Volatility Spillover in Regional Emerging Stock Markets: A Structural Time-Series Approach

Volatility spillovers among the stock markets of Bahrain, Kuwait, and Saudi Arabia are investigated using the concept of stochastic volatility and structural time-series modeling. The results reveal volatility spillovers, in which the Kuwait market plays the major role. It is also found that volatility in one market cannot be explained fully in terms of volatility in the other two markets, but that, out of the three markets, the Kuwait market seems to be the most influential. Some explanations are put forward for why this is the case.     

The Impact of Mergers and Acquisitions on the Efficiency of the US Banking Industry: Further Evidence

Abstract:  Using the Stochastic Frontier Approach (SFA), this study investigates the cost and profit efficiency effects of bank mergers on the US banking industry. We also use the non-parametric technique of Data Envelopment Analysis (DEA) to evaluate the production structure of merged and non-merged banks. The empirical results indicate that mergers have improved the cost and profit efficiencies of banks. Further, evidence shows that merged banks have lower costs than non-merged banks because they are using the most efficient technology available (technical efficiency) as well as a cost minimizing input mix (allocative efficiency). The results suggest that there is an economic rational for future mergers in the banking industry. Finally, mergers may allow the banking industry to take advantage of the opportunities created by improved technology.     

中国保险业SBM效率实证分析——基于修正的三阶段DEA模型

利用修正的三阶段DEA法,实证评估2005～2007年中国保险业的SBM效率。研究表明：若忽略松弛量的影响,对外生环境变量和随机误差因素不作控制,不仅会低估保险公司的经营效率,而且还会使估计出的不同保险公司的效率相对值发生偏误;在对环境因素和随机误差进行调整后,2005～2007年保险业整体的平均SBM效率值达到0.76,比调整前上升25%;市场份额、规模、成立年数、资本结构都是对中国保险业SBM效率产生显著影响的主要环境因素。     

Earnings conservatism: panel data evidence from the European Union and the United States

This paper focuses on earnings conservatism, and provides new evidence based on procedures that account for variability at the firm level, drawing a comparison between the European Union and the United States. A key finding is that the estimated responsiveness of earnings to bad news is substantially higher when unobserved firm-specific effects are modelled. Furthermore, it is shown that accounting has become more conservative not only in the U.S. but also in the EU when taken as a whole, and there is little evidence of marked differences in the asymmetric timeliness of earnings between the two. Indeed, any changes in this property of earnings are likely to be attributable to a common factor that influences firms similarly in both locations, and not necessarily to the process of economic convergence that has taken place in the EU.     

Calibrating the exponential Ornstein–Uhlenbeck multiscale stochastic volatility model

This paper demonstrates a tractable and efficient way of calibrating a multiscale exponential Ornstein–Uhlenbeck stochastic volatility model including a correlation between the asset return and its volatility. As opposed to many contributions where this correlation is assumed to be null, this framework allows one to describe the leverage effect widely observed in equity markets. The resulting model is non-exponential and driven by a degenerate noise, thus requiring a high level of care in designing the estimation algorithm. The way this difficulty is overcome provides guidelines concerning the development of an estimation algorithm in a non-standard framework. The authors propose using a block-type expectation maximization algorithm along with particle smoothing. This method results in an accurate calibration process able to identify up to three timescale factors. Furthermore, we introduce an intuitive heuristic which can be used to choose the number of factors.     

Leverage and Volatility Feedback Effects in High-Frequency Data

We examine the relationship between volatility and past andfuture returns using high-frequency aggregate equity index data.Consistent with a prolonged "leverage" effect, we find the correlationsbetween absolute high-frequency returns and current and pasthigh-frequency returns to be significantly negative for severaldays, whereas the reverse cross-correlations are generally negligible.We also find that high-frequency data may be used in more accuratelyassessing volatility asymmetries over longer daily return horizons.Furthermore, our analysis of several popular continuous-timestochastic volatility models clearly points to the importanceof allowing for multiple latent volatility factors for satisfactorilydescribing the observed volatility asymmetries.     

How Do Investors' Expectations Drive Asset Prices?

Based on an extension of the process of investors' expectations to stochastic volatility we derive asset price processes in a general continuous time pricing kernel framework. Our analysis suggests that stochastic volatility of asset price processes results from the fact that investors do not know the risk of an asset and therefore the volatility of the process of their expectations is stochastic, too. Furthermore, our model is consistent with empirical studies reporting negative correlation between asset prices and their volatility as well as significant variations in the Sharpe ratio.     

多资产大宗商品期权定价研究

基于大宗商品收益率与便利收益服从均值回复过程的假设,建立带协整效应的多资产大宗商品期权定价模型,求解多资产大宗商品期权价格的解析解,将大宗商品期权定价推广到更一般情况.结果表明:标的资产收益率增加,期权价格上升,替代品期权价格下降;标的资产的便利收益增加,期权价格下降,相应替代品的期权价格上升.     

