We consider the discretized Bachelier model where hedging is done on an equidistant set of times. Exponential utility indifference prices are studied for path-dependent European options and we compute their non-trivial scaling limit for a large number of trading times $n$ and when risk aversion is scaled like $n\ell$ for some constant $\ell>0$. Our analysis is purely probabilistic. We first use a duality argument to transform the problem into an optimal drift control problem with a penalty term. We further use martingale techniques and strong invariance principles and get that the limiting problem takes the form of a volatility control problem.

Relying on the classical connection between Backward Stochastic Differential Equations (BSDEs) and non-linear parabolic partial differential equations (PDEs), we propose a new probabilistic learning scheme for solving high-dimensional semi-linear parabolic PDEs. This scheme is inspired by the approach coming from machine learning and developed using deep neural networks in Han and al. [32]. Our algorithm is based on a Picard iteration scheme in which a sequence of linear-quadratic optimisation problem is solved by means of stochastic gradient descent (SGD) algorithm. In the framework of a linear specification of the approximation space, we manage to prove a convergence result for our scheme, under some smallness condition. In practice, in order to be able to treat high-dimensional examples, we employ sparse grid approximation spaces. In the case of periodic coefficients and using pre-wavelet basis functions, we obtain an upper bound on the global complexity of our method. It shows in particular that the curse of dimensionality is tamed in the sense that in order to achieve a root mean squared error of order ${\epsilon}$, for a prescribed precision ${\epsilon}$, the complexity of the Picard algorithm grows polynomially in ${\epsilon}^{-1}$ up to some logarithmic factor $ |log({\epsilon})| $ which grows linearly with respect to the PDE dimension. Various numerical results are presented to validate the performance of our method and to compare them with some recent machine learning schemes proposed in Han and al. [20] and Hur\'e and al. [37].

Novel blockchain technology provides the infrastructure layer for the creation of decentralized appli-cations. A rapidly growing ecosystem of applications is built around financial services, commonly referred to as decentralized finance. Whereas the intangible concept of decentralization is presented as a key driver for the applications, defining and measuring decentralization is multifaceted. This pa-per provides a framework to quantify decentralization of governance power among blockchain appli-cations. Governance of the applications is increasingly important and requires striking a balance be-tween broad distribution, fostering user activity, and financial incentives. Therefore, we aggregate, parse, and analyze empirical data of four finance applications calculating coefficients for the statistical dispersion of the governance token distribution. The gauges potentially support IS scholars for an objective evaluation of the capabilities and limitations of token governance and for fast iteration in design-driven governance mechanisms.

We focus on how individual behavior that complies with social norms interferes with performance-based incentive mechanisms in organizations with multiple distributed decision-making agents. We model social norms to emerge from interactions between agents: agents observe other the agents' actions and, from these observations, induce what kind of behavior is socially acceptable. By complying with the induced socially accepted behavior, agents experience utility. Also, agents get utility from a pay-for-performance incentive mechanism. Thus, agents pursue two objectives. We place the interaction between social norms and performance-based incentive mechanisms in the complex environment of an organization with distributed decision-makers, in which a set of interdependent tasks is allocated to multiple agents. The results suggest that, unless the sets of assigned tasks are highly correlated, complying with emergent socially accepted behavior is detrimental to the organization's performance. However, we find that incentive schemes can help offset the performance loss by applying individual-based incentives in environments with lower task-complexity and team-based incentives in environments with higher task-complexity.

We consider a class of stochastic control problems with a delayed control, both in drift and diffusion, of the type dX t = $\alpha$ t--d (bdt + $\sigma$dW t). We provide a new characterization of the solution in terms of a set of Riccati partial differential equations. Existence and uniqueness are obtained under a sufficient condition expressed directly as a relation between the horizon T and the quantity d(b/$\sigma$) 2. Furthermore, a deep learning scheme is designed and used to illustrate the effect of delay on the Markowitz portfolio allocation problem with execution delay.

Knowledge management is an important aspect of an organization, especially in the ICT industry. Having more control of it is essentials for the organization to stay competitive in the business. One way to assess the organization's knowledge capital is by measuring employee knowledge networks and their personal reputation in social media. Using this measurement, we see how employees build relationships around their peer networks or clients virtually. We are also able to see how knowledge networks support organizational performance. The research objective is to map knowledge network and reputation formulation in order to fully understand how knowledge flow and whether employee reputation has a higher degree of influence in the organization's knowledge network. We particularly develop formulas to measure knowledge networks and personal reputation based on their social media activities. As a case study, we pick an Indonesian ICT company that actively build their business around their employee peer knowledge outside the company. For the knowledge network, we perform data collection by conducting interviews. For reputation management, we collect data from several popular social media. We base our work on Social Network Analysis (SNA) methodology. The result shows that employees' knowledge is directly proportional to their reputation, but there are different reputations level on different social media observed in this research.

In the context of modern marketing, Twitter is considered a communication platform to spread information. Many companies create and acquire several Twitter accounts to support and perform varieties of marketing mix activities. Initially, each accounts used to capture a specific market profile. Together, the accounts create a network of information that provide consumer to the information they need depends on their contextual utilization. From many accounts available, we have the fundamental question on how to measure the influence of each account in the market based not only on their relations but also on the effects of their postings. The magnitude of Influence (MOI) metric is adapted together with Influence Rank (IR) measurement of accounts in their social network neighborhood. We use social network analysis approach to analyze 65 accounts in the social network of an Indonesian mobile phone network operator, Telkomsel which involved in marketing communications mix activities through series of related tweets. Using social network provide the idea of the activity in building and maintaining relationships with the target audience. This paper shows the results of the most potential accounts based on the network structure and engagement. Based on this research, the more number of followers one account has, the more responsibility it has to generate the interaction from their followers in order to achieve the expected effectiveness. The focus of this paper is to determine the most potential accounts in the application of marketing communications mix in Twitter.

