Speculative dynamics and price behavior in the Shanghai Stock Exchange

This article examines the extent to which the trading behavior of heterogeneous investors manifests in stock price changes of asset portfolios which constitute the Shanghai Stock Exchange. There are three major findings that materialize. Firstly, reliable statistical evidence of a negative relation between the conditional first and second moments of the return distributions of stock prices lends support to the volatility feedback effect. Secondly, ‘feedback’, or momentum-type investors, are not present in this market as is often detected from the daily price changes of other industrialized markets. Finally, trade volume as a proxy for ‘information-driven’ trading suggests that such investors play a statistically significant role in stock price movements. Parameter estimates from this latter group of investors imply that a rise in stock prices from a high volume trading day is more likely than a rise resulting from a low volume trading day.     

Do the Forward Sales of Real Estate Stabilize Spot Prices?

We examine the effect of forward sale (pre-sale) activities on the volatility of spot prices in the real estate market. The abundance of pre-sales data and major changes in regulatory control on the pre-sale market during the 90's in Hong Kong allow us to undertake empirical tests using Hong Kong's real estate data. Our results show that the volatility of spot prices increased significantly after forward sales were severely dampened by regulatory control measures introduced in 1994, but decreased again when the measures were partly relaxed in 1998. The results contribute to the long lasting debate on whether the introduction of a futures market reduces the volatility of spot prices. Previous studies were mainly conducted in markets with low transaction costs, notably financial markets. By utilizing the unique regulatory changes in the pre-sale market of Hong Kong, we are able to conduct an experiment on the conditional volatility of spot prices in a high information-cost environment, thereby shedding light on the important role of forward housing contracts in providing price expectation information for spot trading.     

The magnet effect of price limits: A logit approach

We investigate the magnet effect of price limits using transaction data from the Taiwan Stock Exchange. A logit model incorporates explanatory variables from microstructure literature and reveals that the conditional probability of a price increase (decrease) increases significantly when the price approaches the upper (lower) price limit, in support of the magnet effect. Our approach recognizes when the magnet effect starts to emerge and identifies possible determinants of magnet effect. The probability of information-based trading has a significant impact on the magnet effect for lower price limits.     

Nonparametric prediction for the time-dependent volatility of the security price

In this paper we consider a continuous time model for the security price with the time-dependent volatility. It is shown that the non-normality and non-linear dependency of the short-term return, the major characteristics observed on many financial assets, can be incorporated into our model. In order to evaluate the option price formula on the model we propose a nonparametric predictor for the volatility function without reference to a specific functional form. We examine the so-called continuous record asymptotics and show that the proposed predictor is asymptotically minimax for a wide class of the volatility functions. One of the most important results is that the application of the Black-Scholes method can be justified by plugging the proposed predictor in the standard Black-Scholes formula even if the volatility changes over time.     

Performance-weighted ensembles of random forests for predicting price impact

For any large player in financial markets, the impact of their trading activity represents a substantial proportion of transaction costs. This paper proposes a novel machine learning algorithm for predicting the price impact of order book events. Specifically, we introduce a prediction system based on ensembles of random forests (RFs). The system is trained and tested on depth-of-book data from the BATS and Chi-X exchanges and performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression. The results show that recency-weighted ensembles of RFs produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Feature importance ranking is used to explore the significance of various market features on the price impact, finding them to be highly variable through time. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process.     

A general framework for the derivation of asset price bounds: an application to stochastic volatility option models

We present a generalization of Cochrane and Saá-Requejo’s good-deal bounds which allows to include in a flexible way the implications of a given stochastic discount factor model. Furthermore, a useful application to stochastic volatility models of option pricing is provided where closed-form solutions for the bounds are obtained. A calibration exercise demonstrates that our benchmark good-deal pricing results in much tighter bounds. Finally, a discussion of methodological and economic issues is also provided.      

An information-based model of target stock price runup in the market for corporate control

The stock price runup of target firms in the market for corporate control has been anecdotally attributed to inside trading. Moreover, the empirical merger and acquisitions literature documents a time-varying level and duration of the stock price runup of target firms. Using a market microstructure approach, we model stock price runup as a stochastic process that shifts between a random walk without drift and a predictable process dependent on a parsimonious set of state variables. Consistent with the market microstructure literature, predictability in prices can be exploited only by the informed trader. The model is capable of explaining the complex stylized facts observed in stock price runup. It is also consistent with the merger wave literature, as we find that capital liquidity, economic growth, and market valuations drive the complex dynamics of stock price runup.     

