过渡期产业群的演化与政策启示——以浙江永康五金产业群为例

当前我国产业集群正处于过渡时期,市场机制的完善提高了市场交易主体间扩展信任水平,从而降低基于扩展信任的交易费用水平。随着集群规模不断扩大,理性的企业会选择更多地与传统集群外有竞争力的企业进行交易,从导致产业集群由以血缘与亲缘等为纽带的传统集群向基于扩展信任的现代集群演进。本文以浙江永康五金产业群为例对此进行了分析,提出了产业集群发展的几点方略。     

涨跌停机制的绩效:上海股市的经验分析

一直以来,理论界和实务界对证券市场实施涨跌停机制的效果存在争议.本文以改进了的Kim和Rhee(1997)的研究方法为基础,结合上海股市的个股价格资料,从波动率溢出假设、延迟价格发现假设和交易干涉假设三个方面对上海股市涨跌停机制的绩效进行实证考察.研究结果表明,中国股市的涨跌停机制是缺乏效率的.文章的最后提出了放宽价格限制幅度和采取不对称的涨跌停机制的政策建议.     

Short-time at-the-money skew and rough fractional volatility

The Black–Scholes implied volatility skew at the money of SPX options is known to obey a power law with respect to the time to maturity. We construct a model of the underlying asset price process which is dynamically consistent to the power law. The volatility process of the model is driven by a fractional Brownian motion with Hurst parameter less than half. The fractional Brownian motion is correlated with a Brownian motion which drives the asset price process. We derive an asymptotic expansion of the implied volatility as the time to maturity tends to zero. For this purpose, we introduce a new approach to validate such an expansion, which enables us to treat more general models than in the literature. The local-stochastic volatility model is treated as well under an essentially minimal regularity condition in order to show such a standard model cannot be dynamically consistent to the power law.     

Oil price shocks and volatility spillovers in the Nigerian sovereign bond market

This paper investigates volatility spillover in the Nigerian sovereign bond market arising from oil price shocks, using Vector Autoregressive Moving Average ‐ Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (VARMA‐AGARCH) model. The paper covers the period March 22, 2011 to April 14, 2016 and makes use of the daily data of the Nigerian Sovereign Bond, Brent oil and West Texas Intermediate (WTI), respectively. We endogenously and sequentially detect structural break points using the test of Bai and Perron (2003) framework. In order to accurately estimate the model, we modify it by incorporating the break points into the VARMA‐AGARCH model, a process which if ignored would lead to model misspecification. The results obtained demonstrate a significant cross‐market volatility transmission between oil and sovereign bond market with ample sensitivity to structural breaks. The study also computes optimum weight portfolio and hedge ratio both with and without structural breaks and results equally indicate sensitivity to structural breaks.     

What drives idiosyncratic volatility over time?

We document the patterns of market-wide and firm-specific volatility in the Portuguese stock market over the 1991–2005 period and test several explanations for the behavior of firm-level idiosyncratic volatility. Unlike previous studies we find no evidence of a statistically significant rise in firm-specific volatility. On the contrary, the ratio of firm-specific risk to total risk slightly decreases. We show that this result stems from new listings of large privatized companies that display lower firm-specific risk. Our findings are consistent with the idea that changes in idiosyncratic volatility are related to changes in the composition of the market.

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Probabilistic Models for Bacterial Taxonomy

We give a survey of different partitioning methods that have been applied to bacterial taxonomy. We introduce a theoretical framework, which makes it possible to treat the various models in a unified way. The key concepts of our approach are prediction and storing of microbiological information in a Bayesian forecasting setting. We show that there is a close connection between classification and probabilistic identification and that, in fact, our approach ties these two concepts together in a coherent way.     

The impact of the French Tobin tax

We analyze the impact of the introduction of the French Tobin tax on the turnover and measures of the liquidity and volatility of the affected stocks with nonparametric tests on individual stocks, difference-in-difference tests and other robustness checks controlling for simultaneous month-of-the-year and size effects. Our findings indicate that the tax produces a significant reduction in turnover and volatility (measured in terms of stock price volatility and the high–low price range) and inconclusive effects on liquidity when the latter is evaluated under the two dimensions of the estimated bid–ask spread and the Amihud (2002) price impact ratio.     

How to make Dupire’s local volatility work with jumps

There are several (mathematical) reasons why Dupire’s formula fails in the non-diffusion setting. And yet, in practice, ad-hoc preconditioning of the option data works reasonably well. In this note, we attempt to explain why. In particular, we propose a regularization procedure of the option data so that Dupire’s local vol diffusion process recreates the correct option prices, even in manifest presence of jumps.     

Pricing equity options everywhere

Finite difference methods are a popular technique for pricing American options. Since their introduction to finance by Brennan and Schwartz their use has spread from vanilla calls and puts on one stock to path-dependent and exotic options on multiple assets. Despite the breadth of the problems they have been applied to, and the increased sophistication of some of the newer techniques, most approaches to pricing equity options have not adequately addressed the issues of unbounded computational domains and divergent diffusion coefficients. In this article it is shown that these two problems are related and can be overcome using multiple grids. This new technique allows options to be priced for all values of the underlying, and is illustrated using standard put options and the call on the maximum of two stocks. For the latter contract, I also derive a characterization of the asymptotic continuation region in terms of a one-dimensional option pricing problem, and give analytic formulae for the perpetual case.