 online freelancers is constructed. Based on this skill network, relationships between 3,480 different skills are revealed and marginal effects of learning a new skill can be calculated via workers' wages. The results indicate that learning in-demand skills, such as popular programming languages, is beneficial in general, and that diverse skill sets tend to be profitable, too. However, the economic benefit of a new skill is individual, as it complements the existing skill bundle of each worker. As technological and social transformation is reshuffling jobs' task profiles at a fast pace, the findings of this study help to clarify skill sets required for designing individual re-skilling pathways. This can help to increase employability and reduce labour market shortages.