We study how investor composition is related to future return, trading volume, and price volatility in the cross- section of the music-content non-fungible tokens (music NFTs). Our results show that the breadth of NFT ownership negatively predicts weekly collection-level median-price returns and trading counts. In contrast, ownership concentration and the fraction of small wallets are positive predictors. The fraction of large NFT wallets is a bearish signal for future collection floor-price returns. Investor composition measures have weak predictive power on price volatility. Further analysis indicates that an artist’s Spotify presence moderates the predictive power of investor composition for future NFT returns and trading volume, consistent with the notion that reducing information asymmetry helps improve price efficiency.

China’s energy stocks are confronted with significant climate-related challenges. This paper aims to measure the daily climate transition risk in China by assessing the intensity of climate policies. The daily climate physical risk encountered by China’s renewable energy stocks is also measured based on the perspective of temperature change. Then, the partial linear function coefficient model is adopted to empirically investigate the non-linear impacts of climate transition risk and climate physical risk on the return and volatility of renewable energy stocks. The nonlinear moderating effect of climate transition risk is also involved. It is found that: (1) Between 2017 and 2022, the climate transition risk in China exhibited a persistent upward trend, while the climate policies during this period particularly emphasized energy conservation, atmospheric improvements, and carbon emissions reduction. Additionally, the climate physical risk level demonstrated a pattern consistent with a normal distribution. (2) There is a U-shaped nonlinear impact of climate physical risk on the return and volatility of renewable energy stocks. High climate physical risk could not only increase the return of renewable energy stocks but also lead to stock market volatility. (3) Climate transition risk exhibits a U-shaped effect on the return of renewable energy stocks, alongside an inverted U-shaped effect on their volatility. Notably, a high level of climate transition risk not only increases the return of renewable energy stocks but also serves to stabilize the renewable energy stock market. Moreover, the heightened risk associated with climate transition enhances the negative impact of oil price volatility on the yield of renewable energy stocks and, concurrently, leads to an increase in volatility.The strength of this moderating effect is directly correlated with the level of climate risk.

Researchers have recently studied the interactions between corporate and government bond issuances in a variety of countries. Some conclude that government bonds compete with private bond issuances, while others conclude the opposite. We study here the special case of China’s 2017 issuance of two sovereign bonds denominated in U.S. dollars. We find that corporate bonds experienced a decline in yield spreads, bid-ask spreads, and price volatility around the time this sovereign issuance was first announced. The results are particularly strong for corporate bonds with maturities similar to those of the USD sovereigns. We conclude that these new bonds served as useful reference instruments that helped investors price and hedge the risks impounded in Chinese corporate bonds.

In this paper, we study market crowd psychological behaviors in learning by correlation analysis, using every trading high frequency data in China stock market. We introduce a notion of trading conditioning in terms of operant conditioning in psychology and measure its intensity by accumulative trading volume probability in a time interval in the transaction price-volume probability wave equation that can describe market crowd coherence in their interacted trading behavior. We find that there is, in general, significant positive correlation between the rate of price volatility mean return and the change in the intensity of market crowd trading conditioning. They behave significantly disposition effect in stock selling and herd behavior in stock buying with expectation on return simultaneously. Specifically, “the herd” have significant stronger expectation on price momentum than its reversal. Second, there is also a significant negative correlation between them in a subdivided term; market crowd show buy-and-hold behavior when price rises steadily, and panic selling when it drops abruptly in depth. We explain both the puzzle of more peaked, heavily tailed, and clustered characteristics in return distribution by coherence and that of market crowd behavioral “anomalies” by trading conditioning in a unified transaction price-volume probability wave framework.

We hypothesize and test an inverse relationship between liquidity and price volatility derived from microstructure theory. Two important facets of liquidity trading are examined: thickness and noisiness. As represented by expected volume (thickness) and realized average commission cost per share (noisiness) of NYSE equity trading, both facets are found negatively associated with ex post and ex ante price volatilities of the NYSE stock portfolios and the NYSE composite index futures. Furthermore, the inverse association between volatility and noisiness is amplified in times of market crisis. The overall results demonstrate that volatility increases as noise trading declines. All findings retain statistical significance and materiality after controlling for a number of specifications. This inverse liquidity-volatility relationship reflects a microstructure interpretation of the liquidity risk premium documented in the asset pricing literature.

Motivated by how transaction amount constrain trading volume and price volatility in stock market, we, in this paper, study the relation between volume and price if amount of transaction is given. We find that accumulative trading volume gradually emerges a kurtosis near the price mean value over a trading price range when it takes a longer trading time, regardless of actual price fluctuation path, time series, or total transaction volume in the time interval. To explain the volume-price behavior, we, in terms of physics, propose a transaction energy hypothesis, derive a time-independent transaction volume-price probability wave equation, and get two sets of analytical volume distribution eigenfunctions over a trading price range. By empiric test, we show the existence of coherence in stock market and demonstrate the model validation at this early stage. The volume-price behaves like a probability wave.

We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intensity. Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there is, in general, significant positive correlation between the rate of mean return and that of change in trading conditioning intensity; 2) it lacks significance in spite of positive correlation in two time intervals right before and just after bubble crashes; and 3) it shows, particularly, significant negative correlation in a time interval when SSE Composite Index is rising during bull market. Our model and findings can test both disposition effect and herd behavior simultaneously, and explain excessive trading (volume) and other anomalies in stock market.

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student-t distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from November 20, 1999 to October 9, 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after November 27, 2001. The model introducing GARCH jump effect with normal and Student-t distribution assumption can better fit the volatility characteristics. We find that that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.

In recent times, the prices of internationally-traded commodities have reached record highs and are expected to continue growing in the foreseeable future. This phenomenon is partially driven by strong demand from a small number of emerging economies, such as China and India. This paper places the recent commodity price boom in historical context, drawing on an investigation of the long-term time-series properties, and presents unique features for 33 individual commodity prices. Using a new methodology for examining cross-sectional variation of commodity returns and its components, we find strong evidence that the prices of world primary commodities are extremely volatile. In addition, prices are roughly 30 percent more volatile under floating than under fixed exchange rate regimes. Finally, using the capital asset pricing model as a loose framework, we find that global macroeconomic risk components have become relatively more important in explaining commodity price volatility.

Motivated by the behavior of asset prices, trading volume, and price volatility during episodes of asset price bubbles, we present a continuous-time equilibrium model in which overconfidence generates disagreements among agents regarding asset fundamentals. With shortsale constraints, an asset buyer acquires an option to sell the asset to other agents when those agents have more optimistic beliefs. As in a paper by Harrison and Kreps, agents pay prices that exceed their own valuation of future dividends because they believe that in the future they will find a buyer willing to pay even more. This causes a significant bubble component in asset prices even when small differences of beliefs are sufficient to generate a trade. In equilibrium, bubbles are accompanied by large trading volume and high price volatility. Our analysis shows that while Tobin’s tax can substantially reduce speculative trading when transaction costs are small, it has only a limited impact on the size of the bubble or on price volatility.