Almost marginal conditional stochastic dominance

Marginal Conditional Stochastic Dominance (MCSD) developed by Shalit and Yitzhaki (1994) gives the conditions under which all risk-averse individuals prefer to increase the share of one risky asset over another in a given portfolio. In this paper, we extend this concept to provide conditions under which most (and not all) risk-averse investors behave in this way. Instead of stochastic dominance rules, almost stochastic dominance is used to assess the superiority of one asset over another in a given portfolio. Switching from MCSD to Almost MCSD (AMCSD) helps to reconcile common practices in asset allocation and the decision rules supporting stochastic dominance relations. A financial application is further provided to demonstrate that using AMCSD can indeed improve investment efficiency.     

Examination of Conditional Asset Pricing in UK Stock Returns

I examine the empirical performance of various specifications of the capital asset pricing model (CAPM) in UK stock returns, using the stochastic discount framework. When the proxy for the market portfolio includes a proxy for labor income growth in addition to the stock market index, the performance of the CAPM improves. The improvement in performance shows in the magnitude and significance of the pricing errors and in the reduced impact of asset characteristics and other factors in the pricing of assets. There is further improvement when I use conditional versions of the models.     

Effectiveness of Stochastic Neural Network for Prediction of Fall or Rise of TOPIX

Stochastic neural network is a hierarchical network of stochastic neurons which emit 0 or 1 with the probability determined by the values of inputs. We have developed an efficient training algorithm so as to maximize the likelihood of such a neural network. This algorithm enables us to apply the stochastic neural network to a practical problem like prediction of fall or rise of Tokyo Stock Price Index (TOPIX). We trained it with the data from 1994 to 1996 and predicted the fall or rise of 1 day ahead of TOPIX for the period from 1997 to 2000. The result is quite promising. The accuracy of the prediction of the stochastic network is the 60.28%, although those of non-stochastic neural network, autoregressive model and GARCH model are 50.02, 51.38 and 57.21%, respectively. However, the stochastic neural network is not so advantageous over other networks or models for prediction of the TOPIX used for training. This means that the stochastic neural network is less over fitting to the training data than others, and results in the best prediction. We will demonstrate how the stochastic neural network learns well non-linear structure behind of the data in comparison to other models or networks, including Generalized Linear model (GLM).JEL codes: D24, L60, 047     

Numerical Approach to Asset Pricing Models with Stochastic Differential Utility

In this paper employing two heuristic numerical schemes, we study the asset pricing models with stochastic differential utility (SDU), which is formulated by either of backward stochastic differential equations (BSDEs) or forward-backward stochastic differential equations (FBSDEs).The first scheme is based upon a traditional lattice algorithm of option pricing theories, involving the discretization scheme of coupled FBSDEs, which is combined with a technique of solving numerically a certain type of nonlinear equations with respect to the backward state variables. The second one is based upon the four step scheme of Ma et al. (1994) which solves quasi-linear partial differential equations associated with the FBSDEs. We demonstrate that our practical implementation algorithms can successfully solve the asset pricing models with generalized SDU and the large investor problem with market impact which are typical examples such that the usual four step scheme is difficult to implement. As other numerical applications we study the optimal consumption and investment policies of a representative agent with SDU, and the recoverability of preferences and beliefs from observed consumption data.     

The Relative Cost Efficiency of Stock versus Mutual Thrifts: A Bayesian Approach

The relative cost efficiency of the mutual versus stock forms of ownership for thrifts has been a relevant issue in an era of deregulation and competition in the financial services industry. In this study, Bayesian‐based Markov chain Monte Carlo (MCMC) resampling methods are used to solve a stochastic cost frontier model and effectively determine cost efficiencies for the stock and mutual thrift groups. We find a statistically significant difference between both the cost frontiers and the cost efficiencies of the two groups, with the stock group operating at the lower‐cost point. Agency problems explain a significant portion of the cost efficiency difference. Capital structure differences, though not helping to explain differences in cost efficiency, do help to explain differences in cost structure and managerial attitudes toward risk.     

Option Approach to Search for Threshold Rice Price Toward Sustainable Paddy Field Management

This paper applies an option approach to search for the threshold rice price toward the sustainable paddy field management under rice price stochasticity. Rice price is assumed to follow geometric Brownian motion. The management model for paddy fields is a discrete stochastic dynamic programming model with binomial approximation for geometric Brownian motion, where a control variable is a decision to sustain or terminate paddy yield management. Our computational experiments indicate that an increase in rice price volatility could lower the threshold rice price for farmers to continue rice production. It is also shown that depending on the degree of rice price volatility, even under a lower price level than production costs, maintaining the management could become beneficial. Considering an option to terminate production could make the higher expected value of rice production than without it. Using 12 sets of time series data on voluntarily marketed rice produced in Hokkaido, Aomori, Iwate, Miyagi, Akita, Yamagata, Fukui, Ibaragi, Chiba, Niigata, Toyama and Nagano, the minimum threshold rice price of 6,700 Yen/60 kg was found in Chiba with the largest volatility, and the maximum of 7,250 Yen/60 kg in Ibaragi with the smallest volatility. If the market price becomes lower than the threshold rice price, some policy measures would be necessary toward sustainable paddy field management by covering the difference between them.