scholarly journals NONLINEAR RELATIONSHIPS AND VOLATILITY SPILLOVERS AMONG HOUSE PRICES, INTEREST RATES AND STOCK MARKET PRICES

2016 ◽  
Vol 20 (4) ◽  
pp. 371-383 ◽  
Author(s):  
Hsiang-Hsi LIU ◽  
Sheng-Hung CHEN
Keyword(s):  
Stock Market ◽  
Interest Rates ◽  
Goodness Of Fit ◽  
House Prices ◽  
House Price ◽  
Smooth Transition ◽  
Market Returns ◽  
Market Prices ◽  
Stock Market Returns ◽  
Volatility Spillovers

This paper addresses the interaction between interest rates and the significant increases in both Taiwanese house and stock market prices seen in recent years. Changes in house prices impact banks’ nonperforming loans, whereas changes in interest rates directly influence the ability of individuals and businesses to pay loan interest, accentuating the co-movements between house and stock mar-ket prices. We investigate the nonlinear relations and volatility spillovers among house prices, interest rates and stock market prices using monthly data from January 1985 to March 2009 for Taiwan. We find that the Smooth Transition Vector Error Correction GARCH (STVEC-GARCH) model has the best forecasting ability based on goodness of fit tests while showing a nonlinear and co-integrated relation among the three variables. Specifically, house price leads stock market returns when the interest rate is led by either house price or stock market returns. The volatility of stock market returns has significant impacts on interest rates, implying that borrowers should be aware of stock market fluctuations and thus strengthen their risk management because of unexpected changes.

THE INFLUENCES OF STOCK MARKET ON EXCHANGE-TRADED FUND UNDER THE THRESHOLD EFFECT OF MONETARY POLICY IN CHINA

2021 ◽  
pp. 1-21
Author(s):  
Tzu-Yi Yang ◽  
Phan Van Hung ◽  
Chia-Jui Chang ◽  
Nguyen Phuc Nguyen
Keyword(s):  
Monetary Policy ◽  
Stock Market ◽  
Threshold Effect ◽  
Smooth Transition ◽  
Exchange Traded Funds ◽  
Market Returns ◽  
Stock Market Returns ◽  
Market Variables ◽  
Effect Of Monetary Policy ◽  
Exchange Traded Fund

This paper estimates the smooth transition autoregressive model with exogenous variables to evaluate the effects of stock market returns on the exchange-traded funds’ (ETFs) returns in China with reserve requirement ratio (RRR) from monetary policy as a transition variable. The sample used in this study lasts from March 4, 2005 to June 30, 2017. The empirical result points out that there is the effect of RRR value on the relationship between stock market returns and ETF return. Moreover, these effects are variable depending on the conversion and its changes over time in different variations of threshold intervals. Lastly, the larger the change of China’s stock market variables’ lag period, the smaller the impacts on Chinese ETF return.

scholarly journals Co-movements in stock market returns, Ireland and London 1869–1929

2017 ◽  
Vol 24 (2) ◽  
pp. 167-184 ◽  
Author(s):  
Rebecca Stuart
Keyword(s):  
Stock Market ◽  
Capital Gains ◽  
Free State ◽  
Time Varying ◽  
Market Returns ◽  
Stock Market Returns ◽  
Monthly Data ◽  
Volatility Spillovers ◽  
Bivariate Garch Model ◽  
The Relationship

This article studies the relationship between the Irish and London stock markets over the period 1869 to 1929, using monthly data on capital gains. A bivariate GARCH model shows that there were significant volatility spillovers from the London to the Irish market, but not vice versa. This suggests that shocks originating in London were transmitted to Ireland, but that the reverse did not occur. Furthermore, the time-varying correlation indicates that the co-movement between London and Ireland declined during the Irish independence struggle and the establishment of the Irish Free State. The correlation appears to stabilise in the late 1920s.

scholarly journals Hybridization of Bat Algorithm with XGBOOST Model for Precise Prediction of Stock Market Directions

2020 ◽  
Vol 9 (3) ◽  
pp. 3375-3382
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Bat Algorithm ◽  
Market Returns ◽  
Market Prices ◽  
Stock Market Returns ◽  
Proposed Model ◽  
Wide Range ◽  
Precise Prediction ◽  
Stock Values

In recent days, prediction of stock market returns is generally treated as a forecasting problem. The implicit volatile nature of stock market across the world makes the prediction process highly challenging. As a result, prediction and diffusion modeling undermine a wide range of issues present in the stock market prediction. The minimization in prediction error will greatly minimize the investment risks. This paper presents a new method to determine the direction of stock market variations indicating gain and loss. A new machine learning ML based model is applied to predict the direction of stock market prices. The presented model undergoes preprocessing, feature extraction and classification. Initially, preprocessing takes place using exponential smoothing. Then, required features are extracted from the preprocessed dataset. Afterwards, an effective Bat algorithm (BA) with the XGBoost model called BA-XGB is applied for forecasting the stock prices in market. The proposed model predicts whether the stock values gets increased or decreased based on the price existing n days in advance. The presented model is experimented using Apple (APPL) and Facebook (FB) stocks. The obtained simulation outcome stated that the BA-XGB model has offered superior outcome by achieving a maximum accuracy of 96.42.

Oil prices, stock market returns and volatility spillovers: Evidence from Turkey

2020 ◽  
Vol 42 (3) ◽  
pp. 597-614 ◽  
Author(s):  
Nuket Kirci Cevik ◽  
Emrah I. Cevik ◽  
Sel Dibooglu
Keyword(s):  
Stock Market ◽  
Oil Prices ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Spillovers

scholarly journals Time Series Analysis of Interest Rates Volatility and Stock Returns in Ghana

2019 ◽  
Vol 5 (2) ◽  
pp. 89-102
Author(s):  
Johnson Worlanyo Ahiadorme ◽  
Emmanuel Sonyo ◽  
Godwin Ahiase
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Stock Market ◽  
Time Series Analysis ◽  
Interest Rates ◽  
Market Returns ◽  
Stock Market Returns ◽  
Series Analysis ◽  
Long Run ◽  
Short Run

The study utilized time series analysis models and employed the Johansen’s cointegration procedure and the vector error correction model to examine the short-run and long-run dynamics of the relationship between interest rates and stock market returns. The results of this study show that contrary to popular evidence from extant research, interest rate changes positively and significantly affect stock market returns in the long run and the deviation from the long-run equilibrium is corrected each period following a shock to the stock market in the short run. The positive linkages between interest rate changes and stock market outturns may be explained by the relative strength of banking stocks on the Ghana Stock Exchange. The analysis shows that as the long-run equilibrium is approached, the deviations in the short term decrease significantly.

Sign in / Sign up

Export Citation Format

Share Document