In this paper, we focus on estimating ultimate claim counts in multiple insurance processes and thus extend the associated literature of micro-level stochastic reserving models to the multivariate context.

Specifically, we develop a multivariate Cox process to model the joint arrival process of insurance claims in multiple Lines of Business. The dependency structure is introduced via multivariate shot noise intensity processes which are connected with the help of L\'evy copulas. Such a construction is more parsimonious and tractable in higher dimensions than plain vanilla common shock models. We also consider practical implementation and explicitly incorporate known covariates, such as seasonality patterns and trends, which may explain some of the relationship between two insurance processes (or at least help tease out those relationships).

We develop a filtering algorithm based on the reversible-jump Markov Chain Monte Carlo (RJMCMC) method to estimate the unobservable stochastic intensities. Model calibration is illustrated using real data from the AUSI data set.

We present a systematic approach to effectively evaluate potential risk cost caused by exposure to solar proton events (SPEs) from solar flares for the airline industry. We also evaluate associated health risks from radiation, to provide relevant alternative ways to minimize economic loss and opportunity. The estimated radiation dose induced by each SPE for the passengers of each flight is calculated using ExoKyoto and PHITS. We determine a few scenarios for the estimated dose limit at 1 and 20mSv, corresponding to the effective dose limit for the general public and occupational exposure, respectively, as well as a higher dose induced an extreme superflare. We set a hypothetical airline shutdown scenario at 1mSv for a single flight per passenger, due to legal restrictions under the potential radiation dose. In such a scenario, we calculate the potential loss in direct and opportunity cost under the cancelation of the flight. At the same time, we considered that, even under such a scenario, if the airplane flies at a slightly lower altitude (from 12 to 9.5km: atmospheric depth from 234 to 365g/cm$^{2}$), the total loss becomes much smaller than flight cancelation, and the estimated total dose goes down from 1.2 to 0.45mSv, which is below the effective dose limit for the general public. In case of flying at an even lower altitude (7km: atmospheric depth 484g/cm$^{2}$), the estimated total dose becomes much smaller, 0.12 mSv. If we assume the increase of fuel cost is proportional to the increase in atmospheric depth, the increase in cost becomes 1.56 and 2.07 for the case of flying at 9.5 km and at 7 km, respectively. Lower altitude flights provide more safety for the potential risk of radiation doses induced by severe SPEs. At the same time, since there is total loss caused by flight cancelation, we propose that considering lower flight altitude is the best protection against solar flares.

This article investigates the effect of prices and socio-demographic variables on the farmers decision to purchase agricultural insurance. A survey has been conducted to 200 farmers most of whom are engaged in diversified income-generating activities. The logistic estimation results suggest that education and household income from farming activities positively affect the likelihood of purchasing insurance. The demand for insurance is negatively correlated with the premium paid per insured value, suggesting that insurance is a normal good. Farmers are willing to pay (WTP) increasingly higher premiums for contracts with a higher coverage ratio. According to the valuation model, the WTP declines sharply for coverage ratios under 70%.

We demonstrate that the martingale condition in the stock market can be interpreted as a vacuum condition when we express the financial equations in the Hamiltonian form. We then show that the symmetry under the changes of the prices is spontaneously broken in general and the symmetry under changes in the volatility, for the case of the Merton-Garman (MG) equation, is also spontaneously broken. This reproduces a vacuum degeneracy for the system. In this way, we find the conditions under which, the martingale condition can be considered to be a non-degenerate vacuum. This gives us a surprising connection between spontaneous symmetry breaking and the flow of information through the boundaries for the financial systems. Subsequently, we find an extended martingale condition for the MG equation, depending not only prices but also on the volatility and finally, we show what happens if we include additional non-derivative terms on the Black Scholes and on the MG equations, breaking then some other symmetries of the system spontaneously.

We study the optimal order placement strategy with the presence of a liquidity cost. In this problem, a stock trader wishes to clear her large inventory by a predetermined time horizon $T$. A trader uses both limit and market orders, and a large market order faces an adverse price movement caused by the liquidity risk. First, we study a single period model where the trader places a limit order and/or a market order at the beginning. We show the behavior of optimal amount of market order, $m^*$, and optimal placement of limit order, $y^*$, under different market conditions. Next, we extend it to a multi-period model, where the trader makes sequential decisions of limit and market orders at multiple time points.