The price clustering phenomenon, i.e. an increased occurence of specific prices, is widely observed and well-documented for various financial instruments in various financial markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behaviour, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has positive impact on price clustering.

We consider the problem of reducing the carbon emissions of a set of firms over a finite horizon. A regulator dynamically allocates emission allowances to each firm. Firms face idiosyncratic as well as common economic shocks on emissions, and have linear quadratic abatement costs. Firms can trade allowances so to minimise total expected costs, from abatement and trading plus a quadratic terminal penalty. Using variational methods, we exhibit in closed-form the market equilibrium in function of regulator's dynamic allocation. We then solve the Stackelberg game between the regulator and the firms. Again, we obtain a closed-form expression of the dynamic allocation policies that allow a desired expected emission reduction. Optimal policies are not unique but share common properties. Surprisingly, all optimal policies induce a constant abatement effort and a constant price of allowances. Dynamic allocations outperform static ones because of adjustment costs and uncertainty, in particular given the presence of common shocks. Our results are robust to some extensions, like risk aversion of firms or different penalty functions.

Policymakers decide on alternative policies facing restricted budgets and uncertain, ever-changing future. Designing housing policies is further difficult giving the heterogeneous characteristics of properties themselves and the intricacy of housing markets and the spatial context of cities. We propose PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks to integrate economic, spatial and transport literature. PS2 is applied to a comparison among three competing municipal housing policies aimed at alleviating poverty: (a) property acquisition and distribution, (b) rental vouchers and (c) monetary aid. Within the model context, the monetary aid, that is, a smaller amounts of help for a larger number of households, makes the economy perform better in terms of production, consumption, reduction of inequality and maintenance of financial duties. PS2 as such is also a framework that may be further adapted to a number of related research questions.

We develop a model of inter-temporal and intra-temporal price discrimination by monopoly airlines to study the ability of different discriminatory pricing mechanisms to increase efficiency and the associated distributional implications. To estimate the model, we use unique data from international airline markets with flight-level variation in prices across time, cabins, and markets, as well as information on passengers' reasons for travel and time of purchase. We find that the ability to screen passengers across cabins every period increases total surplus by 35% relative to choosing only one price per period, with both the airline and passengers benefiting. However, further discrimination based on passenger's reason to traveling improve airline surplus at the expense of total efficiency. We also find that the current pricing practice yields approximately 89% of the first-best welfare. The source of this inefficiency arises mostly from dynamic uncertainty about demand, not private information about passenger valuations.

Online property business or known as e-commerce is currently experiencing an increase in home sales. Indonesia's e-commerce property business has positive trending shown by the increasing sales of more than 500% from 2011 to 2015. A prediction of the property price is important to help investors or the public to have accurate information before buying property. One of the methods for prediction is a classification based on several distinctive property industry attributes, such as building size, land size, number of rooms, and location. Today, data is easily obtained, there are many open data from E-commerce sites. E-commerce contains information about homes and other properties advertised to sell. People also regularly visit the site to find the right property or to sell the property using price information which collectively available as open data. To predict the property sales, this research employed two different classification methods in Data Mining which are Decision Tree and k-NN classification. We compare which model classification is better to predict property price and their attributes. We use Indonesia's biggest property-based e-commerce site Rumah123.com as our open data source, and choose location Bandung in our experiment. The accuracy result of the decision tree is 75% and KNN is 71%, other than that k-NN can explore more data patterns than the Decision Tree.

A multi-regional input-output table (MRIOT) containing the transactions among the region-sectors in an economy defines a weighted and directed network. Using network analysis tools, we analyze the regional and sectoral structure of the Chinese economy and their temporal dynamics from 2007 to 2012 via the MRIOTs of China. Global analyses are done with network topology measures. Growth-driving province-sector clusters are identified with community detection methods. Influential province-sectors are ranked by weighted PageRank scores. The results revealed a few interesting and telling insights. The level of inter-province-sector activities increased with the rapid growth of the national economy, but not as fast as that of intra-province economic activities. Regional community structures were deeply associated with geographical factors. The community heterogeneity across the regions was high and the regional fragmentation increased during the study period. Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.

The tourism industry is one of the potential revenues and has an important role in economics in Indonesia. The tourism Industry brings job and business opportunities, foreign exchange earnings, and infrastructure development, tourism also plays the role of one of the main drivers in socio-economic progress in Indonesia. The number of foreign tourists visiting Indonesia increase cumulatively and has reached 10.41 million visits or an increase of 10.46 percent from the same period in the previous year. Government trying to increase the number of tourists to visit Indonesia by promoting many Indonesian tourist attractions.

Knowledge is known to be a pre-condition for an individuals behavior. For the most efficient informational strategies for education, it is essential that we identify the types of knowledge that promote behavior effectively and investigate their structure. The purpose of this paper is therefore to examine the factors that affect Kenyan farmers, environmental citizenship behavior (ECB) in the context of Adaptation and mitigation (Climate smart agriculture). To achieve this objective, a theoretical framework has been developed based on value belief norm (VBN) theory. Design/methodology/approach, Data were obtained from 350 farmers using a survey method. Partial lease square structural equation modelling (PLS-SEM) was used to examine the hypothetical model. The results of PLS analysis confirm the direct and mediating effect of the causal sequences of the variables in the VBN model. The moderating role of Environmental knowledge has been seen to be impactful in Climate Smart Agriculture.