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on Computational Economics |
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Issue of 2020‒02‒24
nine papers chosen by |
| By: | Idris Kharroubi (LPSM UMR 8001); Thomas Lim (LaMME, ENSIIE); Xavier Warin (EDF) |
| Abstract: | We study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments. Mathematics Subject Classification (2010): 65C30, 65M75, 60H35, 93E20, 49L25. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.02675&r=all |
| By: | Sang Il Lee |
| Abstract: | In recent years, hyperparameter optimization (HPO) has become an increasingly important issue in the field of machine learning for the development of more accurate forecasting models. In this study, we explore the potential of HPO in modeling stock returns using a deep neural network (DNN). The potential of this approach was evaluated using technical indicators and fundamentals examined based on the effect the regularization of dropouts and batch normalization for all input data. We found that the model using technical indicators and dropout regularization significantly outperforms three other models, showing a positive predictability of 0.53% in-sample and 1.11% out-of-sample, thereby indicating the possibility of beating the historical average. We also demonstrate the stability of the model in terms of the changes in its feature importance over time. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.10278&r=all |
| By: | Francois Belletti; Davis King; James Lottes; Yi-Fan Chen; John Anderson |
| Abstract: | In a recent paper, we have demonstrated how the affinity between TPUs and multi-dimensional financial simulation resulted in fast Monte Carlo simulations that could be setup in a few lines of python Tensorflow code. We also presented a major benefit from writing high performance simulations in an automated differentiation language such as Tensorflow: a single line of code enabled us to estimate sensitivities, i.e. the rate of change in price of financial instrument with respect to another input such as the interest rate, the current price of the underlying, or volatility. Such sensitivities (otherwise known as the famous financial "Greeks") are fundamental for risk assessment and risk mitigation. In the present follow-up short paper, we extend the developments exposed in our previous work about the use of Tensor Processing Units and Tensorflow for TPUs. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.02481&r=all |
| By: | Alberto Ciacci; Takumi Sueshige; Hideki Takayasu; Kim Christensen; Misako Takayasu |
| Abstract: | Foreign exchange rates movements exhibit significant cross-correlations even on very short time-scales. The effect of these statistical relationships become evident during extreme market events, such as flash crashes.In this scenario, an abrupt price swing occurring on a given market is immediately followed by anomalous movements in several related foreign exchange rates. Although a deep understanding of cross-currency correlations would be clearly beneficial for conceiving more stable and safer foreign exchange markets, the microscopic origins of these interdependencies have not been extensively investigated. We introduce an agent-based model which describes the emergence of cross-currency correlations from the interactions between market makers and an arbitrager. Our model qualitatively replicates the time-scale vs. cross-correlation diagrams observed in real trading data, suggesting that triangular arbitrage plays a primary role in the entanglement of the dynamics of different foreign exchange rates. Furthermore, the model shows how the features of the cross-correlation function between two foreign exchange rates, such as its sign and value, emerge from the interplay between triangular arbitrage and trend-following strategies. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.02583&r=all |
| By: | Fisker Peter; Sohnesen Thomas; Malmgren-Hansen David |
| Abstract: | Using convolutional neural networks applied to satellite images covering a 25 km x 12 km rectangle on the northern outskirts of Greater Maputo, we detect and classify buildings from 2010 and 2018 in order to compare the development in quantity and quality of buildings from before and after construction of a major section of ring road.In addition, we analyse how the effects vary by distance to the road and conclude that the area has seen large overall growth in both quantity and quality of housing, but it is not possible to distinguish growth close to the road from general urban growth.Finally, the paper contributes methodologically to a growing strand of literature focused on combining machine-learning image recognition and the availability of high-resolution satellite images. We examine the extent to which it is possible to exploit these methods to analyse changes over time and thus provide an alternative (or complement) to traditional impact analyses. |
| Keywords: | Impact evaluation,infrastructure,remote sensing,Mozambique |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2019-111&r=all |
| By: | Despoina Makariou; Pauline Barrieu; Yining Chen |
| Abstract: | We introduce a random forest approach to enable spreads' prediction in the primary catastrophe bond market. We investigate whether all information provided to investors in the offering circular prior to a new issuance is equally important in predicting its spread. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. The random forest shows an impressive predictive power on unseen primary catastrophe bond data explaining 93% of the total variability. For comparison, linear regression, our benchmark model, has inferior predictive performance explaining only 47% of the total variability. All details provided in the offering circular are predictive of spread but in a varying degree. The stability of the results is studied. The usage of random forest can speed up investment decisions in the catastrophe bond industry. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.10393&r=all |
| By: | Verme, Paolo |
| Abstract: | OLS models are the predominant choice for poverty predictions in a variety of contexts such as proxy-means tests, poverty mapping or cross-survey impu- tations. This paper compares the performance of econometric and machine learning models in predicting poverty using alternative objective functions and stochastic dominance analysis based on coverage curves. It finds that the choice of an optimal model largely depends on the distribution of incomes and the poverty line. Comparing the performance of different econometric and machine learning models is therefore an important step in the process of opti- mizing poverty predictions and targeting ratios. |
| Keywords: | Welfare Modelling,Income Distributions,Poverty Predictions,Imputations |
| JEL: | D31 D63 E64 O15 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:468&r=all |
| By: | Myrto Kalouptsidi (Harvard University); Yuichi Kitamura (Cowles Foundation, Yale University); Lucas Lima (Harvard University); Eduardo Souza-Rodrigues (University of Toronto) |
| Abstract: | We provide a general framework for investigating partial identiï¬ cation of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions. We characterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identiï¬ ed set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms. We obtain a uniformly valid inference procedure by an appropriate application of subsampling. To illustrate the performance and computational feasibility of the method, we consider both a Monte Carlo study of ï¬ rm entry/exit, and an empirical model of export decisions applied to plant-level data from Colombian manufacturing industries. In these applications, we demonstrate how the identiï¬ ed sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identiï¬ cation. |
| Keywords: | Dynamic Discrete Choice, Counterfactual, Partial Identification, Subsampling, Uniform Inference, Structural Model |
| JEL: | C18 C61 C63 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:2221&r=all |
| By: | Walter Farkas; Ludovic Mathys; Nikola Vasiljevi\'c |
| Abstract: | The present article deals with intra-horizon risk in models with jumps. Our general understanding of intra-horizon risk is along the lines of the approach taken in Boudoukh, Richardson, Stanton and Whitelaw (2004), Rossello (2008), Bhattacharyya, Misra and Kodase (2009), Bakshi and Panayotov (2010), and Leippold and Vasiljevi\'c (2019). In particular, we believe that quantifying market risk by strictly relying on point-in-time measures cannot be deemed a satisfactory approach in general. Instead, we argue that complementing this approach by studying measures of risk that capture the magnitude of losses potentially incurred at any time of a trading horizon is necessary when dealing with (m)any financial position(s). To address this issue, we propose an intra-horizon analogue of the expected shortfall for general profit and loss processes and discuss its key properties. Our intra-horizon expected shortfall is well-defined for (m)any popular class(es) of L\'evy processes encountered when modeling market dynamics and constitutes a coherent measure of risk, as introduced in Cheridito, Delbaen and Kupper (2004). On the computational side, we provide a simple method to derive the intra-horizon risk inherent to popular L\'evy dynamics. Our general technique relies on results for maturity-randomized first-passage probabilities and allows for a derivation of diffusion and single jump risk contributions. These theoretical results are complemented with an empirical analysis, where popular L\'evy dynamics are calibrated to S&P 500 index data and an analysis of the resulting intra-horizon risk is presented. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.04675&r=all |