This paper reports the effort of using agent-based mix-game model to predict financial time series. It introduces simple generic algorithm into the prediction methodology, and gives an example of its application to forecasting Shanghai Index. The results show that this prediction methodology is effective and agent-based mix-game model is a potential good model to predict time series of financial markets.

This paper investigates a two-factor affine model for the credit spreads on corporate bonds. The Þrst factor can be interpreted as the level of the spread, and the second factor is the volatility of the spread. The riskless interest rate is modeled using a standard two-factor affine model, thus leading to a four-factor model for corporate yields. This approach allows us to model the volatility of corporate credit spreads as stochastic, and also allows us to capture higher moments of credit spreads. We use an extended Kalman Þlter approach to estimate our model on corporate bond prices for 108 Þrms. The model is found to be successful at Þtting actual corporate bond credit spreads, resulting in a signiÞcantly lower root mean square error (RMSE) than a standard alternative model in both in-sample and out-of-sample analyses. In addition,key properties of actual credit spreads are better captured by the model.

This paper develops a reduced form model of interest rate swap spreads. The model accommodates both the default risk inherent in swap contracts and the liquidity difference between the swap and Treasury markets. We use an extended Kalman Þlter approach to estimate the model parameters. The model Þts the swap rates well. We then solve for the implied general collateral repo rates and use them to decompose the swap spreads into their default risk and liquidity components. This exercise shows that the default risk and liquidity components of swap spreads behave very differently: although default risk accounts for the largest share of the levels of swap spreads, the liquidity component is much more volatile. In addition, while the default risk component has been historically positive, the liquidity component was negative for much of the 1990s and has become positive since the Þnancial market turmoils in 1998.

We provide a comprehensive analysis of the out-of-sample performance of a wide variety of spot rate models in forecasting the probability density of future interest rates. While the most parsimonious models perform best in forecasting the conditional mean of many financial time series, we find that the spot rate models that incorporate conditional heteroskedasticity and excess kurtosis or heavy-tails have better density forecasts. GARCH significantly improves the modeling of the conditional variance and kurtosis, while regime switching and jumps improve the modeling of the marginal density of interest rates. Our analysis shows that the sophisticated spot rate models in the existing literature are important for applications involving density forecasts of interest rates.

We try to explain the severe under-pricing of 523 A-share IPOs in the Chinese markets from 1997 to 2001 using institutional characteristics, absolute value, and relative value of IPO. We find that relative values of IPO are critical determinants of the severe under-pricing of A-share IPOs in China. We also find that relaxing government regulation of offering price increases under-pricing, and thus conclude that the severe under-pricing of A-share IPOs in China is not caused by the government regulation of offering price. We propose a relative value theory to explain why relaxing government regulation of offering price results in higher under-pricing and find some support for the theory.

In this paper we use artificial neural network (ANN) to empirically analyze the liquidity values of the non-floating shares and the influencing factors to China’s stock market in the background of China’s listed companies split share stricture reform. We try to use a proportion which the company’s non-floating shareholders offer compensation to the floating shareholders to test the liquidity values of the non-floating shares and use MATLAP establish a feed-forward BP neural network model to analyze and forecast according to the data of the companies which have announced and actualized their split stricture reform plans. In expansion analysis, we use the perturbation method to measure the influence of these parameters on the liquidity values of the non-floating. As result, the character of the shares, the share structure and the ratio of the shares by the principal shareholder held are the main influencing factors.

Opacity (the converse of transparency) has only recently received attention as it has been considered to be linked to a series of financial crises. This study utilizes Price Waterhouse Cooper’s 2001 opacity indices and capital flow data from the World Bank and Bank for International Settlement. Capital flows are disaggregated into categories of foreign direct investment flows by multinational enterprises, portfolio capital flows and international bank lending. Regression analysis supports the idea that higher opacity leads to a reduction in capital flows, in general. The results have policy-relevant implications as countries wishing to enhance capital inflows need to reduce the level of opacity in decision-making. More interestingly, however, the investigation with opacity sub- indices shows higher capital flows, in general, were associated with higher opacity in corruption and regulatory indices corroborating some existing evidence in the FDI literature that opacity will influence the choice of entry mode rather than the actual level of flows. In addition, the paper supports the notion that Bank Assurance mechanisms are highly desirable with regard to international bank lending, as the nature of these flows means that they are more influenced by general levels of opacity and are less responsive than FDI and portfolio flows.

It is often documented, based on autocorrelation, variance ratio and power spectrum, that exchange rates approximately follow a martingale process. Because autocorrelation, variance ratio and spectrum check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insigniÞcant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange rate changes, and if so, whether nonlinear time series models admissible in the gap can outperform the martingale model in out-of-sample forecasts. Applying the generalized spectral tests of Hong (1999) to Þve major currencies, we Þnd that the changes of exchange rates are often serially uncorrelated, but there exists strong nonlinearity in conditional mean, in addition to the well-known volatility clustering. To forecast the conditional mean, we consider the linear autoregressive, autoregressive polynomial, artiÞcial neural network and functional-coefficient models, as well as their combination. The functional coefficient model allows the autoregressive coefficients to depend on investment positions via an moving average technical trading rule. We evaluate out-of-sample forecasts of these models relative to the martingale model, using four criteria– the mean squared forecast error, the mean absolute forecast error, the mean forecast trading return, and the mean correct forecast direction. White’s (2000) reality check method is used to avoid data-snooping bias. It is found that suitable nonlinear models, particularly their combination, do have superior predictive ability over the martingale model for some currencies in terms of certain forecast evaluation criteria.

This paper studies the relationship between corporate leverage and the ultimate corporate ownership structure, particularly the separation of cash flow rights and control rights. We empirically disentangle the three potential effects of the divergence of control rights from cash flow rights on corporate leverage, i.e., the non-dilution entrenchment effect, the bonding role of debt and the fear-of-financial-distress entrenchment effect. Our evidence from the East Asian corporations mainly supports the notion that controlling shareholders with relatively small ownership share tend to increase leverage out of the motive of raising external finance without diluting their shareholding dominance. The separation of cash flow rights and control rights contributes to the risk-taking tendency of the large controlling shareholders in capital structure choice.

We argue that the existing literature, which justifies banking regulation by either market failures or regulation capture, cannot explain why banking is one of the most regulated industries and why banking regulation is a relatively recent institution in market economies. We present a new theory of banking regulation based on government failure. We first explain that banking as a market institution is intrinsically stable and effective, since its unique financial structure, i.e. most funds come from deposits, makes it very difficult for a bank to be refinanced when its investment projects are unsuccessful, thereby hardening their budget constraint and disciplining the bank’s investment decisions. However, the advent of modern governments, who have both the resources and incentive to bail out failing banks, destroys the stabilizing mechanism of banking. We call this government failure. Banking regulation is an institutional resolution to the government failure by restricting banks’ investment decisions before they fail. We provide historical as well as contemporary evidence to support the theory and explore predictions of the theory that are not derived in the existing theories.