Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?

The speculative nature of both stock and housing markets in China has attracted the attention of observers. However, while stock market data are easily available, the low frequency and low quality of publicly available housing price data hampers the study of the relationship between the two markets. We use original hedonic weekly resale housing prices of a major Chinese housing market and study them in conjunction with Shanghai's stock market index in the second half of the 2000s. The use of the Phillips et al. (2015 a,b) recursive explosive‐root test enables us to detect and date speculative episodes in both markets. We then implement the Greenaway‐McGrevy and Phillips (2016) methodology to detect the presence of migration between the two types of bubbles. We detect significant migration from the stock to the housing market bubble in 2009 and a temporary spillover in 2007.     

Common trends and common cycles in stock markets

In this paper we examine the role of permanent and transitory shocks in explaining variations in the S&P 500, Dow Jones and the NASDAQ. Our modeling technique involves imposing both common trend and common cycle restrictions in extracting the variance decomposition of shocks. We find that: (1) the three stock price indices are characterized by a common trend and common cycle relationship; and (2) permanent shocks explain the bulk of the variations in stock prices over short horizons.     

A Study of Social Network Effects on the Stock Market

Consumer confidence is an economic indicator that measures the degree of optimism that consumers feel about the overall state of the economy as well as their personal financial situation. The authors measure consumer sentiment via analysis of social networks and show that such sentiment affects stock prices; specifically, the S&P 500 and the Dow Jones Industrial Average. Shiller, Fischer and Freidman [1984], Fisher and Statman [2003], and Bremmer [2008] also examine the influence of consumer sentiment, measured from Conference Board data, on the stock market. The authors add to this literature by creating a measure of consumer confidence by utilizing Twitter data and by examining the relationship between our measure of consumer sentiment and the S&P 500 and the Dow. They implemented lexicographic analysis of Twitter data over a three-month period and found that talk intensity of economic issues not only causes shifts in the daily stock market prices, but also has a significant negative effect.     

Correlations and volatility spillovers across commodity and stock markets: Linking energies,food, and gold

This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets.     

The effect of quantitative easing on stock prices: a structural time series approach

A structural time series model is estimated and tested to examine the effect of quantitative easing (QE) on US stock prices. The model is estimated by maximum likelihood in a Time-varying parametric (TVP) framework, using the S&P 500 index as the dependent variable and the Fed’s balance as an explanatory variable in addition to the unobserved components accounting for the behaviour of variables that do not appear explicitly in the equation. The results show that QE had a sizeable, but not exclusive, effect on stock prices and that stock prices were also affected by other missing variables and cyclical movements. Several explanations are presented for the rise of the US stock market in the post-QE period, particularly since the election of Donald Trump.     

TESTING FOR SPECULATIVE BUBBLES USING SPOT AND FORWARD PRICES

The probabilistic structure of periodically collapsing bubbles creates a gap between future spot and forward (futures) asset prices in small samples. By exploiting this fact, we use a recently developed recursive unit root test and rolling Fama regressions for detecting bubbles. Both methods do not rely on a particular model of asset price determination, are robust to explosive fundamentals, and allow date stamping. An application to U.S. dollar exchange rates provides evidence of bubbles during the interwar German hyperinflation, but not during the recent floating‐rate period. A further application to S&P 500 supports the existence of bubbles in the U.S. equity market.     

Bubbles in UK house prices: evidence from ESTR models

Recent movements in stock and house prices have led to an examination of the presence of bubbles. Whilst, there is extensive research on stock price data, there is relatively less for house prices. This paper uses a present‐value model for house prices to test for the presence of bubbles. The results support the presence of a non‐fundamental component within UK national and regional house prices. In particular, for the majority of series considered, evidence is presented of linear non‐stationarity within the fundamental present‐value relationship, and of non‐linear stationarity, implying the presence of a non‐fundamental, or bubble, component. Furthermore, evidence is presented that prices adjust quicker when they are below fundamental equilibrium, than when they are above fundamental equilibrium, i.e. there is downward price stickiness. These results support the hypothesis that house price dynamics can be characterised by price‐to‐price momentum. Finally, forecast evidence suggests that real prices are likely to adjust downwards and converge with fundamental value.     

Testing for rational bubbles in the US housing market

The presence of a bubble in the US housing market prior to the 2007 subprime mortgage financial crisis is investigated. This is done by looking into the relationship between house prices and rental prices, known as the price–rent ratio, which is an important measure of a potential deviation between house prices and its fundamental value. Additionally, the interest rate is taken into account since it is an important factor in determining demand for housing mortgages and thereby influencing house prices, and explosive behavior of house prices is considered. These relationships are investigated through a theoretical and econometrical framework. The empirical evidence suggests that there was a bubble in the housing market prior to the financial crisis, even when controlling for the decreasing interest rate and the fundamental information given by the rental price in the period. Explosiveness was the main source of the price increase, such that a bubble was present in the housing market after correcting for other fundamental factors. The econometric procedures used in the analysis may therefore be relevant for monitoring the housing market.     

