[Qraft] efficient fx hedge with bayesian deep learning joohyunjo

Copyrightⓒ. Saebyeol Yu. All Rights Reserved.
EFFICIENT FX HEDGE
WITH BAYESIAN DEEP LEARNING
베이지안 딥러닝을 활용한 외환 헷지 전략
조 주현
QRAFT TECHNOLOGIES, INC.
SEP 09 2019

1 Trade Dependence(% GDP)
27%
35%
83%
38%
World Bank(2018)
51%
29%
66%

Main Business
Risk
FX
Risk
Risk out of
Main Business
Firms with export/import or foreign business bare
risk from both Main Business and Foreign Exchange
2 Risk of Foreign Business
- Easy to Manage
- Many Experts
- Predictable
- Hard to Manage
- Few Experts
- Unpredictable

Purpose of FX Hedge
Subtask
Basic purpose of Hedge is to dodge shock
from sector other than main business
Risk Averse Action
Profit is not the primary target of Hedge.
Maintaining the value of main operation is.
3

Value-Based Hedge Plan4
1$ = 1,000₩
1,000₩ 1$
1$ = 1,100₩
No-Hedge 1,100₩ 1$
Full-Hedge 1,000₩ 0.91$
1$ = 900₩
No-Hedge 900₩ 1$
Full-Hedge 1,000₩ 1.11$
Fixed Number
But Same Value?

100%
?%
0%
Full-Hedge
Value - Based
Optimal Hedge Plan
No-Hedge
4 Value-Based Hedge Plan
Bet on Dollar High
(Dollar-Selling Case)
Bet on Dollar Low
(Dolalr-Selling Case)

Deterministic
Perfect Prediction
Model
Deterministic Prediction Model is easiest way to solve FX problems(if possible)
But it has critical issues such as
1. Overconfident Prediction
2. Overfitting
3. Local Minima
5 Deterministic Prediction Model
-0.003
-0.002
-0.001
0
0.001
0.002
0.003
0.004
0.005
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DETERMINISTIC PREDICTION

6 Task-Specific Approach
Risk Management
- Stability First, Return Next
- Risk Averse Strategy
ROI Maximization
- High frequency trading
- Maximize return under fixed risk
- Minimize risk under fixed return
Optimal Portfolio Making
- Choose Universe by certain concept
- Adjust weights to achieve best performance
under predefined universe

Distribution
Reparameterization
Sampling with
Stochastic weights
Kernel
Replacement
7 Bayesian Uncertainty Prediction
With DeepLearning
Methodology
GPDNN
Deep Ensemble
Gaussian Mixture Approximate
MC Dropout
Flipout

Stochastic
Risk-Aware Prediction
Model
Stochastic Risk-Aware Prediction Model has advantages on
1. Calculating uncertainty of prediction which can be directly used on hedge
strategy
2. Overfitting prevention
8 Stochastic Prediction Model
-0.015
-0.01
-0.005
0
0.005
0.01
STOCHASTIC PREDICTION MODEL

Stochastic
Risk-Aware Prediction
Model
Sample strategies
1. Take risk on low uncertainty area,
copy benchmark action on high-uncertainty area
2. Calculate VaR and control hedging proportion
8 Stochastic Prediction Model
-0.015
-0.01
-0.005
0
0.005
0.01
STOCHASTIC PREDICTION MODEL
Low Uncertainty Area
High Uncertainty Area

9 Advantage of
Bayesian Uncertainty Prediction
-2000
0
2000
4000
6000
8000
10000
2015-01-02
2015-02-02
2015-03-02
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stochastic deterministic1 deterministic2
Stochastic Deterministic1 Deterministic2
CAGR 14.68% 8.63% 5.20%
MDD(₩) -6.06% -7.93% -9.63%
Dynamic Full-Hedge Simulation (Dollar Futures)
Constraint: 70% ~ 130% Hedge Ratio with Transaction Fee
Stochastic : learned with stochastic network, use stochastic values
Deterministic1 : learned with stochastic network, use only deterministic value
Deterministic2 : learned with deterministic network, use only deterministic value

10 Multi-Task Learning
USD/KRW JPY/KRW CNY/KRW ECON FINANCE ENERGY
MON
TUE
WED
THU
FRI
Input Data
Output Label
Base
Model
Backpropagation

10
Multiple Currency Management / derivative Making
USD/KRW JPY/KRW CNY/KRW ECON FINANCE ENERGY
MON
TUE
WED
THU
Input Data
Output Labels
FRI
General Model
Specialized
Submodels
General
Feature Extraction
Backpropagation
Multi-Task Learning

11 Hyperparameter Optimization

News / Twitter
Analysis
Pretrainable
Network
Attention
Network
Factor
Analysis
Ensemble
Modelling
12 Other Experiments

Value-Based
Dynamic
Hedging
FOREX ETF
FOREX
on
Cloud Service
Risk Averse Hedging solution Following FX Trend
with α
Easily accessible
DeepLearning cloud for
Foreign Exchange
13 Applications

Financial
Deep Learning
- Low quality, Finite Data
- Small data processing technique
- Domain-Driven growth
14 Conclusion
General
Deep Learning
- High-quality, Infinite Data
- Large data processing technique
- Data-Driven growth

THANK YOU

[Qraft] efficient fx hedge with bayesian deep learning joohyunjo

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