Meta-learning in finance: boosting models calibration with deep learning

The Calibration of Stochastic-Local Volatility Models — An Inverse Problem Perspective

Why meta-learning?

Financial model calibration

Schematic financial model calibration

Machine learning for model calibration

Optimization problem we aim to approximate
def dense_bn_block(inn, size):
x = Dense(size, activation='linear')(inn)
x = BatchNormalization()(x)
x = Activation('relu')(x)
return x
def residual_block(inn, size):
x = dense_bn_block(inn, size)
x = add([inn, x])
return x
inputs = Input(shape=(295, ))
x = GaussianNoise(0.05)(inputs)
x = BatchNormalization()(x)
for i in range(1, 5):
x = residual_block(x, 64)
x = Dropout(0.25)(x)
predictions = Dense(1, activation='linear')(x)
model = Model(inputs=inputs, outputs=predictions)

Understanding results

Time and mean absolute error for differential evolution and neural network
Black-Scholes model calibration results in terms of MAE
Merton jump model calibration results in terms of MAE
Option chain variables interpretation


Co-founder of consulting firm Neurons Lab and advisor to AI products builders. On Medium, I write about proven strategies for achieving ML technology leadership

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