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On building inference for the statistical neural network with application to Naira-Dollar exchange rate efficiency : a bootstrap approach
Udomboso, Christopher G.; Saliu, Francisco U. - In: CBN journal of applied statistics 7 (2016) 2, pp. 1-14
In this study, we developed an inference procedure for the neural network using the bootstrap approach, and applied it … to the market efficiency of the Nigerian exchange rate. Data used are exchange rate values from 2001 to 2015. We … conducted a test on the market efficiency hypothesis, including test for relevance of individual and joint network inputs using …
Persistent link: https://www.econbiz.de/10011661509
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Mixtures of t-distributions for finance and forecasting
Giacomini, Raffaella; Gottschling, Andreas; Häfke, … - 2007
We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the...
Persistent link: https://www.econbiz.de/10009735358
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Classifying payment patterns with artificial neural networks : an autoencoder approach
Rubio, Jeniffer; Barucca, Paolo; Gage, Gerardo; Arroyo, John - In: Latin American journal of central banking : LAJCB 1 (2020) 1/4, pp. 1-16
Payments and market infrastructures are the backbone of modern financial systems and play a key role in the economy. One of their main goals is to manage systemic risk, especially in the case of systemically important payment systems (SIPS) serving interbank funds transfers. We develop an...
Persistent link: https://www.econbiz.de/10012545615
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Whatever it takes to understand a central banker : embedding their words using neural networks
Baumgärtner, Martin; Zahner, Johannes - 2021 - Draft: August 2, 2021
Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we...
Persistent link: https://www.econbiz.de/10012608497
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Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?
Teng, Huei-Wen; Li, Yu-Hsien - In: Digital finance : smart data analytics, investment … 5 (2023) 1, pp. 149-182
Persistent link: https://www.econbiz.de/10014251571
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Option pricing with neural networks vs. Black-Scholes under different volatility forecasting approaches for BIST 30 index options
İltüzer, Zeynep - In: Borsa Istanbul Review 22 (2022) 4, pp. 725-742
This study compares the performances of neural network and Black-Scholes models in pricing BIST30 (Borsa Istanbul) index call and put options with different volatility forecasting approaches. Since the volatility is the key parameter in pricing options, GARCH (Generalized Autoregressive...
Persistent link: https://www.econbiz.de/10013334825
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High-frequency CSI300 futures trading volume predicting through the neural network
Xu, Xiaojie; Zhang, Yun - In: Asian journal of economics and banking : AJEB 8 (2024) 1, pp. 26-53
Purpose - For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch...
Persistent link: https://www.econbiz.de/10014497016
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Whatever it takes to understand a central banker : embedding their words using neural networks
Baumgärtner, Martin; Zahner, Johannes - 2023 - This draft: December 14, 2023
Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we...
Persistent link: https://www.econbiz.de/10014443189
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Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines
Südbeck, Insa; Mindlina, Julia; Schnabel, André; … - In: Schmalenbach journal of business research : SBUR 76 (2024) 2, pp. 267-291
Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a...
Persistent link: https://www.econbiz.de/10014551040
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Mixtures of t-distributions for finance and forecasting
Giacomini, Raffaella; Gottschling, Andreas; Häfke, … - 2007
Persistent link: https://www.econbiz.de/10003556381