It is well-known that disciplines such as mechanical engineering, electrical engineering, civil engineering, aerospace engineering, chemical engineering and software engineering witnessed successful applications of reliability engineering concepts. However, the concept of reliability in its strict sense is missing in financial services. Therefore, in order to fill this gap, in a first-of-its-kind-study, we define the reliability of a bank/firm in terms of the financial ratios connoting the financial health of the bank to withstand the likelihood of insolvency or bankruptcy. For the purpose of estimating the reliability of a bank, we invoke a statistical and machine learning algorithm namely, logistic regression (LR). Once, the parameters are estimated in the 1st stage, we fix them and treat the financial ratios as decision variables. Thus, in the 1st stage, we accomplish the hitherto unknown way of estimating the reliability of a bank. Subsequently, in the 2nd stage, in order to maximize the reliability of the bank, we formulate an unconstrained optimization problem in a single-objective environment and solve it using the well-known particle swarm optimization (PSO) algorithm. Thus, in essence, these two stages correspond to predictive and prescriptive analytics respectively. The proposed 2-stage strategy of using them in tandem is beneficial to the decision-makers within a bank who can try to achieve the optimal or near-optimal values of the financial ratios in order to maximize the reliability which is tantamount to safeguarding their bank against solvency or bankruptcy.

In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses (what if the disaster did not occur?), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes.

In recent decades, we have known some interesting applications of Lie theory in the theory of technological progress. Firstly, we will discuss some results of R. Saito in \cite{rS1980} and \cite{rS1981} about the application modeling of Lie groups in the theory of technical progress. Next, we will describe the result on Romanian economy of G. Zaman and Z. Goschin in \cite{ZG2010}. Finally, by using Sato's results and applying the method of G. Zaman and Z. Goschin, we give an estimation of the GDP function of Viet Nam for the 1995-2018 period and give several important observations about the impact of technical progress on economic growth of Viet Nam.

In this article, we provide a comparative analysis of the industry, communication, and research infrastructure among the GCC member states as measured by the United Nations sustainable development goal 9. SDG 9 provides a clear framework for measuring the performance of nations in achieving sustainable industrialization. Three pillars of this goal are defined as quality logistics and efficient transportation, availability of mobile-cellular network with high-speed internet access, and quality research output. Based on the data from both the United Nations' SDG database and the Bertelsmann Stiftung SDG-index, our results suggest that while most of the sub-goals in SDG 9 are achieved, significant challenges remain ahead. Notably, the research output of the GCC member states is not in par with that of the developed world. We suggest the GCC decisionmakers initiate national and supranational research schemes in order to boost research and development in the region.

We relook at the classic equity fund selection and portfolio construction problems from a new perspective and propose an easy-to-implement framework to tackle the problem in practical investment. Rather than the conventional way by constructing a long only portfolio from a big universe of stocks or macro factors, we show how to produce a long-short portfolio from a smaller pool of stocks from mutual fund top holdings and generate impressive results. As these methods are based on statistical evidence, we need closely monitoring the model validity, and prepare repair strategies.

In this paper, we study the characteristics of the member firms on the Korea Exchange. The member firms intermediate between the market participants and the exchange, and all the participants should trade stocks through members. To identify the characteristics of member firms, all member firms are categorized into three groups, such as the domestic members similar to individuals (DIMs), the domestic members similar to institutions (DSMs), and the foreign members (FRMs), in terms of the type of investor. We examine the dynamics of the member firms. The trading characteristics of members are revealed through the directionality and trend. While FRMs tend to trade one-way and move with the price change, DIMs are the opposite. In the market, DIMs and DSMs do herd and the herding moves in the opposite direction of the price change. One the other hand, FRMs do herd in the direction of the price change. The network analysis supports that the members are clustered into three groups similar to DIMs, DSMs, and FRMs. Finally, random matrix theory and a cross-sectional regression show that the inventory variation of members possesses significant information about stock prices and that member herding helps to price the stocks.

The issue of climate change has become increasingly noteworthy in the past years, the transition towards a renewable energy system is a priority in the transition to a sustainable society. In this document, we explore the definition of green energy transition, how it is reached, and what are the driven factors to achieve it. To answer that firstly, we have conducted a literature review discovering definitions from different disciplines, secondly, gathering the key factors that are drivers for energy transition, finally, an analysis of the factors is conducted within the context of European Union data. Preliminary results have shown that household net income and governmental legal actions related to environmental issues are potential candidates to predict energy transition within countries. With this research, we intend to spark new research directions in order to get a common social and scientific understanding of green energy transition.