Causal Relationship Between Stock and Real Estate Market Returns in G7 Countries: A Comparison Before and After the Pandemic
Bibliographic record
Abstract
[[abstract]]本文以G7國家為研究對象，探討疫情前後股票與房地產市場之間的因果關係。為捕捉市場在不同狀態下的互動關係與結構性差異，本研究採用分位數Granger非因果關係檢定，結合Sup-Wald統計量，以檢驗各國的兩類市場之間是否存在顯著的互動關係與結構性變動。同時，本研究亦納入不同滯後階數模型設計進行分析，進一步捕捉市場間是否具有延遲反應。實證結果顯示，股票與房地產市場間的互動關係在低分位(市場低迷)、中分位(市場穩定)與高分位(市場熱絡)等不同市場階段皆呈現明顯異質性。部分國家(如加拿大、義大利與日本)在特定分位數下呈現雙向因果關係，其他國家則僅顯現單向或不顯著的關係。而疫情前後，亦有多數國家的兩類市場之間結構性重組的現象，反映出市場在面對重大經濟衝擊事件與政策制度調整後，互動模式產生顯著改變，並且部分國家的市場具有延遲影響。最後，經研究發現，分位數Granger非因果關係檢定更能揭示潛在的非線性關係與結構性轉變。在實務層面，研究結果亦可協助投資者於極端市場情境下進行跨市場資產配置判斷，同樣，為政策制定者在面對全球性衝擊時，提供策略之參考。 This paper studies the causal relationship between the stock market and the real estate market in G7 countries before and after the COVID-19 pandemic. To capture the interaction under different market conditions and structural differences, this paper adopts the Quantile Granger Non-causality Test combined with the Sup-Wald statistic to examine whether sig-nificant interaction relationships and structural changes exist between the two markets in each country. At the same time, this paper incorporates models with different lag structures to further capture whether delayed responses exist between markets. The empirical results show that the interaction between the stock and real estate mar-kets presents significant heterogeneity across different market stages, including lower quan-tiles (market downturns), median quantiles (market stability), and upper quantiles (market exuberance). In some countries, such as Canada, Italy, and Japan, bidirectional Granger cau-sality appears at specific quantiles, while in others, only unidirectional or insignificant rela-tionships are observed. Before and after the pandemic, many countries also experienced structural reconfigurations between the two markets, reflecting significant changes in inter-action patterns in response to major economic shocks and policy adjustments. In addition, some countries' markets exhibit delayed effects. Finally, the study finds that the Quantile Granger Non-causality Test is more capable of revealing potential nonlinear relationships and structural changes. In practice, the results can assist investors in making cross-market asset allocation decisions under extreme market conditions, and likewise provide references for policymakers in responding to global shocks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".