News shocks and asset price volatility in general equilibrium

We study equity price volatility in general equilibrium with news shocks about future productivity and monetary policy. As West (1988) shows, in a partial equilibrium present discounted value model, news about the future cash flow reduces asset price volatility. We show that introducing news shocks in a canonical dynamic stochastic general equilibrium model may not reduce asset price volatility under plausible parameter assumptions. This is because, in general equilibrium, the asset cash flow itself may be affected by the introduction of news shocks. In addition, we show that neglecting to account for policy news shocks (e.g., policy announcements) can potentially bias empirical estimates of the impact of monetary policy shocks on asset prices.     

Forecasting crude oil price volatility

We use high-frequency intra-day realized volatility data to evaluate the relative forecasting performances of various models that are used commonly for forecasting the volatility of crude oil daily spot returns at multiple horizons. These models include the RiskMetrics, GARCH, asymmetric GARCH, fractional integrated GARCH and Markov switching GARCH models. We begin by implementing Carrasco, Hu, and Ploberger’s (2014) test for regime switching in the mean and variance of the GARCH(1, 1), and find overwhelming support for regime switching. We then perform a comprehensive out-of-sample forecasting performance evaluation using a battery of tests. We find that, under the MSE and QLIKE loss functions: (i) models with a Student’s t innovation are favored over those with a normal innovation; (ii) RiskMetrics and GARCH(1, 1) have good predictive accuracies at short forecast horizons, whereas EGARCH(1, 1) yields the most accurate forecasts at medium horizons; and (iii) the Markov switching GARCH shows a superior predictive accuracy at long horizons. These results are established by computing the equal predictive ability test of Diebold and Mariano (1995) and West (1996) and the model confidence set of Hansen, Lunde, and Nason (2011) over the entire evaluation sample. In addition, a comparison of the MSPE ratios computed using a rolling window suggests that the Markov switching GARCH model is better at predicting the volatility during periods of turmoil.     

Asset price,risk transfer and economic activities: Firm-level evidence from China

The paper investigates the extent of the impact from changes in asset price and risk on corporate investment behaviors as well as the real economy. The results support the unidirectional causality effects from asset price fluctuations on the macro-level. By applying quarterly data of Chinese listed companies, we further find the existence of balance sheet effect on the firm-level, which suggests that the changes in asset prices and risk affect the net asset value, and consequently influence corporate investment decisions. More importantly, the balance sheet effect appears to be much more significant after the implementation of new fair value accounting standards in 2007. The impact on the real economy from asset price risk is found to be more prominent as well.     

流动性溢价理论研究述评

资产流动性是否影响资产的价格一直是资本市场理论研究的热点问题,也是投资者决策的重要理论依据之一。本文系统回顾了国内外有关流动性溢价理论的研究现状,分析了我国股市流动性溢价的影响因素,并指出了我国在流动性溢价理论研究方面所存在的问题以及未来的研究方向。     

房价波动、信贷扩张与金融稳定

在美国次贷危机爆发、国内房地产价格居高不下的背景下,如何实现房地产业健康发展,经济持续增长及金融稳定,是政策当局面临的重大现实问题。本文围绕着房地产价格波动、信贷扩张和金融不稳定之间的互动机理的研究进展,梳理这一领域的前沿研究成果,并对理论发展脉络进行述评,最后提出了这一领域值得继续研究的若干问题。     

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.     

Asset market response to monetary policy news from SNB press releases

This paper analyses the effects of Swiss National Bank (SNB) communication on asset prices. It distinguishes between different monetary policy news contained in press releases following a monetary policy decision. Employing a latent variable approach and event-study methods, I find that medium- and long-term bond yields respond to changes in the communicated inflation and GDP forecasts as well as to the degree of pessimism expressed in press releases. Exchange rates mainly react to changes in the GDP forecast while stocks do not react to SNB communication on monetary policy announcement days. Additionally, short-term expectations about the future path of the policy rate are driven by the communicated inflation forecast. The results underline the role of qualitative news beyond quantitative forecasts in influencing market expectations and asset prices.     