Dynamic hedge ratio for stock index futures: application of threshold VECM

This study represents one of the first papers in stock-index-futures arbitrage literature to investigate the effects of arbitrage threshold on stock index futures hedging effectiveness by using threshold vector error correction model (hereafter threshold VECM). Moreover, in contrast to prior studies focusing on examining case studies involving mature stock markets, this study not only adopts US S&P 500 stock market as the sample but also adds an analysis of one emerging stock market, Hungarian BSI and examines the differences between them. Finally, this investigation employs a rolling estimation process to examine the impact of arbitrage threshold behaviours on the setting of futures hedging ratio. The empirical findings of this study are consistent with the following notions. First, arbitrage behaviour reduces co-movement between futures and spot markets and increases the volatility of both futures and spot markets. Second, this article denotes the outer regime of futures-spot market for the case of Hungarian BSI (US S&P 500) as a crisis (an unusual) condition. Moreover, arbitrage threshold behaviours make remarkable (unremarkable) shift on optimal hedge ratio between two different market regimes for the case of Hungarian BSI (US S&P 500). Finally, the framework involving regime-varying hedge ratio designed in this study provides a more efficient futures hedge ratio design for Hungarian BSI stock market, but not for US S&P 500 stock market.     

Exploring what stock markets tell us about GDP in theory and practice

This paper explores the stock market-GDP relationship from basic theory to simple empirics to better understand what stock market movements tell us about underlying GDP in real time. We present a simple theoretical model to make key relationships clear, then explore US GDP and US stock market (S&P 500) performance through a range of analytical tools from visual inspection to correlations, regressions, counting and extreme value calculations to a few illustrative narrative investigations. We find that the S&P 500 is weakly correlated with real GDP as well as with vintage GDP releases contemporaneous, but more strongly and statistically significantly with one lag as theory predicts. We also find that the S&P 500 is more closely related both contemporaneously and with a lag to final, revised GDP numbers - only known months later - than to vintage GDP estimates, suggesting that stock market trends are informative about true GDP.     

The recent boom–bust cycle: The relative contribution of capital flows, credit supply and asset bubbles

We use an estimated open economy DSGE model with financial frictions for the US and the rest of the world to evaluate various competing explanations about the recent boom–bust cycle. We find that the savings glut hypothesis is insufficient for explaining all aspects of the boom in the US. Relatively strong TFP growth and expansionary monetary policy are also not able to explain fully the volatility of corporate and in particular residential investment. We identify bubbles in the stock and housing market as crucial. Concerning the downturn in 2008/2009, the fall in house prices and residential investment only plays a minor role. Mortgage defaults have more explanatory power, especially in a specification of the model with a segregated equity market. Finally, the bursting of the stock market bubble was at least as important in this recession as in 2001. Because of various negative shocks hitting the economy at the same time in 2008/2009 and continued positive technology growth, not only the real interest rate declined but inflation fell rapidly and left insufficient room for monetary policy to play a similar stabilising role as in previous recessions.     

Asset bubbles,monetary policy and bank lending in Japan: an empirical investigation

Asset prices rose rapidly in Japan during the latter half of the 1980s, and then declined as quickly in the early 1990s. Their behaviour is consistent with the existence of speculative ‘bubbles’ in these markets. This paper investigates the dynamic relationships among stock and land prices in Japan, output, and monetary and bank lending variables. The results of causality tests and variance decompositions are reported for two time periods, 1972#1501985 and 1986#1501991. The price bubbles affected each other in the first period, although the size of this impact is dependent on the choice of variables in the VARs. In the bubble period, there is strong evidence that the stock market bubble was determined by its own past and also influenced the land market bubble, accounting for a significant proportion of the variance of the land market bubble. However, neither output, the money supply nor the lending variables were significant in the causality tests or in explaining the variation of the two assset bubbles.     

Market overreaction and investment strategies

We investigated the overreaction of the Korean market in response to shocks in the US stock market, and analysed the dynamic relationship between these two markets since 1996. We found that the KOSPI 200 index futures overreacted to the S&P 500 index returns during the period from 2000 to 2009 when the Korean market was in its growth stage. As the Korean market matured and the KOSPI 200 overnight futures were introduced in 2009, the overreaction disappeared. When investors employed the Kelly model or Value-at-Risk to exploit the overreaction, their trading strategies produced significant profits during the growth stage even after considering transaction costs and risk, but the profits attenuated once the overnight futures market was launched in 2009.     

