A deep solver for BSDEs with jumps

The aim of this work is to propose an extension of the Deep BSDE solver by Han, E, Jentzen (2017) to the case of FBSDEs with jumps. As in the aforementioned solver, starting from a discretized version of the BSDE and parametrizing the (high dimensional) control processes by means of a family of ANNs, the BSDE is viewed as model-based reinforcement learning problem and the ANN parameters are fitted so as to minimize a prescribed loss function. We take into account both finite and infinite jump activity by introducing, in the latter case, an approximation with finitely many jumps of the forward process.Comment: 31 page

A deep solver for BSDEs with jumps

The aim of this work is to propose an extension of the Deep BSDE solver by Han, E, Jentzen (2017) to the case of FBSDEs with jumps. As in the aforementioned solver, starting from a discretized version of the BSDE and parametrizing the (high dimensional) control processes by means of a family of artificial neural networks (ANNs), the BSDE is viewed as model-based reinforcement learning problem and the ANN parameters are fitted so as to minimize a prescribed loss function. We take into account both finite and infinite jump activity by introducing, in the latter case, an approximation with finitely many jumps of the forward process

A change of measure formula for recursive conditional expectations

In this paper, we derive a representation for the value process associated to the solutions of FBSDEs in a jump-diffusion setting under multiple probability measures. Motivated by concrete financial problems, the latter representations are then applied to devise a generalization of the change of num\'eraire technique allowing to obtain recursive pricing formulas in the presence of multiple interest rates and collateralization.Comment: 25 pages. Minor typos remove

Three billion new trees in the EU’s biodiversity strategy:low ambition, but better environmental outcomes?

The EU Biodiversity strategy aims to plant 3 billion trees by 2030, in order to improve ecosystem restoration and biodiversity. Here, we compute the land area that would be required to support this number of newly planted trees by taking account of different tree species and planting regimes across the EU member states. We find that 3 billion trees would require a total land area of between 0.81 and 1.37 Mha (avg. 1.02 Mha). The historic forest expansion in the EU since 2010 was 2.44 Mha, meaning that despite 3 billion trees sounding like a large number this target is considerably lower than historic afforestation rates within the EU, i.e. only 40% of the past trend. Abandoned agricultural land is often proposed as providing capacity for afforestation. We estimate agricultural abandoned land areas from the HIstoric Land Dynamics Assessment+ database using two time thresholds (abandonment since 2009 or 2014) to identify potential areas for tree planting. The area of agricultural abandoned land was 2.6 Mha (potentially accommodating 7.2 billion trees) since 2009 and 0.2 Mha (potentially accommodating 741 million trees) since 2014. Our study highlights that sufficient space could be available to meet the 3 billion tree planting target from abandoned land. However, large-scale afforestation beyond abandoned land could have displacement effects elsewhere in the world because of the embodied deforestation in the import of agricultural crops and livestock. This would negate the expected benefits of EU afforestation. Hence, the EU’s relatively low ambition on tree planting may actually be better in terms of avoiding such displacement effects. We suggest that tree planting targets should be set at a level that considers physical ecosystem dynamics as well as socio-economic conditions.</p

Three billion new trees in the EU’s biodiversity strategy: low ambition, but better environmental outcomes?

The EU Biodiversity strategy aims to plant 3 billion trees by 2030, in order to improve ecosystem restoration and biodiversity. Here, we compute the land area that would be required to support this number of newly planted trees by taking account of different tree species and planting regimes across the EU member states. We find that 3 billion trees would require a total land area of between 0.81 and 1.37 Mha (avg. 1.02 Mha). The historic forest expansion in the EU since 2010 was 2.44 Mha, meaning that despite 3 billion trees sounding like a large number this target is considerably lower than historic afforestation rates within the EU, i.e. only 40% of the past trend. Abandoned agricultural land is often proposed as providing capacity for afforestation. We estimate agricultural abandoned land areas from the HIstoric Land Dynamics Assessment+ database using two time thresholds (abandonment since 2009 or 2014) to identify potential areas for tree planting. The area of agricultural abandoned land was 2.6 Mha (potentially accommodating 7.2 billion trees) since 2009 and 0.2 Mha (potentially accommodating 741 million trees) since 2014. Our study highlights that sufficient space could be available to meet the 3 billion tree planting target from abandoned land. However, large-scale afforestation beyond abandoned land could have displacement effects elsewhere in the world because of the embodied deforestation in the import of agricultural crops and livestock. This would negate the expected benefits of EU afforestation. Hence, the EU\u27s relatively low ambition on tree planting may actually be better in terms of avoiding such displacement effects. We suggest that tree planting targets should be set at a level that considers physical ecosystem dynamics as well as socio-economic conditions

Significant increase in natural disturbance impacts on European forests since 1950

Over the last decades, the natural disturbance is increasingly putting pressure on European forests. Shifts in disturbance regimes may compromise forest functioning and the continuous provisioning of ecosystem services to society, including their climate change mitigation potential. Although forests are central to many European policies, we lack the long-term empirical data needed for thoroughly understanding disturbance dynamics, modeling them, and developing adaptive management strategies. Here, we present a unique database of >170,000 records of ground-based natural disturbance observations in European forests from 1950 to 2019. Reported data confirm a significant increase in forest disturbance in 34 European countries, causing on an average of 43.8 million m3 of disturbed timber volume per year over the 70-year study period. This value is likely a conservative estimate due to under-reporting, especially of small-scale disturbances. We used machine learning techniques for assessing the magnitude of unreported disturbances, which are estimated to be between 8.6 and 18.3 million m3/year. In the last 20 years, disturbances on average accounted for 16% of the mean annual harvest in Europe. Wind was the most important disturbance agent over the study period (46% of total damage), followed by fire (24%) and bark beetles (17%). Bark beetle disturbance doubled its share of the total damage in the last 20 years. Forest disturbances can profoundly impact ecosystem services (e.g., climate change mitigation), affect regional forest resource provisioning and consequently disrupt long-term management planning objectives and timber markets. We conclude that adaptation to changing disturbance regimes must be placed at the core of the European forest management and policy debate. Furthermore, a coherent and homogeneous monitoring system of natural disturbances is urgently needed in Europe, to better observe and respond to the ongoing changes in forest disturbance regimes

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

The Quantitative Easing Bursts Bitcoin Price

In this paper we analyze the existence of cointegrating relationships between Bitcoin, S&amp;P 500, and the quantity of money M2. We perform our analysis with and without applying time warping pre-processing. In all cases we find strong evidence that, in the period 2016-2021 the three time series show two cointegrating relationships and therefore share a common stochastic trend. In addition, a low correlation between Bitcoin and S&amp;P 500 is detected. These finding justify the increased interest of investors in Bitcoin as an alternative asset class. The economic interpretation is that the stock valuation is primarily determined by financial phenomena, in particular the availability of large quantity of money. Money supporting investment is due both to the actions of Quantitative Easing and to the exchange of creditor/debtor role that took place between households and firms. The price of both Bitcoin and stocks is increasingly influenced by the amount of money in circulation and follows the same stochastic trend