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Heston model calibration to option prices and implied volatility

I hope that you are having a great day, I am trying to write a research paper on the Heston model deep calibration. I noticed during my literature review that the most common approach is to calibrate ...
sxminho's user avatar
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0 answers
40 views

Parameters bounds for Heston model calibration

Still working on my master thesis and I have a question I have been looking at for some time but can't find a good reason. I am looking to follow the steps of Horvath et al. (2019) in order to ...
sxminho's user avatar
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1 vote
1 answer
95 views

Deep calibration in the Heston Model

I am doing my master thesis on deep calibration in the Heston Model, and after reading a few academic paper (eg. Horvath et al. 2019) on the subject I understand pretty well the procedure and the ...
sxminho's user avatar
  • 33
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0 answers
78 views

LSTM multivariate Time Series Simulation

I am currently working on a project involving the simulation of multivariate time series (implied-volatilities). To facilitate this process, I am seeking a GitHub repository that provides an ...
BloomShell's user avatar
2 votes
1 answer
112 views

Deep vs "shallow" calibration of option pricing models

I am currently investigating the application of deep learning in calibrating option pricing models, specifically, models of rough volatility, such as rBergomi. While there is a lot of research on ...
dasfobia's user avatar
1 vote
1 answer
95 views

Neural network time series prediction tool [closed]

What are some of the state of the art time series prediction tool with neural network?
Hans's user avatar
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1 vote
1 answer
75 views

Sampling dollar bars for ML model of multiple tickers

I have a Neural Network model that provides predictions for the future returns of a portfolio comprising stocks and cryptocurrencies. The original model operates on standard time bars and generates ...
apt45's user avatar
  • 213
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0 answers
43 views

Implied volatility of Below intrinsic value Heston prices for deep calibraiton

I am trying to generate implied volatility surfaces for European call in the Heston model. I get below intrinsic values for deep ITM, so about 2% of my surface has below intrinsic prices, which doesn'...
girly_quant's user avatar
0 votes
0 answers
108 views

Neural Network to learn Heston Model parameters

I am trying to solve this question: Write down pseudocode to learn a local stochastic volatility for finitely many given option prices: assume a Heston stochastic variance and parametrize local ...
Niko's user avatar
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2 votes
2 answers
2k views

One-day-ahead prediction of S&P500 with Temporal Convolutional Networks

I'm trying to predict the one-day ahead movement of the S&P 500 with Temporal Convolutional Networks 1 to capture some "memory". I use daily close data with the loss function $\mathrm{...
Lejoon's user avatar
  • 147
0 votes
1 answer
66 views

Optimal Input and Target Variables for Forecasting Using a Deep Neural Network on Daily Stock/Index Data [closed]

What is the optimal input and target variables for forecasting with a deep neural network on daily stock/index data? More specifically I’m training a temporal convolutional network, but a more general ...
Lejoon's user avatar
  • 147
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1 answer
99 views

In stock prediction with LSTM, is there a need to get a dataset for a specific time period in order to predict future close price?

I am currently trying to predict the close price of the TSLA stock for March 2022 using LSTM model. Initially, I was using TSLA stock data starting from 2012 to of course March 2022. However, I was ...
CS1999's user avatar
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0 answers
112 views

Good (non-random walk) financial time series to perform forecasting on

I would like to start with a brief caveat, namely that I am by no means a domain expert in financial markets. Therefore the question I am asking may sound silly to a practitioner but I am asking it ...
Mark Fisher's user avatar
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0 answers
97 views

transforming a model to long short instead of long-only

I am currently trying to adapt a model to a long short portfolio strategy. The model is stated here: A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem by Jiang, Xu,...
user101893's user avatar
1 vote
2 answers
257 views

Theoretical and practical drawbacks of using Deep Learning for calibration and pricing

I am investigating the suitability of using deep learning for pricing and calibration for the full implied volatility surface. Such examples of their application are in papers here and here. During ...
Hamish Gibson's user avatar
0 votes
1 answer
677 views

Feature engineering for mid-price prediction - quickly changing features

I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
BGa's user avatar
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3 answers
854 views

Consistent offset/lag in time-series prediction using Neural Network (all code provided)

I'm using a neural network (keras package) to predict Bitcoin prices 48 hours in advance. The issue is that for some reason, my predictions are "correct" but they are lagging behind the true ...
Vladimir Belik's user avatar
2 votes
1 answer
960 views

Why are my Neural Network predictions “correct”, but offset from true value? Not using any past lagged values

Please bear with me through the whole question - I just want to make it very clear what I've done so far and why I'm so perplexed. I am working with a neural network with the Keras package in R, ...
Vladimir Belik's user avatar
4 votes
1 answer
1k views

How to use neural network for technical analysis?