Identifying the Transition from Efficient-Market to Herding Behavior: Using a Method from Econophysics

We test whether “detrended fluctuation analysis” (DFA)—an econophysics method—identifies the transition from efficient-market trading to herding behavior and the rise of the NASDAQ dot.com stock market bubble. DFA divides a time series into “segments” of varying lengths and then tests whether power-law distributions exist within the segments. A power-law distribution of stock-price changes within a segment indicates herding behavior and the start of the dot.com bubble. The clarity of the transition indication depends on both segment lengths and segment starting dates. Our findings show that DFA can be used to identify the beginning of stock-market bubbles but not the beginning of crashes.     

China's housing bubble burst?

In this paper, we developed the recursive unit root tests to identify the beginning and end of potential speculative bubbles in the Chinese housing price cycles during 2006–2013 for the 70 major cities of China. The method is best suited for a practical implementation with a time series and delivers a consistent date‐stamping strategy for the origination and termination of multiple bubbles. Simulations demonstrate that the test significantly improves discriminatory power and leads to distinct power gains when multiple bubbles occur. Overall, the results indicate that the speculative housing price bubbles in China are not bursting, and they indicate that the stationarity of the housing price level varies across the different city sizes. Between the cities, approximately one‐fourth of the bubbles have burst up to December 2013, while the first‐tier city bubble may not burst due to the urbanization process.     

Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach

Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging (DMA) and dynamic model selection (DMS) are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.     

Projection Bias and Financial Risk Tolerance

Behavioral finance theories explain "why" individuals exhibit behaviors that do not maximize expected utility. This study explores how projection bias, as explained by regret theory, may shape financial risk tolerance attitudes. The results suggest that gender, income, and stock market price changes, as measured by the NASDAQ, the Dow Jones Industrial Average, and the Standard & Poor's 500 indexes, help explain risk attitudes. Risk tolerance appears to be an elastic and changeable attitude. This research expands on the work of Shefrin [2000], who reported that recent stock market price changes exert a strong influence on risk tolerance attitudes and behaviors.     

Detection of high and low states in stock market returns with MCMC method in a Markov switching model

To detect abnormal states in stock market returns, this study considers seven indices, over a 21-year period, the Dow Jones, S&P500, Nasdaq, Nikkei225, FTSE100, DAX, and CAC40. Three states are possible, namely a state of high rate of return, a state of low rate of return, both with high volatility and an intermediate state with low volatility. To determine the state of the market at each date, we study the returns using Markov chain Monte Carlo method (Metropolis–Hastings algorithm). Then at a second time, using a Cramer's coefficient, we deduce association coefficients or “correlations” among the different states of the major stock exchange markets around the world. First, the associations were globally stronger during the subprime crisis than during the dot-com bubble period. Second, among European markets Cramer's V is higher regardless of the period. Third, the associations between the Nikkei and the other market indices are systematically lower, indicating the relative disconnection of the Japanese market.     

Stock,Energy and Currency Effects on the Asymmetric Wheat Market

The purpose of this paper is to explore the effects of financial and currency indicators on wheat futures prices. The results suggest that the stock market, and particularly the S&P 500, positively influence the wheat market, a fact that is attributed to the wealth effect and the modern portfolio management in the context of international markets’ integration. There is also evidence that the energy markets affecting the supply and demand side exert significant impact on the wheat market. Furthermore, the results show that the shocks of the U.S. dollar/yen exchange rate are transmitted to the wheat market. Finally, the structural analysis of wheat prices’ volatility support the hypothesis of the asymmetric conditional variance, as it appears to be more volatile in response to positive shocks caused by higher wheat prices, contrary to the respective results of the equities market.     

Bidirectional relationship between investor sentiment and excess returns: new evidence from the wavelet perspective

We propose to use the wavelet concept of the phase angle to determine the lead–lag relationship between investor sentiment and excess returns that are related to the bubble component of stock prices. The wavelet phase angle allows for decoupling short- and long-run relations and is additionally capable of identifying time-varying comovement patterns. Based on the monthly S&P500 index and two alternative monthly US sentiment indicators, we find that in the short run (until 3 months), sentiment is leading returns whereas for periods above 3 months the opposite can be observed. Moreover, the initially strong positive relationship becomes less pronounced with increasing time horizon, thereby indicating that the over- or undervaluation in the short run is gradually corrected in the long run.