股价波动、通货膨胀与固定资产投资——基于协整和VEC模型的经验证据

经验证据表明,我国股票市场时全社会固定资产投资的影响作用还比较小,股票市场对固定资产投资的冲击在部分时段是负效应,可能存在部分"挤出效应".而影响固定资产投资的主要因素是GDP的变动.另外在我国,通货膨胀率对固定资产投资的影响比较明显.从短期来看,固定资产投资也带动了通货膨胀.国内固定资产投资的波动不仅受偏离均衡的影响,还受到GDP、Cpi、上证综合指数与存款利率水平自身变动的影响.央行应加强审慎的宏观调控,优化信贷结构,加强农村水利建设的长期投资,为经济结构调整和经济增长方式的转变提供稳定的货币金融环境.     

A perfect storm in the financial market

This study provides a model explaining how small changes in asset prices may disrupt an entire financial market. Based on the capital asset pricing model (CAPM), our model implies that during a market crash, asset price changes affect the relative distribution of the CAPM betas of individual assets and force all tradable assets to co-move. Using US stock market data, our empirical results are consistent with the model’s predictions. Overall, the study aids understanding of the price patterns of assets during substantial market downturns, such as financial crises.     

Equilibrium moment restrictions on asset returns: normal and crisis periods

Empirically, the covariance between stock returns varies with their volatility. We seek a robust theoretical explanation of this. With minimal assumptions, we model stochastic properties of equilibrium returns which result from the interaction between inter-temporal traders and noisy, price-sensitive short-term traders. The inter-temporal traders can have arbitrary investment rules, preferences and information. In all cases we find a set of restrictions between second moments of equilibrium returns. With two assets there is also a bound on the correlation between asset returns. Estimation with second moments of global stock returns supports our theoretical framework. Higher volatility in at least one market can increase comovement among markets. With globalization, covariances between two stock markets can also affect covariances between two other stock markets. We also find that the changes in trader behavior between normal and crisis periods lead to changes in the moment restrictions between asset returns.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

An evolutionary CAPM under heterogeneous beliefs

Heterogeneity and evolutionary behaviour of investors are two of the most important characteristics of financial markets. This paper incorporates the adaptive behaviour of agents with heterogeneous beliefs and establishes an evolutionary capital asset pricing model (ECAPM) within the mean-variance framework. We show that the rational behaviour of agents switching to better-performing trading strategies can cause large deviations of the market price from the fundamental value of one asset to spill over to other assets. Also, this spill-over effect is associated with high trading volumes and persistent volatility characterized by significantly decaying autocorrelations of, and positive correlation between, price volatility and trading volume.     

Multi-regime nonlinear capital asset pricing models

A multiple-regime threshold generalized autoregressive conditionally heteroskedastic capital asset pricing model is introduced. The model captures asymmetric risk through allowing market beta to change discretely between regimes that are driven by market information. Asymmetric volatility and mean equation dynamics are also captured. We confirm the time-varying nature of market risk, in response to changes in the market, and that this discrete time variation can differ across assets. These findings could have important implications for optimizing investment decisions: e.g. in risk assessment, portfolio selection and hedging decisions.     

波动率风险及风险价格——来自中国A股市场的证据

本文应用Fama-Macbeth估计方法,以1997年2月至2009年6月中国A股股票为样本,考察股票市场波动率风险及其风险价格的特征。研究表明:波动率风险是一个显著的横截面定价因子,其风险价格为负,该结论不受流动性及市场偏度因子、待检资产改变、波动率模型设定的影响;在资产定价模型中引入波动率风险因子有利于解释规模效应和账面市值比效应异象。波动率的风险因子可以涵盖部分宏观经济变量的定价信息,规模因子是波动率风险因子的代理变量。     

Leverage causes fat tails and clustered volatility

We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called ‘value investing’, i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are approximately normally distributed and uncorrelated across time. This changes when the funds are allowed to leverage, i.e. borrow from a bank, which allows them to purchase more assets than their wealth would otherwise permit. During good times, funds that use more leverage have higher profits, increasing their wealth and making them dominant in the market. However, if a downward price fluctuation occurs while one or more funds is fully leveraged, the resulting margin call causes them to sell into an already falling market, amplifying the downward price movement. If the funds hold large positions in the asset, this can cause substantial losses. This in turn leads to clustered volatility: before a crash, when the value funds are dominant, they damp volatility, and after the crash, when they suffer severe losses, volatility is high. This leads to power-law tails, which are both due to the leverage-induced crashes and due to the clustered volatility induced by the wealth dynamics. This is in contrast to previous explanations of fat tails and clustered volatility, which depended on ‘irrational behavior’, such as trend following. A standard (supposedly more sophisticated) risk control policy in which individual banks base leverage limits on volatility causes leverage to rise during periods of low volatility, and to contract more quickly when volatility becomes high, making these extreme fluctuations even worse.     

