Commodity prices and BRIC and G3 liquidity: A SFAVEC approach

This paper investigates the influence of liquidity in the major developed and major developing economies on commodity prices. Liquidity is taken to be M2. A novel finding is that unanticipated increases in the BRIC countries’ liquidity is associated with significant and persistent increases in commodity prices that are much larger than the effect of unanticipated increases in G3 liquidity, and the difference increases over time. Over 1999–2012 BRIC liquidity is strongly linked with global energy prices and global real activity whereas G3 liquidity is not. The impact of BRIC liquidity on mineral and metal prices is twice as large as that of G3 liquidity. Granger casualty goes from liquidity to commodity prices. BRIC and G3 liquidity and commodity prices are cointegrated. BRIC and G3 liquidity and global output and global prices are cointegrated. We construct a structural factor-augmented error correction (SFAVEC) model.     

Quantitative easing and the loan to collateral value ratio

We study optimal monetary policy in a New Keynesian model at the zero bound interest rate where households use cash alongside house equity borrowing to conduct transactions. The amount of borrowing is limited by a collateral constraint. When either the loan to value ratio declines or house prices fall, we observe a decrease in the money multiplier. We argue that the central bank should respond to the fall in the money multiplier and therefore to the reduction in house prices or the loan to collateral value ratio. We also find that optimal monetary policy generates a large and persistent fall in the money multiplier in response to the drop in the loan to collateral value ratio.     

Do housing prices promote total factor productivity? Evidence from spatial panel data models in explaining the mediating role of population density

The issue of what to promote in total factor productivity (TFP) in urban areas has been widely discussed in academia and housing prices and population density are confirmed to be two of the most essential driving factors. However, research into the interaction of housing prices and population density with TFP has been neglected, with no previous studies taking spatial factors into consideration, which may bias the results. From this perspective, using spatial panel data models and employing instrumental variables to solve the endogenous problem, this study examines the impact of housing prices on TFP through the mediating effect of population density for 283 Chinese cities during the period 2000–2013, and confirms that the mediating effect accounts for 18.70 % of the total effect. The results show the positive and significant association of housing prices with TFP and the inverted U-shape of population density. The underlying logic is that housing prices change population density by attracting people with high purchasing power and discouraging those unable to afford housing, whereas increased density helps to promote productivity since the settled inhabitants always have highly developed work skills and are well educated. The influencing mechanism of housing prices on TFP through population density is analyzed, namely the spillover effect. We find that the spillover effect exists in the eastern and central regions, as well as first, second, and third tier cities, while for western regions and fifth tier cities, population mobility and increased in housing prices slows their economic development. There is no evidence of any spillover effect in fourth tier cities. A discussion and suggested policy implications are also provided.     

Estimating the duration of different seasons and their impact on hotel room prices

The purpose of the study is to accurately measure the span of different seasons and its effect on the fluctuation of the occupancy rate and the average room rates in the hospitality sector. While prior studies have concentrated on measuring seasonality using calendar months, this study takes a different approach by measuring seasons in terms of the date count so that the exact starting and ending date of a season can be identified. This step involves adopting a non-parametric methodology that split the time scale into several small parts to obtain a better fit of the relationship and that can detect the starting and end of the season when given specific dates.     

Time-frequency co-movements between oil prices and interest rates: Evidence from a wavelet-based approach

This paper investigates time–frequency co-movements between crude oil prices and interest rates. To test this relationship, the study applied a continuous wavelet and cross wavelet approaches to data from West Texas Intermediate (WTI) crude oil prices and interest rates in the United States (U.S.). Results from the sample period revealed significant relationships, in the intermediate term, between WTI crude oil prices and U.S. interest rates. Moreover, co-movements between oil price and interest rate variables were especially sensitive during abnormal political events and periods of financial ‘meltdown’. We further use Partial Wavelet Coherence (PWC) and Multiple Wavelet Coherence (MWC) methods to investigate the impacts of five major control variables namely GDP growth, unemployment, three-month Treasury bill, CPI index and industrial production index. The results show a powerful impact of control variables on oil-interest rates co-movements under different frequencies. Finally, we show evidence of co-integrating long run relationship between oil markets and control variables. These results have important implications for energy investors and policy makers.     

