Evaluating Spread Decomposition Models with a Basket Security

2008 ◽  
Author(s):  
Patricia Chelley-Steeley ◽  
Keebong Park
Keyword(s):  
Decomposition Models ◽  
Spread Decomposition

scholarly journals The accuracy of spread decomposition models in capturing informed trades

2017 ◽  
Vol 9 (1) ◽  
pp. 2-13
Author(s):  
Andros Gregoriou ◽  
Mark Rhodes
Keyword(s):  
Stock Exchange ◽  
Empirical Relationship ◽  
Market Liquidity ◽  
Content Type ◽  
Bid Ask Spread ◽  
Overwhelming Evidence ◽  
Model Predictions ◽  
Decomposition Models ◽  
Spread Decomposition ◽  
Trade Prices

Purpose The purpose of this paper is to examine the empirical relationship between trades undertaken by informed agents (managers) and the proxies for informed trades computed by bid-ask spread decomposition models. Design/methodology/approach An econometric application of spread decomposition models to data from the London Stock Exchange, with an examination of whether the model predictions are co-integrated with actual outcomes. Findings The authors find overwhelming evidence of non-stationary behaviour between the actual and predicted informed trade prices. The findings suggest that there is a clear need for an alternative to extant spread decomposition models perhaps incorporating findings from behavioural finance. Originality/value Given the importance of stock market liquidity and the extensive use of spread decomposition models in predicting informed trades, the authors believe that the research conducted in the paper is an important contribution to the market microstructure literature.

Real-domain QR decomposition models employing zeroing neural network and time-discretization formulas for time-varying matrices

2021 ◽  
Vol 448 ◽  
pp. 217-227
Author(s):  
Zhenyu Li ◽  
Yunong Zhang ◽  
Liangjie Ming ◽  
Jinjin Guo ◽  
Vasilios N. Katsikis
Keyword(s):  
Neural Network ◽  
Time Discretization ◽  
Qr Decomposition ◽  
Time Varying ◽  
Real Domain ◽  
Decomposition Models

Evaluation of transposition and decomposition models for converting global solar irradiance from tilted surface to horizontal in tropical regions

Solar Energy ◽  
2013 ◽  
Vol 97 ◽  
pp. 369-387 ◽  
Author(s):  
Dazhi Yang ◽  
Zibo Dong ◽  
André Nobre ◽  
Yong Sheng Khoo ◽  
Panida Jirutitijaroen ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Tropical Regions ◽  
Tilted Surface ◽  
Decomposition Models

Peak detection in sediment - charcoal records: impacts of alternative data analysis methods on fire-history interpretations

2010 ◽  
Vol 19 (8) ◽  
pp. 996 ◽  
Author(s):  
Philip E. Higuera ◽  
Daniel G. Gavin ◽  
Patrick J. Bartlein ◽  
Douglas J. Hallett
Keyword(s):  
Data Analysis ◽  
Fire History ◽  
Threshold Value ◽  
Decomposition Methods ◽  
Peak Detection ◽  
Detection Methods ◽  
Statistical Framework ◽  
Analysis Methods ◽  
Decomposition Models ◽  
Data Analysis Methods

Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight alternative decomposition models (four detrending methods used with two threshold-determination methods) and evaluate their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the combination of a globally defined threshold with specific detrending methods can produce strongly biased results, depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the alternative decomposition methods on three previously published charcoal records largely supports our conclusions from simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are warranted with a given sediment–charcoal record.

Three-Dimensional Decomposition Models for Chinese Manufacturing Subsector over 1996-2008

2013 ◽  
Vol 291-294 ◽  
pp. 3004-3013
Author(s):  
Ding Ma ◽  
Li Ning Wang ◽  
Wen Ying Chen
Keyword(s):  
Energy Intensity ◽  
Three Dimensional ◽  
Ghg Emissions ◽  
Carbon Intensity ◽  
Emissions Reduction ◽  
Country Level ◽  
Rapid Economic Growth ◽  
Decomposition Models ◽  
Energy And Climate Policy ◽  
The Impact

At a time of increased international concern and negotiation for GHG emissions reduction, country studies on the underlying effects of GHG growth gain importance. China experienced continuous, rapid economic growth over the past. At the same time, energy consumption and CO2 emissions increased rapidly while the energy intensity and carbon intensity showed a downward trend at country level. What factors were driving this change? What measures can be adopted to ensure the continual decrease of energy intensity and carbon intensity? The refined IDA method is employed in this paper to identify the impact of each factor. A year-by-year decomposition is carried out at sector level, and various interesting results on the underlying effects are found. The results yield important hints for the planning of energy and climate policy.

scholarly journals ORCHIMIC (v1.0), a microbe-driven model for soil organic matter decomposition designed for large-scale applications

2018 ◽  
Author(s):  
Ye Huang ◽  
Bertrand Guenet ◽  
Philippe Ciais ◽  
Ivan A. Janssens ◽  
Jennifer L. Soong ◽  
...  
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Input Data ◽  
Priming Effect ◽  
Temporal Dynamics ◽  
Litter Input ◽  
Decomposition Models ◽  
Soc Stock ◽  
Global Vegetation ◽  
Soc Decomposition

Abstract. The role of soil microorganisms in regulating soil organic matter (SOM) decomposition is of primary importance in the carbon cycle, and in particular in the context of global change. Modelling soil microbial community dynamics to simulate its impact on soil gaseous carbon (C) emissions and nitrogen (N) mineralization at large spatial scales is a recent research field with the potential to improve predictions of SOM responses to global climate change. We here present a SOM model called ORCHIMIC whose input data that are consistent with those of global vegetation models. The model simulates decomposition of SOM by explicitly accounting for enzyme production and distinguishing three different microbial functional groups: fresh organic matter (FOM) specialists, SOM specialists, and generalists, while implicitly also accounting for microbes that do not produce extracellular enzymes, i.e. cheaters. This ORCHIMIC model and two other organic matter decomposition models, CENTURY (based on first order kinetics and representative for the structure of most current global soil carbon models) and PRIM (with FOM accelerating the decomposition rate of SOM) were calibrated to reproduce the observed respiration fluxes from FOM and SOM and their possible interactions from incubation experiments of Blagodatskaya et al. (2014). Among the three models, ORCHIMIC was the only one that captured well both the temporal dynamics of the respiratory fluxes and the magnitude of the priming effect observed during the incubation experiment. ORCHIMIC also reproduced well the temporal dynamics of microbial biomass. We then applied different idealized changes to the model input data, i.e. a 5 K stepwise increase of temperature and/or a doubling of plant litter inputs. Under 5 K warming, ORCHIMIC predicted a 0.002 K−1 decrease in the C use efficiency (defined as the ratio of C allocated to microbial growth to the sum of C allocated to growth and respiration) and a 3 % loss of SOC. Under the double litter input scenario, ORCHIMIC predicted a doubling of microbial biomass, while SOC stock increased by less than 1 % due to the priming effect. This limited increase in SOC stock contrasted with the proportional increase in SOC stock as modelled by the conventional SOC decomposition model (CENTURY), which cannot reproduce the priming effect. If temperature increased by 5 K and litter input is doubled, the model predicted almost the same loss of SOC as when only temperature was increased. These tests suggest that the responses of SOC stock to warming and increasing input may differ a lot from those simulated by conventional SOC decomposition models, when microbial dynamics is included. The next step is to incorporate the ORCHIMIC model into a global vegetation model to perform simulations for representative sites and future scenarios.

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