I am working on building a Neural network for technical analysis of stocks. The input I have is the open price and two (so far) technical indicators : RSI and William's R - for the past 2 years. I can ...
Adnan Tamimi's user avatar
3 votes
1 answer
196 views

time series data modeling for deep learning

what is the best format to feed the input data, which are time series with varying density over time, to a deep learning network, while at any iteration we want to feed a batch of data including a ...
fatb's user avatar
  • 31
1 vote
1 answer
499 views

Predicting time series using Jump Diffusion model and Neural Networks

I am trying to understand the difference between using Jump diffusion model and Neural Networks or more precisely LSTM to predict time series data regardless what that data contains for example a ...
Furqan Hashim's user avatar
4 votes
1 answer
163 views

Neural Networks for Estimation of Unmarked Private Asset Returns from Market Data

Let's assume it is March and my illiquid private assets portfolio is only 50% marked for 12/31, but I want to get the most accurate estimate of my final return for the quarter ended on 12/31. What is ...
Alexis Olson's user avatar
9 votes
2 answers
857 views

Defining an objective function for machine learning task of trading

A simplified example. Given: asset's price time series fixed distances to stop and target. A function of these inputs has two possible output values: $1$ if price is likely to hit the target ...
Xpector's user avatar
  • 275
3 votes
1 answer
851 views

Algorithmic Trading: Normalization and Selection of Technical Indicators for Artificial Neural Networks [closed]

I study on algorithmic trading for a while based on technical indicators. I started to learn about neural networks and want to use technical trading indicators in this approach. However, I am not ...
Rıdvan Sözen's user avatar
-2 votes
2 answers
1k views

Least-Squares-Monte-Carlo by Neural Network Estimator for pricing American Option Python [closed]

First I did the LSM (Longstaff-Schwartz) to understand how its work to price an American option. code for standard_normal ...
joey's user avatar
  • 7
3 votes
3 answers
2k views

Appropriate way to normalize Bollinger Bands?

I am playing around with using neural nets to make predictions on market trends. I am currently feeding in a portfolio of historical data of many stocks, and am now implementing several technical ...
KOB's user avatar
  • 193
10 votes
2 answers
354 views

Reference request: Quantitative approaches to market abuse detection

have been asked to look at some financial timeseries for potential suspicious activity. These are stocks (my background fixed income hybrids trading and not forensic analyst...) and most of the ...
Mehness's user avatar
  • 533
1 vote
0 answers
340 views

Probability Integral Transform: Standardisation

I've been applying the probability integral transform as shown here to standardise date for input into a neural network: https://math.stackexchange.com/questions/592076/mapping-cdfs-to-each-other?...
Bazman's user avatar
  • 879
2 votes
1 answer
4k views

When and how to use RNN for stock analysis or trading

I am learning about neural network and created some small networks in feed forwarding network myself. I was curious about Recurrent Neural Networks (RNN) and read some papers about RNN in trading. The ...
Eka's user avatar
  • 647
1 vote
1 answer
810 views

Design models using adjusted or unadjusted stock prices (time series prediction)?

I'm creating a predictive model for closing price of stocks (using neural network and support vector machines.). Is it appropriate to use adjusted prices or unadjusted prices for this prediction ...
user2991243's user avatar
7 votes
2 answers
451 views

Research topics - neural networks and market liquidity

I am a masters student looking for some direction on using neural network on market depth data to help predict market liquidity and bid-ask spreads. Can some of the more experienced people give me ...
user2007598's user avatar
3 votes
1 answer
525 views

Applying Time Delay Neural Network to financial events

I have an IT background and I would like to use data from a forex calendar like this one to predict prices. The problem is that calendar news impacts can last for days or weeks or even can effect ...
MithPaul's user avatar
2 votes
0 answers
49 views

Non-overlapping ranges of HCNN' observables and of state transition function

In the artcicle Forecasting and Trading the High-Low Range of Stocks and ETFs with Neural Networks HCNN is used for forecasting of nine time-series, namely: returns of the lows returns of the highs ...
zer0hedge's user avatar
  • 1,704
1 vote
2 answers
547 views

Feature Selection Effect on Deep Multi-Layer-Perceptron for Financial Applications

I am trying to build a machine learning system for financial price prediction. I am using a 3 layer MLP (a deep network) with 3 outputs (buy,hold,sell). I am using different features such as price ...
guyov's user avatar
  • 27
1 vote
4 answers
2k views

What Is A Good Success Rate Using Machine Learning For A Beginner?

I know this question will be quickly destroyed and my account summarily banned, but I just have to ask: For a trader using machine-learning algorithms (SVMs, ANNs, GAs, Decision Trees) for ...
poorly_built_human's user avatar
1 vote
2 answers
694 views

Machine Learning on matlab 2010

I am trying to develop a trading model. It uses certain technical and fundamental features and the model learns from the past. I have a 3-class output - bullish, neutral and bearish. On trying neural ...
dgmattam's user avatar
4 votes
3 answers
417 views

selecting test data for neural networks

I have been working on a neural network based on certain technical indicators. As people familiar with neural networks would know after developing a hypothesis, the developer is also supposed to ...
user6762's user avatar
1 vote
2 answers
1k views

Howto Calculate An Error's Partial Derivative in ANN

This is a follow-on question from this post I made, "Multilayer Perceptron (Neural Network) for Time Series Prediction", a few months back. I'm constructing a feed-forward artificial neural network, ...
Nutritioustim's user avatar
14 votes
1 answer
8k views

Multilayer Perceptron (Neural Network) for Time Series Prediction

I have it in mind to build a Multilayer Perceptron for predicting financial time series. I understand the algorithm concepts (linear combiner, activation function, etc). But while trying to build the ...
Nutritioustim's user avatar
3 votes
1 answer
536 views

Econometric vs ANN models for forecast?

I hope this is an appropriate question for this forum... for me it is an obvious query since it intrigues me for a long time. Ok, assume there are 2 distinct classes of models: econometric (AR, MA, ...
DBS's user avatar
  • 141