SREC markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh. Firms offset these certificates against the floor and pay a penalty for any lacking certificates. Certificates are tradable assets, allowing firms to purchase/sell them freely. In this work, we formulate a stochastic control problem for generating and trading in SREC markets from a regulated firm's perspective. We account for generation and trading costs, the impact both have on SREC prices, provide a characterization of the optimal strategy, and develop a numerical algorithm to solve this control problem. Through numerical experiments, we explore how a firm who acts optimally behaves under various conditions. We find that an optimal firm's generation and trading behaviour can be separated into various regimes, based on the marginal benefit of obtaining an additional SREC, and validate our theoretical characterization of the optimal strategy. We also conduct parameter sensitivity experiments and conduct comparisons of the optimal strategy to other candidate strategies.

Many countries are ethnically diverse. However, despite the benefits of ethnic heterogeneity, ethnic-based political inequality and discrimination are pervasive. Why is this? This study suggests that part of the variation in ethnic-based political inequality depends on the relative size of ethnic groups within each country. Using group-level data for 569 ethnic groups in 175 countries from 1946 to 2017, I find evidence of an inverted-U-shaped relationship between an ethnic group's relative size and its access to power. This single-peaked relationship is robust to many alternative specifications, and a battery of robustness checks suggests that relative size influences access to power. Through a very simple model, I propose an explanation based on an initial high level of political inequality, and on the incentives that more powerful groups have to continue limiting other groups' access to power. This explanation incorporates essential elements of several existing theories on the relationship between group size and discrimination, and suggests a new empirical prediction: the single-peaked pattern should be weaker in countries where political institutions have historically been less open. This additional prediction is supported by the data.

In order to scale transaction rates for deployment across the global web, many cryptocurrencies have deployed so-called "Layer-2" networks of private payment channels. An idealized payment network behaves like a Credit Network, a model for transactions across a network of bilateral trust relationships. Credit Networks capture many aspects of traditional currencies as well as new virtual currencies and payment mechanisms. In the traditional credit network model, if an agent defaults, every other node that trusted it is vulnerable to loss. In a cryptocurrency context, trust is manufactured by capital deposits, and thus there arises a natural tradeoff between network liquidity (i.e. the fraction of transactions that succeed) and the cost of capital deposits.

In this paper, we introduce constraints that bound the total amount of loss that the rest of the network can suffer if an agent (or a set of agents) were to default - equivalently, how the network changes if agents can support limited solvency guarantees.

We show that these constraints preserve the analytical structure of a credit network. Furthermore, we show that aggregate borrowing constraints greatly simplify the network structure and in the payment network context achieve the optimal tradeoff between liquidity and amount of escrowed capital.

The paper explains the low-volatility anomaly from a new perspective. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find the basis assets significantly related to each of the portfolios. The AMF results show that the two portfolios load on very different factors, which indicates that the volatility is not an independent measure of risk, but are related to the basis assets and risk factors in the related industries. It is the performance of the loaded factors that results in the low-volatility anomaly. The out-performance of the low-volatility portfolio may not because of its low-risk (which contradicts the risk-premium theory), but because of the out-performance of the risk factors the low-volatility portfolio is loaded on. Also, we compare the AMF model with the traditional Fama-French 5-factor (FF5) model in various aspects, which shows the superior performance of the AMF model over FF5 in many perspectives.

This article studies the interregional Greek road network (GRN) by applying complex network analysis (CNA) and an empirical approach. The study aims to extract the socioeconomic information immanent to the GRN's topology and to interpret the way in which this road network serves and promotes the regional development. The analysis shows that the topology of the GRN is submitted to spatial constraints, having lattice-like characteristics. Also, the GRN's structure is described by a gravity pattern, where places of higher population enjoy greater functionality, and its interpretation in regional terms illustrates the elementary pattern expressed by regional development through road construction. The study also reveals some interesting contradictions between the metropolitan and non-metropolitan (excluding Attica and Thessaloniki) comparison. Overall, the article highlights the effectiveness of using complex network analysis in the modeling of spatial networks and in particular of transportation systems and promotes the use of the network paradigm in the spatial and regional research.

An economic model of crime is used to explore the consistent estimation of a simultaneous linear equation without recourse to instrumental variables. A maximum-likelihood procedure (NISE) is introduced, and its results are compared to ordinary least squares and two-stage least squares. The paper is motivated by previous research on the crime model and by the well-known practical problem that valid instruments are frequently unavailable.

We introduce a strategic behavior in reinsurance bilateral transactions, where agents choose the risk preferences they will appear to have in the transaction. Within a wide class of risk measures, we identify agents' strategic choices to a range of risk aversion coefficients. It is shown that at the strictly beneficial Nash equilibria, agents appear homogeneous with respect to their risk preferences. While the game does not cause any loss of total welfare gain, its allocation between agents is heavily affected by the agents' strategic behavior. This allocation is reflected in the reinsurance premium, while the insurance indemnity remains the same in all strictly beneficial Nash equilibria. Furthermore, the effect of agents' bargaining power vanishes through the game procedure and the agent who gets more welfare gain is the one who has an advantage in choosing the common risk aversion at the equilibrium.

