A Complete Nonparametric Event Study Approach

Event studies have been used to examine the direction, magnitude, and speed of security price reactions to various phenomenon. Concerns over the lack of normality in stock return distributions motivated the introduction of nonparametric test statistics in the event study literature. A parametric procedure (OLS), however, has been extensively employed in the estimation of parameters for the market model. This paper, in contrast, applies Theil's nonparametric regression in the estimation of abnormal returns; an approach which is distribution free and provides a complete nonparametric approach for the detection of abnormal performance. Simulation results indicate Theil's estimation procedure offers a slight improvement in power in the detection of abnormal performance over the traditionally employed methodology. The results suggest employing Theil's nonparametric estimation procedure combined with the rank statistic. This complete nonparametric combination offers similar power with fewer underlying assumptions.     

Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation

Although stock prices fluctuate, the variations are relatively small and are frequently assumed to be normally distributed on a large time scale. But sometimes these fluctuations can become determinant, especially when unforeseen large drops in asset prices are observed that could result in huge losses or even in market crashes. The evidence shows that these events happen far more often than would be expected under the generalised assumption of normally distributed financial returns. Thus it is crucial to model distribution tails properly so as to be able to predict the frequency and magnitude of extreme stock price returns. In this paper we follow the approach suggested by McNeil and Frey in 2000 and combine GARCH-type models with the extreme value theory to estimate the tails of three financial index returns – S&P 500, FTSE 100 and NIKKEI 225 – representing three important financial areas in the world. Our results indicate that EVT-based conditional quantile estimates are more accurate than those from conventional GARCH models assuming normal or Student's t distribution innovations when doing not only in-sample but also out-of-sample estimation. Moreover, these results are robust to alternative GARCH model specifications. The findings of this paper should be useful to investors in general, since their goal is to be able to forecast unforeseen price movements and take advantage of them by positioning themselves in the market according to these predictions.     

Simulated testing of nonparametric measure changes for hedging European options

We test the accuracy and hedging performance of the deltas given by a range of nonparametric measure changes. The nonparametric models accurately estimate deltas across a number of asset price dynamics. The optimal nonparametric measure change displays superior estimation bias, which depends on how the models capture the stylised features of the dynamics, moneyness, and time-to-expiry. Differences in estimation error appear negligible. The optimal measure change produces superior static hedging outcomes compared to the Black–Scholes model. Differences in dynamic hedging outcomes are negligible.     

Managing extreme risks in tranquil and volatile markets using conditional extreme value theory

Financial risk management typically deals with low-probability events in the tails of asset price distributions. To capture the behavior of these tails, one should therefore rely on models that explicitly focus on the tails. Extreme value theory (EVT)-based models do exactly that, and in this paper, we apply both unconditional and conditional EVT models to the management of extreme market risks in stock markets. We find conditional EVT models to give particularly accurate Value-at-Risk (VaR) measures, and a comparison with traditional (Generalized ARCH (GARCH)) approaches to calculate VaR demonstrates EVT as being the superior approach both for standard and more extreme VaR quantiles.     

Oil Price and Stock Market Synchronization in Gulf Cooperation Council Countries

Knowing that the Gulf Cooperation Council (GCC) economies are dichotomous in nature, and growth in the non-oil sector is tributary to the oil sector, we document the extent of synchronization between crude oil prices and stock markets for each of the GCC markets and for the GCC as an economic bloc. We use both the bivariate and multivariate nonparametric synchronicity measures proposed by Mink et al. (2007) to assess that linkage. We find a low to mild (mild to strong) degree of synchronization between oil price and stock market returns (volatilities). In a very few instances, we find very strong (above 80 percent) associations between these variables. These results hold irrespective of whether we assume that stock market participants form adaptive or rational expectations about the price of oil. Dynamic factor results confirm that shocks to volatility are more important than shocks to oil price returns for the GCC stock markets.     

Estimating the Volatility of Discrete Stock Prices

This paper introduces an estimator of stock price volatility that eliminates, at least asymptotically, the biases that are caused by the discreteness of observed stock prices. Assuming that the observed stock prices are continuously monitored, an estimator is constructed using the notion of how quickly the price changes rather than how much the price changes. It is shown that this estimator has desirable asymptotic properties, including consistency and asymptotic normality. Also, through a simulation study, the authors show that it outperforms natural estimators for the low- and middle-priced stocks. Furthermoret, he simulation study demonstratest hat the proposed estimator is robust to certain misspecifications in measuring the time between price changes.     

On the origin of power-law tails in price fluctuations

We analyse large stock price changes of more than five standard deviations for (i) TAQ data for the year 1997 and (ii) order book data from the Island ECN for the year 2002. We argue that a large trading volume alone is not a sufficient explanation for large price changes. Instead, we find that a low density of limit orders in the order book, i.e. a small liquidity, is a necessary prerequisite for the occurrence of extreme price fluctuations. Taking into account both order flow and liquidity, large stock price fluctuations can be explained quantitatively.     