Asset prices in affine real business cycle models

I describe a tractable way to study macroeconomic quantities and asset prices in a large class of dynamic stochastic general equilibrium models. The proposed approximate solution is analytical, log-linear, and adjusted for risk. Therefore, it is well suited to investigate economic mechanisms, describe the time series properties or estimate the model, and deal with stochastic volatility. I explain the pitfalls encountered by previous attempts to use simple approximation techniques, in particular with models featuring recursive preferences. Finally, I show the theoretical relationship between my solution and higher-order perturbation methods.     

Safe asset shortages and asset price bubbles

We build a model economy in which a shortage of safe assets can create conditions for intrinsically useless ‘safe’ bubble assets to circulate at a positive price. Our environment features infinitely lived individuals who are not subject to credit constraints but who face uninsurable idiosyncratic production risk. Bubbly equilibria exist when safe assets offer real returns below the growth rate of the economy. Bubble assets circulate at a positive price only if they offer returns which are safe relative to production returns. These ‘safe’ bubbles reduce consumption volatility but exert a contractionary effect on the economy.     

Economic policy uncertainty,oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.     

Liquidity indicators,early warning signals in banks,and financial crises

This research compares the performance of three liquidity indicators, namely liquidity ratio (LiqR), liquidity creation (LiqC) and net stable funding difference (NSFD), for sending early warning signals for distressed banks. Recent evidence has shown that LiqR appears incapable of measuring the liquidity condition of banks. However, LiqC and NSFD have not yet been fully examined. Thus, which indicator is more useful in an early warning model becomes an interesting issue. We classify distressed banks as banks that have experienced a bank run, bailout, or failure. Sample data are collected from the United States and the European Union from before and after the financial crisis. We then estimate model predictive value using the sample before the crisis to predict liquidity shortages. Evidence shows that the academic (LiqC) and officially recommended indicators (NSFD) outperform LiqR as early warning signals. Furthermore, LiqC performs best when banks actively engage in income diversification but not fund diversification. Therefore, a well income-diversified bank with high LiqC tends to have high distress probability in the next period.     

Dependencies between price duration,volatility, volume and return on the Warsaw Stock Exchange

The successive changes of asset prices are the most visible manifestation of financial markets dynamics. There exist different views about factors generating these changes, but many researchers and practitioners agree that the most important among them is the impact of information flow. According to the market microstructure theories, it depends mainly on the behavior of informed and uniformed traders. In the paper, we investigate dependencies between the possible proxies of information process： price duration and corresponding to it volume change and return. Our main objective is to answer the question about the most important factor in the process of discovering information by uniformed traders. We apply a set of models for volatility, volume and duration data. Our analysis is performed for selected equities listed on the Warsaw Stock Exchange and uses tick-by-tick data. The obtained results show that the stock liquidity on this leading stock market in Central and Eastern Europe is the most important factor influencing the process of discovering information by uninformed traders.     

Modeling common volatility characteristics and dynamic risk premia in European equity markets

The paper applies a Factor-GARCH model to evaluate the impact of the market portfolio, as a single common dynamic risk factor, on conditional volatility and risk premia for the returns on size-based equity portfolios of three major European markets; France, Germany and the United Kingdom. The results show that for the size-based portfolios the factor loading for the dynamic market factor is significant and positive but the association between the risk premia and the conditional market volatility is weak. However, the dynamic market factor is shown to explain common characteristics in the conditional variance such as asymmetry and persistence. This finding is consistent across markets and portfolio sizes.     

Endogenous participation risk in speculative markets

This paper analyses the dynamic implications of an asset-pricing model with incomplete participation due to entry costs. It is shown that heterogeneity in entry costs can lead to the existence of multiple stochastic sunspot equilibria, whereby the number of agents in the market and asset prices fluctuate endogeneously over time in the absence of fundamental uncertainty. Such asset-price fluctuations occur despite the uniqueness of the deterministic equilibrium, and thus bear no link to the usual notion of steady-state indeterminacy. In addition to excess volatility, the equilibria exhibit predictable and conditionally heteroskedastic returns.     

