Myanmar is languishing at the bottom of key international indexes. United Nations considers the country as a structurally weak and vulnerable economy. Yet, from 2011 when Myanmar ended decades of military rule and isolationism and transited towards democracy, its breakneck development has led to many considering the country to be one of the final frontiers for growth in the Asia region. One such industry that has benefitted from the opening of the country is telecommunications. The mobile penetration rate at 4.8% in 2011 has increased significantly to 90% in 2016. Despite renewed optimism and development in the economy, one statistic remains disappointing. According to a report by Asian Development Bank (ADB), only 23% of the adult population have access to a bank account. This highlights a need to reach out and increase access to financial resources to a population that is severely unbanked and underbanked. This creates an interesting proposition of allowing both the telecommunications and financial sector to form the mobile financial services (MFS) sector and meet the need of improving access to financial resources for the population. This report explores the government role in supporting, growing and sustaining the MFS sector and conducts a comparative research into Singapore, Malaysia and Thailand to understand the steps taken by these governments to develop their own Financial Technology (FinTech), specifically MFS, industry. Finally, the report will present preliminary recommendations that the Myanmar government could consider implementing to drive growth in its MFS sector.

We propose a novel probabilistic model to facilitate the learning of multivariate tail dependence of multiple financial assets. Our method allows one to construct from known random vectors, e.g., standard normal, sophisticated joint heavy-tailed random vectors featuring not only distinct marginal tail heaviness, but also flexible tail dependence structure. The novelty lies in that pairwise tail dependence between any two dimensions is modeled separately from their correlation, and can vary respectively according to its own parameter rather than the correlation parameter, which is an essential advantage over many commonly used methods such as multivariate $t$ or elliptical distribution. It is also intuitive to interpret, easy to track, and simple to sample comparing to the copula approach. We show its flexible tail dependence structure through simulation. Coupled with a GARCH model to eliminate serial dependence of each individual asset return series, we use this novel method to model and forecast multivariate conditional distribution of stock returns, and obtain notable performance improvements in multi-dimensional coverage tests. Besides, our empirical finding about the asymmetry of tails of the idiosyncratic component as well as the market component is interesting and worth to be well studied in the future.

Attaining the optimal scale size of production systems is an issue frequently found in the priority questions on management agendas of various types of organizations. Determining the most productive scale size (MPSS) allows the decision makers not only to know the best scale size that their systems can achieve but also to tell the decision makers how to move the inefficient systems onto the MPSS region. This paper investigates the MPSS concept for production systems consisting of multiple subsystems connected in parallel. First, we propose a relational model where the MPSS of the whole system and the internal subsystems are measured in a single DEA implementation. Then, it is proved that the MPSS of the system can be decomposed as the weighted sum of the MPSS of the individual subsystems. The main result is that the system is overall MPSS if and only if it is MPSS in each subsystem. MPSS decomposition allows the decision makers to target the non-MPSS subsystems so that the necessary improvements can be readily suggested. An application of China's Five-Year Plans (FYPs) with shared inputs is used to show the applicability of the proposed model for estimating and decomposing MPSS in parallel network DEA. Industry and Agriculture sectors are selected as two parallel subsystems in the FYPs. Interesting findings have been noticed. Using the same amount of resources, the Industry sector had a better economic scale than the Agriculture sector. Furthermore, the last two FYPs, 11th and 12th, were the perfect two FYPs among the others.

An important question in economics is how people choose between different payments in the future. The classical normative model predicts that a decision maker discounts a later payment relative to an earlier one by an exponential function of the time between them. Descriptive models use non-exponential functions to fit observed behavioral phenomena, such as preference reversal. Here we propose a model of discounting, consistent with standard axioms of choice, in which decision makers maximize the growth rate of their wealth. Four specifications of the model produce four forms of discounting -- no discounting, exponential, hyperbolic, and a hybrid of exponential and hyperbolic -- two of which predict preference reversal. Our model requires no assumption of behavioral bias or payment risk.

Challenge Theory (CT), a new approach to decision under risk departs significantly from expected utility, and is based on firmly psychological, rather than economic, assumptions. The paper demonstrates that a purely cognitive-psychological paradigm for decision under risk can yield excellent predictions, comparable to those attained by more complex economic or psychological models that remain attached to conventional economic constructs and assumptions. The study presents a new model for predicting the popularity of choices made in binary risk problems. A CT-based regression model is tested on data gathered from 126 respondents who indicated their preferences with respect to 44 choice problems. Results support CT's central hypothesis, strongly associating between the Challenge Index (CI) attributable to every binary risk problem, and the observed popularity of the bold prospect in that problem (with r=-0.92 and r=-0.93 for gains and for losses, respectively). The novelty of the CT perspective as a new paradigm is illuminated by its simple, single-index (CI) representation of psychological effects proposed by Prospect Theory for describing choice behavior (certainty effect, reflection effect, overweighting small probabilities and loss aversion).

Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed. In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via model-free reinforcement learning. We calibrate the model to real market data from the London Stock Exchange over the years 2007 to 2018, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals. Agent learning thus enables model emulation of the microstructure with superior realism.

The link between taxation and justice is a classic debate issue, while also being very relevant at a time of changing environmental factors and conditions of the social and economic system. Technologically speaking, there are three types of taxes: progressive, proportional and regressive. Although justice, like freedom, is an element and manifestation of the imagined reality in citizens minds, the state must comply with it. In particular, the tax system has to adapt to the mass imagined reality in order for it to appear fairer and more acceptable.

We propose a non-robust interpretation of the distributionally robust optimization (DRO) problem by relating the impact of uncertainties around the distribution on the impact of constraining the objective through tail probabilities. Our interpretation allows utility maximizers to figure out the size of the ambiguity set through parameters that are directly linked to the chance parameters. We first show that for general $\phi$-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems, where the ambiguity radius controls investor's risk preference. Based on this non-robust reformulation, we then show that when a boundedness constraint is added to the investment strategy. The DRO problem can be cast as a chance-constrained optimization (CCO) problem without distributional uncertainties. Without the boundedness constraint, the CCO problem is shown to perform uniformly better than the DRO problem, irrespective of the radius of the ambiguity set, the choice of the divergence measure, or the tail heaviness of the center distribution. Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our results apply to divergence measures that accommodate well heavy tail distribution such as the student $t$-distribution and the lognormal distribution. Comprehensive testings on synthetic data and real data are provided.