Common Volatility: An Empirical Investigation of Closed-End Country Funds

The study of international integration of equity markets has received a great deal of interest. This paper investigates whether returns of forty-one closed-end country funds share a common volatility process with three comparable return series: the underlying net asset value (NAV), U.S. stock market returns, and foreign stock market returns. Country funds are a natural setting to test for international market integration, as they are traded in the U.S. market, whereas their underlying assets are traded in foreign stock markets. Our results indicate that only a few emerging markets' country funds share common volatility processes with their comparable asset returns. This, in turn, suggests weak linkages through the second moment of related assets.     

Valuation of asset and volatility derivatives using decoupled time-changed Lévy processes

In this paper we propose a general derivative pricing framework that employs decoupled time-changed (DTC) Lévy processes to model the underlying assets of contingent claims. A DTC Lévy process is a generalized time-changed Lévy process whose continuous and pure jump parts are allowed to follow separate random time scalings; we devise the martingale structure for a DTC Lévy-driven asset and revisit many popular models which fall under this framework. Postulating different time changes for the underlying Lévy decomposition allows the introduction of asset price models consistent with the assumption of a correlated pair of continuous and jump market activity rates; we study one illustrative DTC model of this kind based on the so-called Wishart process. The theory we develop is applied to the problem of pricing not only claims that depend on the price or the volatility of an underlying asset, but also more sophisticated derivatives whose payoffs rely on the joint performance of these two financial variables, such as the target volatility option. We solve the pricing problem through a Fourier-inversion method. Numerical analyses validating our techniques are provided. In particular, we present some evidence that correlating the activity rates could be beneficial for modeling the volatility skew dynamics.     

Recovery of hidden state participation effects on oil and gas asset values

Commodity price shocks are shown to cause shifts in both the quantity and timing of risk in natural resource assets. We provide evidence that static risk measures understate the periodicity of price risk implicit in depleting assets. Risk measurement is demonstrated to be asset specific and to vary heterogeneously in response to the combined effects of state participation and market factors. We use a global sample of oilfield assets to demonstrate that oilfield participation terms cause corporate asset cash flows, volatility horizons and minimum variance hedge ratios to vary in response to oil price. We provide additional insights into movements in the timing of physical oil and gas asset risk, a hidden effect not recoverable from market oil prices. Temporal variance for physical assets is shown to be a hidden dimensional outcome of the effects of market factors and state participation.     

Hedging volatility risk

Volatility risk plays an important role in the management of portfolios of derivative assets as well as portfolios of basic assets. This risk is currently managed by volatility “swaps” or futures. However, this risk could be managed more efficiently using options on volatility that were proposed in the past but were never introduced mainly due to the lack of a cost efficient tradable underlying asset.The objective of this paper is to introduce a new volatility instrument, an option on a straddle, which can be used to hedge volatility risk. The design and valuation of such an instrument are the basic ingredients of a successful financial product. In order to value these options, we combine the approaches of compound options and stochastic volatility. Our numerical results show that the straddle option is a powerful instrument to hedge volatility risk. An additional benefit of such an innovation is that it will provide a direct estimate of the market price for volatility risk.     

Information,interest rates,and the volatility of asset prices

The volatility of an asset price is modelled as a function of the volatility of an information signal, real interest rates and inflation expectations. Volatility depends on the duration of cash flows, and the degree to which cash flows are indexed to real rates and inflation. The model is applied to determine asset betas, the volatility of the futures prices of assets and the volatility of equity prices.     

Competition,premature trading and excess volatility

A substantial body of research suggests that it is difficult to account for all of the volatility of asset prices in terms of news. This paper attempts to explain the excess volatility puzzle as a consequence of competitive interaction between market participants in the presence of noisy information. We develop a model of competitive interaction between market participants in response to unverified information. Our model shows that in the presence of competitive pressures, market participants find it optimal to act prematurely on unverified information. This premature reaction leads to lower total profits and excess market volatility in equilibrium. Our model also shows that the spike in volatility at the closing time of the market can be modelled as a direct consequence of premature trading.     