Asset allocation and multivariate position based trading

I add a second risky asset and a risk free bond to the univariate artificial market investigated by Lux and Marchesi (Int J Theor Appl Finance 3(4):675–702, 2000), keeping track of traders aggregate positions and wealth. Asset allocation and security selection are modeled as separate decision processes, as is common practice in financial institutions. Introducing position based trading avoids inconsistencies in traders inventories resulting from the order based setup of the original model, while preserving its ability to reproduce the stylized facts of financial return series.      

Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility

Using monthly data from 1973 through 2020, we explore whether it is possible to improve the accuracy of one-month ahead log-aggregate equity return realized volatility point forecasts by conditioning on various nonlinear crude oil price measures widely relied on in the literature. When evaluating the evidence of unconditional relative equal predictive ability as specified in Diebold and Mariano (1995), we observe that similar to well-known economic variables, such as the dividend yield, the default yield spread and the rate of inflation, we rarely observe evidence of statistical gains in relative point forecast accuracy in favor of the crude oil price-based models. However, when evaluating the evidence of conditionalrelative equal predictive ability as specified in Giacomini and White (2006), we observe that contrary to well-known economic predictors, certain nonlinear crude oil price variables, such as the one-year net crude oil price increase suggested in Hamilton (1996) offer sizable point forecast accuracy gains relative to the benchmark. These statistical gains can also be translated into economic gains.     

Forecasting volatility with time-varying leverage and volatility of volatility effects

Predicting volatility is of primary importance for business applications in risk management, asset allocation, and the pricing of derivative instruments. This paper proposes a measurement model that considers the possibly time-varying interaction of realized volatility and asset returns according to a bivariate model to capture its major characteristics: (i) the long-term memory of the volatility process, (ii) the heavy-tailedness of the distribution of returns, and (iii) the negative dependence of volatility and daily market returns. We assess the relevance of the effects of “the volatility of volatility” and time-varying “leverage” to the out-of-sample forecasting performance of the model, and evaluate the density of forecasts of market volatility. Empirical results show that our specification can outperform the benchmark HAR–GARCH model in terms of both point and density forecasts.     

Asset price dynamics with heterogeneous beliefs and local network interactions

In this paper we investigate the effects of network topologies on asset price dynamics. We introduce network communications into a simple asset pricing model with heterogeneous beliefs. The agents may switch between several belief types according to their performance. The performance information is available to the agents only locally through their own experience and the experience of other agents directly connected to them. We model the communications with four commonly considered network topologies: a fully connected network, a regular lattice, a small world, and a random graph. The results show that the network topologies influence asset price dynamics in terms of the regions of stability, amplitudes of fluctuations and statistical properties.     

Compensation for illiquidity in China: Evidence from an alternative measure

This paper shows that liquidity is an important source of priced risk in China. Using A-share stocks in Shanghai and Shenzhen Exchange over the period 2007–2017, we examine the influence of liquidity on stock returns. A new liquidity measure that captures multiple dimensions of liquidity is proposed. Fama-Macbeth cross-sectional regression shows that the expected return is negatively correlated with liquidity. Based on Fama and French (1993), we propose a five-factor pricing model by incorporating reversal factor and liquidity factor. Time-series regressions show that the liquidity factor makes significantly marginal contributions to explaining excess stock returns. The liquidity factor based on the proposed measure works better than alternative liquidity measures such as turnover, Amihud illiquidity measure and the measure in Liu (2006).     

House price risk and the hedging benefits of home ownership

Using a repeat-sales methodology, this paper finds that estimates of house price risk based on aggregate house price indices substantially underestimate the true size of house price risk. This is the result of the fact that aggregate house price indices average away the idiosyncratic volatility in house prices. Additional results show that the idiosyncratic risk exceeds the hedging benefits of home ownership. These results imply that for many home owners, owning a house may well add more price risk than it hedges away. These findings are based on a detailed dataset of individual housing transactions in the Netherlands.