FX volatility forecasts and the informational content of market data for volatility

The paper examines the medium-term forecasting ability of several alternative models of currency volatility. The data period covers more than eight years of daily observations, January 1991 to March 1999, for the spot exchange rate, 1- and 3-month volatility of the DEM/JPY, GBP/DEM, GBP/USD, USD/CHF, USD/DEM and USD/JPY. Comparing with the results of ‘pure’ time series models, the reported work investigates whether market implied volatility data can add value in terms of medium-term forecasting accuracy. This is done using data directly available from the marketplace in order to avoid the potential biases arising from ‘backing out’ volatility from a specific option pricing model. On the basis of the over 34 000 out-of-sample forecasts produced, evidence tends to indicate that, although no single volatility model emerges as an overall winner in terms of forecasting accuracy, the ‘mixed’ models incorporating market data for currency volatility perform best most of the time.     

Implications of market microstructure for realized variance measurement

Volatility measuring and estimation based on intra-day high-frequency data has grown in popularity during the last few years. A significant part of the research uses volatility and variance measures based on the sum of squared high-frequency returns. These volatility measures, introduced and mathematically justified in a series of papers by Andersen et al. [1999. (Understanding, optimizing, using and forecasting) realized volatility and correlation. Leonard N. Stern School Finance Department Working Paper Series, 99-061, New York University; 2000a. The distribution of realized exchange rate volatility. Journal of the American Statistical Association 96, no. 453: 42–55; 2000b. Exchange rate returns standardized by realized volatility are (nearly) Gaussian. Multinational Finance Journal 4, no. 3/4: 159–179; 2003. Modeling and forecasting realized volatility. NBER Working Paper Series 8160.] and Andersen et al. 2001a. Modeling and forecasting realized volatility. NBER Working Paper Series 8160., are referred to as ‘realized variance’. From the theory of quadratic variations of diffusions, it is possible to show that realized variance measures, based on sufficiently frequently sampled returns, are error-free volatility estimates. Our objective here is to examine realized variance measures, where well-documented market microstructure effects, such as return autocorrelation and volatility clustering, are included in the return generating process. Our findings are that the use of squared returns as a measure for realized variance will lead to estimation errors on sampling frequencies adopted in the literature. In the case of return autocorrelation, there will be systematic biases. Further, we establish increased standard deviation in the error between measured and real variance as sampling frequency decreases and when volatility is non-constant.     

The dynamic effects of monetary policy: A structural factor model approach

A structural factor model for 112 US monthly macroeconomic series is used to study the effects of monetary policy. Monetary policy shocks are identified using a standard recursive scheme, in which the impact effects on both industrial production and prices are zero. The main findings are the following. First, the maximal effect on bilateral real exchange rates is observed on impact, so that the “delayed overshooting” puzzle disappears. Second, after a contractionary shock prices fall at all horizons, so that the price puzzle is not there. Finally, monetary policy has a sizable effect on both real and nominal variables.     

On the short-time behavior of the implied volatility for jump-diffusion models with stochastic volatility

In this paper we use Malliavin calculus techniques to obtain an expression for the short-time behavior of the at-the-money implied volatility skew for a generalization of the Bates model, where the volatility does not need to be a diffusion or a Markov process, as the examples in Sect. 7 show. This expression depends on the derivative of the volatility in the sense of Malliavin calculus. E. Alòs’ research is supported by grants MEC FEDER MTM 2006 06427 and SEJ2006-13537. J.A. León’s research is partially supported by the CONACyT grant 45684-F. J. Vives’ research is supported by grant MEC FEDER MTM 2006 06427.     

Unemployment as excess supply of labor: Implications for wage and price inflation

The introduction of both market-clearing wages and nominal rigidities on wage setting can be used to rationalize unemployment as excess supply of labor in the New Keynesian model. As a result, wage inflation dynamics are forward-looking and depend negatively on the rate of unemployment. Moreover, both price inflation and wage inflation evolve as indicated by equations equivalent to those obtained in Erceg et al. (2000), though with different slope coefficients. In an equal-volatility comparison, the model with unemployment conveys less price stickiness and more wage stickiness.     