In a market with a rough or Markovian mean-reverting stochastic volatility there is no perfect hedge. Here it is shown how various delta-type hedging strategies perform and can be evaluated in such markets in the case of European options. A precise characterization of the hedging cost, the replication cost caused by the volatility fluctuations, is presented in an asymptotic regime of rapid mean reversion for the volatility fluctuations. The optimal dynamic asset based hedging strategy in the considered regime is identified as the so-called `practitioners' delta hedging scheme. It is moreover shown that the performances of the delta-type hedging schemes are essentially independent of the regularity of the volatility paths in the considered regime and that the hedging costs are related to a vega risk martingale whose magnitude is proportional to a new market risk parameter. It is also shown via numerical simulations that the proposed hedging schemes which derive from option price approximations in the regime of rapid mean reversion, are robust: the `practitioners' delta hedging scheme that is identified as being optimal by our asymptotic analysis when the mean reversion time is small seems to be optimal with arbitrary mean reversion times.

We present a parsimonious stochastic model for valuation of options on the fraction of infected individuals during an epidemic. The underlying stochastic dynamical system is a stochastic differential version of the SIR model of mathematical epidemiology.

In the information-based pricing framework of Brody, Hughston and Macrina, the market filtration $\{ \mathcal F_t\}_{t\geq 0}$ is generated by an information process $\{ \xi_t\}_{t\geq0}$ defined in such a way that at some fixed time $T$ an $\mathcal F_T$-measurable random variable $X_T$ is "revealed". A cash flow $H_T$ is taken to depend on the market factor $X_T$, and one considers the valuation of a financial asset that delivers $H_T$ at $T$. The value $S_t$ of the asset at any time $t\in[0,T)$ is the discounted conditional expectation of $H_T$ with respect to $\mathcal F_t$, where the expectation is under the risk neutral measure and the interest rate is constant. Then $S_{T^-} = H_T$, and $S_t = 0$ for $t\geq T$. In the general situation one has a countable number of cash flows, and each cash flow can depend on a vector of market factors, each associated with an information process. In the present work, we construct a new class of models for the market filtration based on the variance-gamma process. The information process is obtained by subordinating a particular type of Brownian random bridge with a gamma process. The filtration is taken to be generated by the information process together with the gamma bridge associated with the gamma subordinator. We show that the resulting extended information process has the Markov property and hence can be used to price a variety of different financial assets, several examples of which are discussed in detail.

Interest in predicting multivariate probability distributions is growing due to the increasing availability of rich datasets and computational developments. Scoring functions enable the comparison of forecast accuracy, and can potentially be used for estimation. A scoring function for multivariate distributions that has gained some popularity is the energy score. This is a generalization of the continuous ranked probability score (CRPS), which is widely used for univariate distributions. A little-known, alternative generalization is the multivariate CRPS (MCRPS). We propose a theoretical framework for scoring functions for multivariate distributions, which encompasses the energy score and MCRPS, as well as the quadratic score, which has also received little attention. We demonstrate how this framework can be used to generate new scores. For univariate distributions, it is well-established that the CRPS can be expressed as the integral over a quantile score. We show that, in a similar way, scoring functions for multivariate distributions can be "disintegrated" to obtain scoring functions for level sets. Using this, we present scoring functions for different types of level set, including those for densities and cumulative distributions. To compute the scoring functions, we propose a simple numerical algorithm. We illustrate our proposals using simulated and stock returns data.

This article studies the Greek interregional commuting network (GRN) by using measures and methods of complex network analysis and empirical techniques. The study aims to detect structural characteristics of the commuting phenomenon, which are configured by the functionality of the land transport infrastructures, and to interpret how this network serves and promotes the regional development. In the empirical analysis, a multiple linear regression model for the number of commuters is constructed, which is based on the conceptual framework of the term network, in effort to promote the interdisciplinary dialogue. The analysis highlights the effect of the spatial constraints on the network's structure, provides information on the major road transport infrastructure projects that constructed recently and influenced the country capacity, and outlines a gravity pattern describing the commuting phenomenon, which expresses that cities of high population attract large volumes of commuting activity within their boundaries, a fact that contributes to the reduction of their outgoing commuting and consequently to the increase of their inbound productivity. Overall, this paper highlights the effectiveness of complex network analysis in the modeling of spatial and particularly of transportation network and promotes the use of the network paradigm in the spatial and regional research.

This article attempts to highlight the importance that transportation has in the economic development of Greece and in particular the importance of the transportation infrastructure and transportation networks, which suggest a fixed structured capital covering the total of the country. For this purpose, longitudinal and cross-sectoral statistical data are examined over a set of fundamental macroeconomic measures and metrics. Furthermore, the study attempts to highlight the structural and functional aspects composing the concept of transportation networks and to highlight the necessity of their joint consideration on the relevant research. The transportation networks that are examined in this paper are the Greek road (GRN), rail (GRAN), maritime (GMN) and air transport network (GAN), which are studied both in terms of their geometry and technical characteristics, as well as of their historical, traffic and political framework. For the empirical assessment of the transportation networks importance in Greece an econometric model is constructed, expressing the welfare level of the Greek regions as a multivariate function of their transportation infrastructure and of their socioeconomic environment. The further purpose of the article is to highlight, macroscopically, all the aspects related the study of transportation infrastructure and networks.