Forward-looking portfolio selection with multivariate non-Gaussian models

In this study, we suggest a portfolio selection framework based on time series of stock log-returns, option-implied information, and multivariate non-Gaussian processes. We empirically assess a multivariate extension of the normal tempered stable (NTS) model and of the generalized hyperbolic (GH) one by implementing an estimation method that simultaneously calibrates the multivariate time series of log-returns and, for each margin, the univariate observed one-month implied volatility smile. To extract option-implied information, the connection between the historical measure P and the risk-neutral measure Q, needed to price options, is provided by the multivariate Esscher transform. The method is applied to fit a 50-dimensional series of stock returns, to evaluate widely known portfolio risk measures and to perform a forward-looking portfolio selection analysis. The proposed models are able to produce asymmetries, heavy tails, both linear and non-linear dependence and, to calibrate them, there is no need for liquid multivariate derivative quotes.     

Corporate name changes: Price reactions and long-run performance

Stock price reactions and long-run performance after a corporate name change are investigated using a sample of Hong Kong listed companies spanning 1999 to 2008. Corporate name changes are classified into four types. Investors react positively around the announcement date to changes announced as being due to a merger or acquisition, a restructuring or a change in business type. Name changes to provide clarity or for reputational reasons generate no stock price reaction. No abnormal trading activity is detected around the announcement and in the post-event period. There is very weak evidence of a relationship between long-run abnormal stock returns, operating performance changes and corporate name changes. The results suggest that name changes have short-term stock price effects but no long-term relationship with stock price or operating performance.     

Capital investment and momentum strategies

The main purpose of this paper is to investigate whether capital investment can affect stock price momentum. We provide empirical evidence that momentum strategies tend to be more profitable for stocks with large capital investment or investment changes. We present a simple explanation for our empirical results and show that our finding is consistent with the behavioral finance theory that characterizes investors?? increased psychological bias and the more limited arbitrage opportunity when the estimation of firm value becomes more difficult or less accurate.     

Zero-leverage policy and stock price crash risk: Evidence from Korea

This study examines the difference in stock price crash risk between zero-leverage and non-zero-leverage firms. We find that zero-leverage firms have a significantly higher future stock price crash risk than non-zero-leverage firms. Next, we find that the positive relation between zero-leverage policy and future stock price crash risk is more pronounced when firms have higher controlling shareholders' ownership and foreign ownership. We also find that the positive relation is more pronounced for firms with low cash holdings than for those with high cash holdings. Further, we find that the positive relation is stronger for dividend-paying firms than non-dividend-paying firms. Our results are robust to alternative estimation specifications and endogeneity concerns. Overall, our findings shed light on the extent to which extreme corporate financial policy has an impact on future stock price crash risk. Our empirical evidence also provides meaningful implications for how stakeholders (especially investors) predict stock price crash risk in the context of extremely conservative capital structure.     

Estimating the basis risk of index-linked hedging strategies using multivariate extreme value theory

This paper studies the empirical quantification of basis risk in the context of index-linked hedging strategies. Basis risk refers to the risk of non-payment of the index-linked instrument, given that the hedger’s loss exceeds some critical level. The quantification of such risk measures from empirical data can be done in various ways and requires special consideration of the dependence structure between the index and the company’s losses as well as the estimation of the tails of a distribution. In this context, previous literature shows that extreme value theory can be superior to traditional methods with respect to estimating quantile risk measures such as the value at risk. Thus, the aim of this paper is to conduct an empirical analysis of basis risk using multivariate extreme value theory and extreme value copulas to estimate the underlying risk processes and their dependence structure in order to obtain a more adequate picture of basis risk associated with index-linked hedging strategies. Our results emphasize that the application of extreme value theory leads to better fits of the tails of the marginal distributions in the considered stock price sample and that traditional methods in regard to estimating marginal distributions tend to overestimate basis risk, while basis risk can in contrast be higher when taking into account extreme value copulas.     

Systematic risk, and oil price and exchange rate sensitivities in Asia-Pacific stock markets

This paper examines the relationship between beta risk and realized stock index return in the presence of oil and exchange rate sensitivities for 15 countries in the Asia-Pacific region using the international factor model. Thirteen of the 15 countries have the expected beta signs and show significant sensitivity to domestic risk when the world stock market is in both up and down modes. In terms of oil sensitivity, only the Philippines and South Korea are oil-sensitive to changes in the oil price in the short run, when the price is expressed in local currency only. Basically no country shows sensitivity to oil price measured in US dollar regardless whether the oil market is up or down. Nine countries are affected by changes in the exchange rate. In terms of relative factor sensitivity distribution, one is willing to conclude that these stock markets are more conditionally sensitive to local currency oil price changes than to beta risk wherever the relationships are significant.     