机构投资者影响我国股价波动的实证研究

采用 TGARCH 模型对机构投资者与我国股指波动的关系进行研究,实证结果表明：无论是否考虑宏观经济因素对股票市场的影响,机构投资者对我国股票市场波动均产生正向影响。进一步用面板数据模型对机构投资者与上市公司股价波动的关系进行研究,发现机构投资者在不同宏观经济环境下也均未起到稳定上市公司股价波动的作用。     

Implications of Debt Renegotiation for Optimal Bank Policy and Firm Behavior

This paper analyzes the problems associated with the renegotiation of debt contracts involving a bank (the lender) and a firm (the borrower) when the latter is operated by a risk averse manager. Firms undertake risky projects with loan capital borrowed from the bank. When a firm cannot pay off a loan it is technically bankrupt. Both the borrower and the lender may however experience a Pareto-improvement in their positions by renegotiating the loan. By renegotiating the terms of the debt the financially distressed firm can avoid the stigmatization of bankruptcy and the bank can avoid the costs of seizing the borrower's assets. However, our main finding is that, from the bank's point of view, renegotiating as a policy of recovering loan payments may be inefficient in practice because of false bankruptcy claims and moral hazard problems associated with exposure of the borrowing firm to the risk of default. We present a solution to the false bankruptcy claim problem that involves a mixe d strategy between asset seizure by the bank and debt renegotiation.     

Stock Price Reaction to Evidence of Earnings Management: Implications for Supplementary Financial Disclosure

We condition security price reactions to quarterly earnings announcements on whether firms disclose supplementary balance sheet and/or cashflow information that can be used to estimate the consequences of earnings management. Disclosure of supplementary information is voluntary, and thus, we consider the possibility that firms that disclose balance sheet and/or cashflow information differ systematically from firms that do not disclose. Results indicate that investors discount evidence of earnings management at the disclosure date when supplementary information is disclosed. Such results indicate more informed earnings interpretations of quarterly earnings when firms provide balance sheet and/or cashflow information concurrently.

The commodity super price cycle and real options: Implications for the Greeks of mining firms

The Real Options Approach (ROA) to the management and valuation of mining firms should impart a distinctive pattern to the time path of the Greeks displayed by such firms during the recent price super cycle. This paper simulates the delta, gamma, vega and rho of a gold mining firm holding a portfolio of heterogeneous mines over the recent gold price cycle, to find out the telltale signs that the ROA should leave on the trajectories exhibited by such variables during that period. We show that the ROA and the standard NPV approach to mine management and valuation predict markedly different trajectories for the Greeks.     

发达国家促进文化产业发展税收政策及其借鉴

本文介绍了发达国家促进文化产业发展的各项税收优惠政策,并分析了我国现行税收政策在促进文化产业发展中存在的问题,提出了促进我国文化产业发展的税收政策建议。     

A Simple Multi-Factor,Time-Dependent-Parameter Model for the Term Structure of Interest Rates

In this paper, we present a simple version of the Duffie and Kan model (1996). Our model can perfectly fit the yield curve and the volatility curve and further provide true closed form solutions to the pure discount bond price and its European contingent claims. Due to the specific factor structure in our model, the calibration exercise is easy to implement. This advantage will improve the computational efficiency in pricing American style claims.     

Aggregation of Nonparametric Estimators for Volatility Matrix

An aggregated method of nonparametric estimators based on time-domainand state-domain estimators is proposed and studied. To attenuatethe curse of dimensionality, we propose a factor modeling strategy.We first investigate the asymptotic behavior of nonparametricestimators of the volatility matrix in the time domain and inthe state domain. Asymptotic normality is separately establishedfor nonparametric estimators in the time domain and state domain.These two estimators are asymptotically independent. Hence,they can be combined, through a dynamic weighting scheme, toimprove the efficiency of volatility matrix estimation. Theoptimal dynamic weights are derived, and it is shown that theaggregated estimator uniformly dominates volatility matrix estimatorsusing time-domain or state-domain smoothing alone. A simulationstudy, based on an essentially affine model for the term structure,is conducted, and it demonstrates convincingly that the newlyproposed procedure outperforms both time- and state-domain estimators.Empirical studies further endorse the advantages of our aggregatedmethod.