224 research outputs found
ESTIMATION OF IMPLIED VOLATILITY SURFACE AND ITS DYNAMICS: EVIDENCE FROM S&P 500 INDEX OPTION IN POST-FINANCIAL CRISIS MARKET
There is now an extensive literature on modeling the implied volatility surface (IVS) as a function of options’ strike prices and time to maturity. The polynomial parameterization is one of these approaches and it provides a simple and efficient way for practitioners to estimate implied volatility. This project tests the predictive capability of this methodology in the post-financial crisis market. Using data for the period from July 1st, 2012 to June 30th, 2015 for European puts and calls of the S&P 500 index options, we estimate a vector autoregressive model to capture the dynamics of the IVS. Our results show that this methodology has better predictive capability on IVS of index options in post-financial crisis market than on IVS of equity options in pre-financial crisis period
Macro action selection with deep reinforcement learning in StarCraft
StarCraft (SC) is one of the most popular and successful Real Time Strategy
(RTS) games. In recent years, SC is also widely accepted as a challenging
testbed for AI research because of its enormous state space, partially observed
information, multi-agent collaboration, and so on. With the help of annual
AIIDE and CIG competitions, a growing number of SC bots are proposed and
continuously improved. However, a large gap remains between the top-level bot
and the professional human player. One vital reason is that current SC bots
mainly rely on predefined rules to select macro actions during their games.
These rules are not scalable and efficient enough to cope with the enormous yet
partially observed state space in the game. In this paper, we propose a deep
reinforcement learning (DRL) framework to improve the selection of macro
actions. Our framework is based on the combination of the Ape-X DQN and the
Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as
LastOrder. Our evaluation, based on training against all bots from the AIIDE
2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning
rate, outperforming 26 bots in total 28 entrants
Adoption and implication of the Biased-Annotator Competence Estimation (BACE) model into COVID-19 vaccine Twitter data: Human annotation for latent message features
Traditional quantitative content analysis approach (human coding method) has
weaknesses, such as assuming all human coders are equally accurate once the
intercoder reliability for training reaches a threshold score. We applied the
Biased-Annotator Competence Estimation (BACE) model (Tyler, 2021), which draws
on Bayesian modeling to improve human coding. An important contribution of this
model is it takes each coder's potential biases and reliability into
consideration and treats the "true" label of each message as a latent
parameter, with quantifiable estimation uncertainties. In contrast, in
conventional human coding, each message will receive a fixed label without
estimates for measurement uncertainties. In this extended abstract, we first
summarize the weaknesses of conventional human coding; and then apply the BACE
model to COVID-19 vaccine Twitter data and compare BACE with other statistical
models; finally, we discuss how the BACE model can be applied to improve human
coding of latent message features
Diversity is Strength: Mastering Football Full Game with Interactive Reinforcement Learning of Multiple AIs
Training AI with strong and rich strategies in multi-agent environments
remains an important research topic in Deep Reinforcement Learning (DRL). The
AI's strength is closely related to its diversity of strategies, and this
relationship can guide us to train AI with both strong and rich strategies. To
prove this point, we propose Diversity is Strength (DIS), a novel DRL training
framework that can simultaneously train multiple kinds of AIs. These AIs are
linked through an interconnected history model pool structure, which enhances
their capabilities and strategy diversities. We also design a model evaluation
and screening scheme to select the best models to enrich the model pool and
obtain the final AI. The proposed training method provides diverse,
generalizable, and strong AI strategies without using human data. We tested our
method in an AI competition based on Google Research Football (GRF) and won the
5v5 and 11v11 tracks. The method enables a GRF AI to have a high level on both
5v5 and 11v11 tracks for the first time, which are under complex multi-agent
environments. The behavior analysis shows that the trained AI has rich
strategies, and the ablation experiments proved that the designed modules
benefit the training process
Macro action selection with deep reinforcement learning in StarCraft
StarCraft (SC) is one of the most popular and successful Real Time Strategy
(RTS) games. In recent years, SC is also considered as a testbed for AI
research, due to its enormous state space, hidden information, multi-agent
collaboration and so on. Thanks to the annual AIIDE and CIG competitions, a
growing number of bots are proposed and being continuously improved. However, a
big gap still remains between the top bot and the professional human players.
One vital reason is that current bots mainly rely on predefined rules to
perform macro actions. These rules are not scalable and efficient enough to
cope with the large but partially observed macro state space in SC. In this
paper, we propose a DRL based framework to do macro action selection. Our
framework combines the reinforcement learning approach Ape-X DQN with
Long-Short-Term-Memory (LSTM) to improve the macro action selection in bot. We
evaluate our bot, named as LastOrder, on the AIIDE 2017 StarCraft AI
competition bots set. Our bot achieves overall 83% win-rate, outperforming 26
bots in total 28 entrants
How roughness and thermal properties of a solid substrate determine the Leidenfrost temperature:Experiments and a model
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