Forecast Interbank Payment Flow in Large Value Payment System

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19 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

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Forecast Interbank Payment Flow in


Large Value Payment System


Problem Provider: The People

s Bank of China

2008 Canada
-
China Problem Solving Workshop in Finance

Weihai, China, October 2008

Problem Solving Report

Problem

Background


Large Value Payment System (LVPS)


——

a major payment infrastructure in China


Participants: most of the banking institutes


Amount: over 90% of payment



Liquidity risk of LVPS


Closing time of money market: 4:00PM


Closing time of LVPS: 5:00PM


Participants: delay settlement for the reserve
requirement


Risk: system traffic for too many transactions;


shortage of liquidity of a participant



PBC should monitor and remind the participants

Problems


How to forecast the interbank payment
flow in LVPS



Analyze the statistical property of each
commercial bank

s payment flow


Build models based on bank

s own payment
flow


Forecast results should have higher
accuracy



Solving Group


Pr. Zhongxing Ye Shanghai Jiao Tong University


Pr. Yong Zhou Chinese Academy of Science


Pr. Gang Wei Shandong University


Pr. Zhijun Zhao Chinese Academy of Social Sciences


Dr. Ying Bao Industrial and Commercial Bank of China


Min Zhang Chinese Academy of Science


Chen Su/Jing Zhao/Liping Zhu Shandong University

3 days hard working!

Solving report

Outline



Property of Payment Flow



Statistic Models


Wavelet Neural Network Model


ARIMA
-
GARCH Model


Additive Time Series Model



Further Research

Property of Payment Flow

1

Two stages

Reason: Several times of the reserve requirement


ratio adjustment since July,2006

Object: number of daily paid
-
out transactions

Property of Payment Flow

2

Yearly Effect

Reason: New year is the end of a settlement year;

Springing Festival is the real end of a year in China

Property of Payment Flow

3

Monthly Effect

Reason: Bank checks the performance of the


employee at the end of the month

Property of Payment Flow

4

Weekly Effect

Fact: Employee always works hard at Tuesday

Statistic Models



Wavelet Neural Network Model



ARIMA
-
GARCH Model



Additive Time Series Model


Wavelet Neural Network


Artificial Neural Network



is a new computing technology. It has been widely used
because of its flexible model and less data requirement. ANN
is composed of interconnected units or artificial neurons.



Wavelet Analysis



is a kind of method breaking down data at different frequency
and combines time
-
domain analysis and frequency
-
domain
analysis together to dig more information.



Wavelet neutral network



combines ANN and wavelet analysis. It processes both self
-
learning ability of ANN and local property of wavelet
transformation and has higher approaching ability, faster
convergence speed and better forecast results.



Wavelet Neural Network


Fitting and Forecast:

ARIMA
-
GARCH Model


ARMA model

for stationary series





ARIMA model

for nonstationary series

covert nonstationary data to stationary by difference


GARCH model

for conditional eteroskedasticity
series



Time Series Model



Forecast :

Additive Time Series Model

Build model in every month. One day in a month:

Simple Form:

where

Additive Time Series Model

Additive Time Series Model

For month:

LSE:

Additive Time Series Model

Different deta for different week:

Additive Time Series Model

Using average deta:

Additive Time Series Model

Seasonal Effect:

Additive Time Series Model

Trending Effect:

Additive Time Series Model

Fitting:

Additive Time Series Model

Forecast:

Further Research

Forecast:



Number of paid
-
in transactions



Amount of paid
-
out transactions



Amount of paid
-
in transactions





Thanks !