Information in stock prices: the case of the 2016 U.S. presidential election

On the day before the 2016 U.S. presidential election, the odds of Hillary Clinton winning the presidency, according to political prediction markets, were above 90%. Surprisingly, Donald Trump won the Electoral College handily. In this study, we examine how movements in specific stock prices foreshadowed the eventual outcome. Specifically, we conduct a series of standard event-study tests focused on pharmaceutical companies, which became a focal point during the presidential campaign. Results show that while stocks of pharmaceutical companies significantly underperformed the market prior to the election, prices substantially increased beginning three days before the election outcome. This increase is both statistically significant and economically meaningful and robust to various event-study methodologies. These results suggest that some sectors of the stock market seemed to anticipate the election outcome.     

Bear beta

We test whether bear market risk, time variation in the probability of future bear market states, is priced. We construct an Arrow–Debreu security that pays off in bear market states (AD Bear) from traded Standard & Poor’s (S&P) 500 index options and use its returns to measure bear market risk. We find that bear beta (exposure to bear market risk) has a strong relation with expected stock returns that is robust, persistent, and remains strong among liquid and large stocks. Historical bear beta also predicts future bear market risk exposure. We conclude that bear market risk is priced in the cross section of stock returns.     

Forecasting Bordeaux wine prices using state-space methods

Generic Bordeaux red wine (basic claret) can be regarded as being similar to an agricultural commodity. Production volumes are substantial, they are traded at high frequency and the quality of the product is relatively homogeneous. Unlike other commodities and the top-end wines (which represent only 3% of the traded volume), there is no futures market for generic Bordeaux wine. Reliable forecasts of prices can to large extent replace this information deficiency and improve the functioning of the market. We use state-space methods with monthly data to obtain a univariate forecasting model for the average price. The estimates highlight the stochastic trend and the seasonality present in the evolution of the price over the period 1999 to 2016. The model predicts the path of wine prices out of sample reasonably well, suggesting that this approach is useful for making reasonably accurate forecasts of future price movements.     

Time-varying inter-urban housing price spillovers in China: Causes and consequences

The spatial spillovers of housing prices across regions are well documented by a large body of previous studies. This paper tries to investigate the dynamic (time-varying) evolution of spatial interactions and their underlying driving factors intensively. Using a recently developed Generalized Autoregressive Score (GAS) model, this paper examines the time-varying spatial spillovers of housing prices in 70 major and median cities of China from 2006 to 2019. We find that the GAS model can well capture the impact of time-varying critical events of Chinese real estate market on the whole. However, different regions display heterogeneous variation patterns over time. Further investigation shows that inter-regional labor mobility and trades are two major channels, accounting for 1.25% and 2.58% of the monthly standard deviations of spatial spillover effects from one city to another, respectively. We also characterize and distinguish between three time-varying patterns of spatial spillovers within different regions of China. Our results shed lights on the understanding of spatial spillovers across regional real estate markets across different city network structures within China.     

Land use regulation and urban land value: Evidence from China

Land use regulation has always been regarded as one of the most crucial means of macro-control of urban growth, which can affect a city’s land values directly and further determine related urban economic well-being. Since the New Type Urbanization Strategy proposed by the government in 2014, China’s mode of urban growth has been transformed from addressing “quantity” to “quality" in the urbanization process. In this case, the regulation of land use by the Chinese government plays a more important role in urban growth. With their planned land regulatory scheme, the various instruments employed by Chinese governments have quite different mechanisms influencing land prices. However, there are no rigorous studies focusing on the land use regulation system and its impact on land values to date, particular in China. This study seeks to explore how land use regulation affects urban land values through the systematic lens. We summarize the main land use regulatory instruments based on the analysis of China’s planned land use system and urban land banking system, including the construction land quota, constraints on the allowed floor area ratio (FAR) of each land transaction parcel, and land supply restrictions. A new dataset based on land transaction data from 2007 to 2016 that covers 286 prefectural cities from the country’s coastal, central, and western regions is used in the empirical analysis. The results show that the effects of the floor area ratio (FAR) on land values are significant and positive, with the residential and commercial land supply ratio being a key factor. Moreover, the results imply that the effects of constraints on FAR and commercial land supply vary between regions. These findings indicate that the Chinese land market is considerably distorted by excessive administrative interventions by local governments, in the stage of urban transition guided, the capable regulatory instruments could play an important role in adjusting urban land prices and hence impact on urban growth.