12,938 research outputs found
Introducing the -Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting
This paper introduces the -Cell, a novel Recurrent Neural Network
(RNN) architecture for financial volatility modeling. Bridging traditional
econometric approaches like GARCH with deep learning, the -Cell
incorporates stochastic layers and time-varying parameters to capture dynamic
volatility patterns. Our model serves as a generative network, approximating
the conditional distribution of latent variables. We employ a
log-likelihood-based loss function and a specialized activation function to
enhance performance. Experimental results demonstrate superior forecasting
accuracy compared to traditional GARCH and Stochastic Volatility models, making
the next step in integrating domain knowledge with neural networks
Deep calibration of rough stochastic volatility models
Sparked by Alòs, León und Vives (2007); Fukasawa (2011, 2017); Gatheral, Jaisson und Rosenbaum
(2018), so-called rough stochastic volatility models such as the rough Bergomi model by
Bayer, Friz und Gatheral (2016) constitute the latest evolution in option price modeling. Unlike
standard bivariate diffusion models such as Heston (1993), these non-Markovian models with
fractional volatility drivers allow to parsimoniously recover key stylized facts of market implied
volatility surfaces such as the exploding power-law behaviour of the at-the-money volatility skew
as time to maturity goes to zero. Standard model calibration routines rely on the repetitive evaluation
of the map from model parameters to Black-Scholes implied volatility, rendering calibration
of many (rough) stochastic volatility models prohibitively expensive since there the map can often
only be approximated by costly Monte Carlo (MC) simulations (Bennedsen, Lunde & Pakkanen,
2017; McCrickerd & Pakkanen, 2018; Bayer et al., 2016; Horvath, Jacquier & Muguruza, 2017).
As a remedy, we propose to combine a standard Levenberg-Marquardt calibration routine with
neural network regression, replacing expensive MC simulations with cheap forward runs of a neural
network trained to approximate the implied volatility map. Numerical experiments confirm the
high accuracy and speed of our approach
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed
to calibrate financial asset price models using an Artificial Neural Network
(ANN). Determining optimal values of the model parameters is formulated as
training hidden neurons within a machine learning framework, based on available
financial option prices. The framework consists of two parts: a forward pass in
which we train the weights of the ANN off-line, valuing options under many
different asset model parameter settings; and a backward pass, in which we
evaluate the trained ANN-solver on-line, aiming to find the weights of the
neurons in the input layer. The rapid on-line learning of implied volatility by
ANNs, in combination with the use of an adapted parallel global optimization
method, tackles the computation bottleneck and provides a fast and reliable
technique for calibrating model parameters while avoiding, as much as possible,
getting stuck in local minima. Numerical experiments confirm that this
machine-learning framework can be employed to calibrate parameters of
high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table
The influence of the noradrenergic system on optimal control of neural plasticity
Decision making under uncertainty is challenging for any autonomous agent. The challenge increases when the environment’s stochastic properties change over time, i.e., when the environment is volatile. In order to efficiently adapt to volatile environments, agents must primarily rely on recent outcomes to quickly change their decision strategies; in other words, they need to increase their knowledge plasticity. On the contrary, in stable environments, knowledge stability must be preferred to preserve useful information against noise. Here we propose that in mammalian brain, the locus coeruleus (LC) is one of the nuclei involved in volatility estimation and in the subsequent control of neural plasticity. During a reinforcement learning task, LC activation, measured by means of pupil diameter, coded both for environmental volatility and learning rate. We hypothesize that LC could be responsible, through norepinephrinic modulation, for adaptations to optimize decision making in volatile environments. We also suggest a computational model on the interaction between the anterior cingulate cortex (ACC) and LC for volatility estimation
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
In this paper we develop a novel neural network model for predicting implied
volatility surface. Prior financial domain knowledge is taken into account. A
new activation function that incorporates volatility smile is proposed, which
is used for the hidden nodes that process the underlying asset price. In
addition, financial conditions, such as the absence of arbitrage, the
boundaries and the asymptotic slope, are embedded into the loss function. This
is one of the very first studies which discuss a methodological framework that
incorporates prior financial domain knowledge into neural network architecture
design and model training. The proposed model outperforms the benchmarked
models with the option data on the S&P 500 index over 20 years. More
importantly, the domain knowledge is satisfied empirically, showing the model
is consistent with the existing financial theories and conditions related to
implied volatility surface.Comment: 8 pages, SIGKDD 202
- …