4,498 research outputs found
Extending the feature set of a data-driven artificial neural network model of pricing financial option
Prices of derivative contracts, such as options, traded in the financial markets are expected to have complex relationships to fluctuations in the values of the underlying assets, the time to maturity and type of exercise of the contracts as well as other macroeconomic variables. Hutchinson, Lo and Poggio showed in 1994 that a non-parametric artificial neural network may be trained to approximate this complex functional relationship. Here, we consider this model with additional inputs relevant to the pricing of options and showthat the accuracy of approximation may indeed be improved. We consider volume traded, historic volatility, observed interest rates and combinations of these as additional features. In addition to giving empirical results on how the inclusion of these variables helps predicting option prices, we also analyse prediction errors of the different models with volatility and volume traded as inputs, and report an interesting correlation between their contributions
Model-Independent Estimation of Optimal Hedging Strategies with Deep Neural Networks
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedging and pricing of financial derivatives with neural networks. We utilize the concept of convex risk measures to define optimal hedging strategies without strong assumptions on the underlying market dynamics. Furthermore, the setting allows the incorporation of market frictions and thus the determination of optimal hedging strategies and prices even in incomplete markets. We then use the approximation capabilities of neural networks to find close-to optimal estimates for these strategies.
We will elaborate on the theoretical foundations of this approach and carry out implementations and a detailed analysis of the method with simulated market data.
Our experiments show that the neural network-based algorithm is a powerful tool for the model-independent pricing of any financial derivative and the estimation of optimal hedging strategies for these instruments
Model-Independent Estimation of Optimal Hedging Strategies with Deep Neural Networks
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedging and pricing of financial derivatives with neural networks. We utilize the concept of convex risk measures to define optimal hedging strategies without strong assumptions on the underlying market dynamics. Furthermore, the setting allows the incorporation of market frictions and thus the determination of optimal hedging strategies and prices even in incomplete markets. We then use the approximation capabilities of neural networks to find close-to optimal estimates for these strategies.
We will elaborate on the theoretical foundations of this approach and carry out implementations and a detailed analysis of the method with simulated market data.
Our experiments show that the neural network-based algorithm is a powerful tool for the model-independent pricing of any financial derivative and the estimation of optimal hedging strategies for these instruments
Volatility forecasting with garch models and recurrent neural networks
The three main ways to estimate future volatilities include the implied volatility of option
prices, time-series volatility models, and neural network models. This project investigates
whether there are economically meaningful differences between those approaches. Seminal
time-series models like the GARCH, as well as recurrent neural network models like the LSTM
are investigated to forecast volatilities. An eventual informational advantage over the market’s
expectation of future volatility in the form of implied volatility is sought after. Through trading
strategies involving options, as well as investment vehicles that emulate the VIX, it is attempted
to trade volatility in a profitable way
Sequential Design for Ranking Response Surfaces
We propose and analyze sequential design methods for the problem of ranking
several response surfaces. Namely, given response surfaces over a
continuous input space , the aim is to efficiently find the index of
the minimal response across the entire . The response surfaces are not
known and have to be noisily sampled one-at-a-time. This setting is motivated
by stochastic control applications and requires joint experimental design both
in space and response-index dimensions. To generate sequential design
heuristics we investigate stepwise uncertainty reduction approaches, as well as
sampling based on posterior classification complexity. We also make connections
between our continuous-input formulation and the discrete framework of pure
regret in multi-armed bandits. To model the response surfaces we utilize
kriging surrogates. Several numerical examples using both synthetic data and an
epidemics control problem are provided to illustrate our approach and the
efficacy of respective adaptive designs.Comment: 26 pages, 7 figures (updated several sections and figures
Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models
The purpose of this paper is to improve the accuracy of dynamic hedging using
implied volatilities generated by genetic programming. Using real data from
S&P500 index options, the genetic programming's ability to forecast Black and
Scholes implied volatility is compared between static and dynamic
training-subset selection methods. The performance of the best generated GP
implied volatilities is tested in dynamic hedging and compared with
Black-Scholes model. Based on MSE total, the dynamic training of GP yields
better results than those obtained from static training with fixed samples.
According to hedging errors, the GP model is more accurate almost in all
hedging strategies than the BS model, particularly for in-the-money call
options and at-the-money put options.Comment: 32 pages,13 figures, Intech Open Scienc
構造化データに対する予測手法:グラフ,順序,時系列
京都大学新制・課程博士博士(情報学)甲第23439号情博第769号新制||情||131(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 阿久津 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA
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