Price discovery and the international flow of information

We document that the opening volatility of American depositary receipts (ADRs) is lower when the trading of the underlying asset overlaps trading of the ADR on the New York Stock Exchange (NYSE). This lower volatility is consistent with the notion that price discovery on the NYSE is enhanced by concurrent trading in the underlying market. We also find that ADR volatility does not change when the underlying market closes, indicating that there is no significant change in the flow of public and private information at that time. This finding suggests that the NYSE becomes the dominant market during periods when both ADRs and their underlying assets are traded.     

基金业绩排名与泡沫资产配置

基金在业绩排名压力下的泡沫资产配置行为会加剧市场波动、推升市场泡沫。本文基于事件研究法与固定效应模型研究了2013―2020年基金的重仓持股,发现:(1)基金在泡沫膨胀阶段显著超配了泡沫资产,而在泡沫破裂前后显著减配,展现了“泡沫骑乘者”的特征;(2)2015年前后,泡沫资产主要为小市值股票,2019―2020年,泡沫资产逐渐转为大市值“白马股”,且基金超配的程度显著提高,呈现抱团大盘白马股的特征;(3)排名前5%的赢家基金,以及1/3和1/10关键排名处的基金更激进地超配泡沫资产;(4)相邻排名基金的业绩差异越大,基金的泡沫资产配置行为越明显。综上表明,基金公司并不一定是天然的市场稳定器,监管层应密切关注业绩压力下基金超配泡沫资产的行为,投资者则应理性选择长期声誉较高的基金。     

Can cryptocurrencies provide a viable hedging mechanism for benchmark index investors?

This paper investigates the non-linear causal nexus at lower, medium, and upper quantiles that cryptocurrencies can generate as a hedging mechanism for international benchmark indices, as represented by the MSCI World index and its sectoral sub-indices. Significant causality-in-mean between each of the assets examined is detected at lower quantiles only in a small number of sectors, whereas all analysed assets are found to trigger important non-linear causal impacts on the volatility of the selected benchmark indices, particularly during periods denoted to possess significant asset price fluctuations. From a financial perspective, this result suggests that cryptocurrencies are developing to possess a very similar ability to that of long-standing traditional financial assets, serving as a hedging mechanism against extreme fluctuations within investor portfolios that include MSCI constituents during a variety of very different market conditions.     

Asymmetric Volatility,Skewness, and Downside Risk in Different Asset Classes: Evidence from Futures Markets

This study examines the cross‐sectional variation of futures returns from different asset classes. The monthly returns are positively correlated with downside risk and negatively correlated with coskewness. The asymmetric volatility effect generates negatively skewed returns. Assets with high coskewness and low downside betas provide hedges against market downside risk and offer low returns. The high returns offered by assets with low coskewness and high downside betas are a risk premium for bearing downside risk. The asset pricing model that incorporates downside risk partially explains the futures returns. The results indicate a unified risk perspective to jointly price different asset classes.     

The Effect of the Transparency of Order Flows in a Dealer Market with Several Securities

This paper compares two trading mechanisms in a dealer market with several securities exhibiting asymmetric information and imperfect competition. These two market structures differ in the information received by market-makers. While in the first of them when setting the price of an asset, they observe the order flows of all assets, in the second one they only observe the order flow corresponding to this asset. In order to make this comparison, we analyze several market indicators such as the informed expected traded volume, the market depth, the volatility and the informativeness of equilibrium prices, and the informed traders' ex-ante expected profits. Journal of Economic Literature Classification Numbers: G10.     

The intertemporal capital asset pricing model with dynamic conditional correlations

The intertemporal capital asset pricing model of Merton (1973) is examined using the dynamic conditional correlation (DCC) model of Engle (2002). The mean-reverting DCC model is used to estimate a stock’s (portfolio’s) conditional covariance with the market and test whether the conditional covariance predicts time-variation in the stock’s (portfolio’s) expected return. The risk-aversion coefficient, restricted to be the same across assets in panel regression, is estimated to be between two and four and highly significant. The risk premium induced by the conditional covariation of assets with the market portfolio remains positive and significant after controlling for risk premia induced by conditional covariation with macroeconomic, financial, and volatility factors.