scholarly journals International Real Estate Review

2009 ◽  
Vol 12 (3) ◽  
pp. 221-251
Author(s):  
Wei-han Liu ◽  
◽  
Zhefang Zhou ◽  
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Inflation Rate ◽  
Forecast Error ◽  
Real Estate Market ◽  
Short Term ◽  
Forecast Horizon ◽  
Vector Autoregressive ◽  
Long Run ◽  
Inflation Hedging

This paper examines the inflation-hedging behavior of the Hong Kong securitized real estate market between April 1986 and April 2007. The monthly series of the Hang Seng Property Index (HSPI) is selected as the proxy of the Hong Kong securitized real estate market due to its comprehensive coverage and availability of rich data. We find that the vector autoregressive forecast error method, which is introduced by Den Haan (2000), outperforms the traditional linear vector autoregressive model and vector error correction model techniques in depicting the comovement between the HSPI and inflation rate. The comovement estimates show a positive correlation between the HSPI and inflation rate in the short-term and a negative correlation in the long term which indicates that the Hong Kong securitized real estate market can serve as an inflation hedge in the short term, but becomes a perverse inflation hedge in the long run. This inflation-hedging pattern differs from those of its neighboring major East Asian markets. This study demonstrates that the inflation-hedging capability of securitized real estate is not a static issue, but rather, depends on the length of the forecast horizon.

scholarly journals The Relationship Between Macroeconomy and Asset Prices: Long Run Causality Evidence From Lithuania

2017 ◽  
Vol 8 (1) ◽  
pp. 63-85 ◽  
Author(s):  
Linas Jurkšas ◽  
Arvydas Paškevičius
Keyword(s):  
Real Estate ◽  
Asset Prices ◽  
Consumer Price Index ◽  
Real Estate Market ◽  
Forecast Horizon ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Long Run ◽  
Macroeconomic Indicators ◽  
Lag Model

The purpose of this paper is to determine the long-run causal impact of various economic factors on Lithuanian stock, government securities and real estate prices, and to assess how accurately future asset returns can be forecasted based solely on economic information. Five macroeconomic indicators, namely, gross domestic product (GDP), foreign direct investment (FDI), consumer price index (CPI), money supply (MS) and Vilnius interbank offered rate (VILIBOR), were included in the model. The results of the created autoregressive distributed lag model (ARDL) revealed that a long-run causal relationship between Lithuanian assets and macroeconomic variables exists and that changing values of these indicators explain about half of the variability of assets’ returns. The results of ARDL model forecast showed that the most precise predictions are obtainable in real estate market, while forecasted returns of stock and government securities are not so accurate, especially the further forecast horizon. The possibility to understand driving factors behind changes of asset prices and to predict future return is of a particular importance not only for investors and businessmen, but also for the policy makers who are responsible for making substantiated decisions regarding monetary, macroprudential and fiscal policies they conduct.

scholarly journals Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

2021 ◽  
pp. 1-21
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Inflation Rate ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Impulse Response Functions ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Long Run ◽  
The Us

Abstract Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

scholarly journals Tax Cuts and Fee Reductions and Labor Demand of SMEs

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Suwei Xiao
Keyword(s):  
Labor Demand ◽  
Life Cycles ◽  
Economic Downturn ◽  
Short Term ◽  
Vector Autoregressive ◽  
Long Run ◽  
Employment Fluctuations ◽  
Important Means ◽  
Stable Employment ◽  
Tax Cuts

<p>Policies to cut taxes and fees are important means to deal with the economic downturn, which strongly support to the development of the majority of small and medium-sized enterprises (SMEs). The current study has no consistent conclusion of whether SMEs expand their labor demand because of this. This paper builds a structural vector autoregressive (VAR) model to analyze the dynamic effects of tax cuts and fee reduction policies on increasing labor demand for SMEs. The empirical results show that tax cuts and fee reductions are important causes of short-term employment fluctuations, but in the long run, it is difficult for taxation policies to have a direct positive effect on employment. Therefore, this article puts forward the idea that different tax incentives can be formulated for small and medium-sized enterprises in the short term according to their life cycles. In the long run, the focus of macro-control needs to be turned to supply management to achieve the goal of stable employment.</p>

scholarly journals Association between Securities and Real Estate Markets: The Case of Ho Chi Minh City

2016 ◽  
Vol 23 (04) ◽  
pp. 62-79
Author(s):  
Nguyet Phan Thi Bich ◽  
Thao Pham Duong Phuong
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Securities Market ◽  
Ho Chi Minh City ◽  
Real Estate Markets ◽  
Investment Control ◽  
Expert Opinions ◽  
Long Run ◽  
Property Price ◽  
Comprehensive Survey