Selectivity,Quality Adjustment and Mean Reversion in the Measurement of House Values*

This paper develops a model of price formation in the housing market which accounts for the non-random selection of those dwellings sold on the market from the stock of existing houses. The model we develop also accounts for changes in the quality of dwellings themselves and tests for mean reversion in individual house prices. The model is applied to a unique body of data representing all dwellings sold in Sweden's largest metropolitan area during the period 1982–1999. The analysis compares house price indices that account for selectivity, quality change and mean reversion with the conventional repeat sales models used to describe the course of metropolitan housing prices. We find that the repeat sales method yields systematically large biased estimates of the value of the housing stock. Our comparison suggests that the more general approach to the estimation of housing prices or housing wealth yields substantially improved estimates of the course of housing prices and housing wealth.     

Semiparametric estimation of a characteristic-based factor model of common stock returns

We introduce an alternative version of the Fama–French three-factor model of stock returns together with a new estimation methodology. We assume that the factor betas in the model are smooth nonlinear functions of observed security characteristics. We develop an estimation procedure that combines nonparametric kernel methods for constructing mimicking portfolios with parametric nonlinear regression to estimate factor returns and factor betas simultaneously. The methodology is applied to US common stocks and the empirical findings compared to those of Fama and French.     

房价预期与城镇居民家庭股市参与——理论探讨与微观经验证据

本文基于中国家庭金融调查数据,通过构建理论模型和实证检验分析了房价预期与家庭股市参与的关系,考察了行为金融偏差在房价预期影响股市参与过程中的作用,并根据背景风险、社会网络和户主特征进行异质性分析。结果表明:(1)房价上涨预期通过降低居民家庭的股票收益率预期和增加住房资产,进而降低居民家庭的股市参与概率和参与程度;(2)“心理账户”以及“有限关注”的存在显著弱化了房价上涨预期对家庭股市参与的负向作用;(3)房价上涨预期对股市参与概率和参与程度的负向作用在收入风险更高、健康状况更差、社会网络水平较低以及受教育程度偏低的家庭中更大。因此,稳定房价预期能够通过提升家庭股市参与,进而从需求角度促进股票市场的健康发展。     

Cointegration and causality between macroeconomic variables and stock market returns

The purpose of this study is to investigate whether current economic activities in Korea can explain stock market returns by using a cointegration test and a Granger causality test from a vector error correction model. This study finds that the Korean stock market reflects macroeconomic variables on stock price indices. The cointegration test and the vector error correction model illustrate that stock price indices are cointegrated with a set of macroeconomic variables—that is, the production index, exchange rate, trade balance, and money supply—which provides a direct long-run equilibrium relation with each stock price index. However, the stock price indices are not a leading indicator for economic variables, which is inconsistent with the previous findings that the stock market rationally signals changes in real activities.     

Risk Attitude and Housing Wealth Effect

This paper examines whether the housing wealth effect—the consumption change induced by house price appreciation is dependent upon households’ attitudes toward risk. A simple theoretical model is introduced to highlight a negative relationship between the wealth effect and risk aversion. The paper empirically tests for this negative relationship, using data from the U.S. Consumer Expenditure Survey. The investigation involves two steps. In the first step, we make use of households’ demographics and their risky and liquid asset holdings to estimate risk aversion. The Heckman correction model is applied to address the issue of limited stock market participation. For the second step, we construct pseudo panel data through grouping households by their birth years and their predicted values of risk aversion, and then, we estimate the responses of households’ consumption changes to house price fluctuations by risk-attitude group. Consistent with the prediction of the theoretical model, the estimation results suggest a significant negative relationship between the housing wealth effect and households’ risk attitudes. Households, who are less risk averse, experience greater consumption changes in response to house price appreciation.     

China,Japan and the US Stock Markets and the Global Financial Crisis

In this paper, while focusing on the impact that the global financial crisis had on the stock markets of China, Japan, and the United States, the stock-price volatilities and linkage between these three countries are analyzed. In addition, the relationships between macroeconomic variables (real-economy variables and monetary-policy variables) and stock price volatility in each country are investigated. The estimation results of the EGARCH model revealed that although China’s stock price volatility was far greater than those of Japanese and US stock prices, China was less affected by the global financial crisis in 2007 than Japan and the United States. For China, stock price volatility was greater in the early 1990s, shortly after the stock market had been established, than in 2007 when the global financial crisis occurred. Furthermore, it has been revealed that the linkage of Chinese, Japanese, and US stock prices has increased since the global financial crisis. Moreover, Granger causality testing revealed China’s real-economy variables and monetary-policy variables do not affect China’s stock price volatility.