This study inspects the relationship between the securities market and real estate market in Vietnam, particularly the case of Ho Chi Minh City from Q1/2009 through Q3/2014. Using a comprehensive survey of expert opinions, we find that several macro factors including GDP, interest rate, inflation, fiscal policy, monetary policy, securities market regulations, international capital flows, and money market have effects on both the securities and real estate markets, which, in turn, do have mutual interactions. Furthermore, it is suggested by the survey results that among the determinants, policy on foreign investment control has the most powerful impact on capital movements between the two markets. The results of TECM analysis of property price index and VN-Index reveal a bidirectional causality between the two markets, which are positively related in the long run

scholarly journals International Real Estate Review

2017 ◽  
Vol 20 (4) ◽  
pp. 417-450
Author(s):  
Kim Hin Ho ◽  
◽  
Satyanarain Rengarajan ◽  
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Future Expectations ◽  
Public And Private ◽  
Vector Autoregressive ◽  
Macroeconomic Conditions ◽  
Knowledge Based ◽  
Knowledge Intensive ◽  
Using Data ◽  
Industrial Real Estate

he behavioural structure of large and strategic industrial real estate accommodation does not exist in a vacuum. Instead, its fundamental investment values and yields are uniquely affected through the dynamic interaction among exogenous and endogenous forces related to the industrial real estate demand-supply conditions, macroeconomic and institutional polices as well as urban industrial plans. This study aims to understand the dynamic behaviour of the industrial real estate market in Singapore that is slowly transitioning from a capital intensive to knowledge intensive economy. Using data obtained from various sources between 2001Q4-2010Q2 which essentially capture three property cycles, we incorporate a vector autoregressive (VAR) approach to holistically model the industrial real estate market in Singapore with respect to its demand-supply conditions, market capitalization rates which encompass information about rental yields, capital values along with future expectations. This study will help policy makers and developers to understand the structure of the industrial real estate market in Singapore along with respect to its macroeconomic conditions. The results are insightful as the data capture both the public and private markets along with a new hi-tech industrial accommodation (science parks), which is slowly gaining prominence as of the turn at the 21st century as Singapore strives to steer towards a knowledge based industrial economy.

Feedback trading strategies in international real estate markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chrysanthi Balomenou ◽  
Vassilios Babalos ◽  
Dimitrios Vortelinos ◽  
Athanasios Koulakiotis
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Short Term ◽  
Content Type ◽  
Real Estate Markets ◽  
The Real Estate ◽  
The Uk ◽  
The Impact ◽  
Feedback Trading

Purpose Motivated by recent evidence that securitized real estate returns exhibit higher levels of predictability than stock market returns and that feedback trading (FT) can induce returns autocorrelation and market volatility, the purpose of this study is to examine the impact of FT strategies on long-term market volatility of eight international real estate markets (UK, Germany, France, Italy, Sweden, Australia, Japan and Hong Kong). Design/methodology/approach Assuming that the return autocorrelation may vary over time and the impact of positive feedback trading (PFT) or negative feedback trading (NFT) could be a function of return volatility, the authors use a combination of a FT model and a fractionally integrated Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model. Findings The results are mixed, revealing that both PFT and NFT strategies persist. Specifically, the authors detect PFT in the real estate markets of France, Hong Kong and Italy as opposed to the real estate markets of Australia, Germany, Japan and Sweden where NFT was present. A noteworthy exception is the UK real estate market, with important and rational FT strategies to sustain. With respect to the long-term volatility persistence, this seems to capture the mean reversion of real estate returns in the UK and Hong Kong markets. In general, the results are not consistent with those reported in previous studies because NFT dominates PFT in the majority of real estate markets under consideration. Originality/value The main contribution of this study is the investigation of the link between short-term PFT or NFT and long-term volatility in eight international real estate markets, symmetrically. Particular attention has been given to the link between short-term FT and long-term volatility, by means of a fractionally integrated GARCH approach, a symmetric one. Moreover, investigating the relationship between returns’ volatility and investors’ strategies based on FT entails significant implications because real estate assets offer a good alternative investment for many investors and speculators.

scholarly journals The Real Estate Market and Financial Stability

2020 ◽  
Vol 5 (6) ◽  
pp. 1270-1283
Author(s):  
My-Linh Thi Nguyen ◽  
Toan Ngoc Bui
Keyword(s):  
Real Estate ◽  
Financial Stability ◽  
Real Estate Market ◽  
Data Series ◽  
Stock Market Volatility ◽  
The Real ◽  
The Real Estate ◽  
Long Run ◽  
The Real Estate Market ◽  
Short Run

This paper investigates the relationship between the real estate market (REM) and financial stability in Vietnam. Financial stability is measured using stock market volatility. The research is performed in Vietnam, a developing country whose stock and real estate markets are considered to be nascent, so the data series is very short. To solve this problem, the autoregressive distributed lag (ARDL) approach, which generates more valid results than its counterparts, is adopted. Furthermore, the ARDL approach is appropriate for a model with non-stationary data series and especially allows the analysis of the impact between data series in the short run and the long run. The results reveal the positive relationship between the real estate market and stock market volatility. However, this correlation only exists in the short run, which is a difference between Vietnam and developed countries. The paper also obtains an unprecedented finding confirming that the global financial crisis exerted a negative impact on the REM in Vietnam in the short run and the long run.

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