Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S.

We propose three Realized-GARCH-Kernel-type models which do not make the distribution assumptions on the return disturbance terms. We use this type of model to predict the return volatilities of the 50ETF in China and the S&P500 index in the U.S. The semiparametric kernel density estimator of our models, which captures the skewness, asymmetry and fat-tail of financial assets, performs well both statistically and economically. Our models have more predictive power than other eight comparable volatility models that need to pre-specify the distribution of the disturbance terms. Our results are robust to eight measures of realized volatility. Using option straddle strategies, we show that our models generate larger trading profits and greater Sharpe ratios than the other competing models.     

How do neighboring peer companies influence SMEs’ financial behavior?

Recent research into corporate finances has found that the financial decisions of peer companies are related. Companies tend to “kept an eye” on the decisions of other peer companies, among other things, trying to overcome the limitations caused by the lack of information. This paper further examines these interactions including geographical proximity among companies. With this aim, we use a heterogeneous Partial Adjustment Model on a sample of 12,444 small and medium Spanish manufacturing industrial companies. We find strong nonlinearities in the adjustment processes of liquidity, indebtedness and profitability ratios associated with basic characteristics of the companies such as size, technology, age or financial imbalances. Our results indicate that the influence of the environment on the financial behavior of each company, and its responsiveness, vary in function of neighbor firms’ characteristics.     

The impact of macroprudential policies on the transmission of shocks across financially integrated countries

We study the implications of macroprudential policies across countries on the transmission of shocks when international investment activities are allowed. In a two-country dynamic stochastic general equilibrium (DSGE) model in which international investors are borrowing constrained and pledge international assets, we introduce a time-varying loan-to-value (LTV) ratio that adjusts to the variation of three different financial vulnerability indicators. We examine the effect of these policies on negative productivity and borrowing capacity shocks. Although time-varying LTV ratios reduce the international propagation of the productivity shock, their response to the shock depends on the financial vulnerability indicator with which the LTV ratio changes. With a productivity shock, the adjustment of the LTV ratio to the deviation of credit or asset price helps to reverse the negative impact of the shock. With a financial shock, LTV ratios varying with a deviation of credit-to-GDP ratio or aggregate credit can mitigate the impact of a negative financial shock. Adjustment of the LTV ratios reduces the fluctuation of international investors' balance sheets, investment, and productivity. We find that countries improve their welfare when time-varying LTV ratios are in place. The magnitude of the welfare gain differs with both the financial vulnerability indicator and the shock.     

How does reinsurance and derivatives usage affect financial performance? Evidence from the UK non-life insurance industry

A three-equation structural model is applied in this study to facilitate our examination of 1994–2011 regulatory returns data on UK non-life insurers, from which we find that those insurers using more reinsurance tended to have inferior financial performance, whilst those insurers with a predisposition towards risk management tended to have used both reinsurance and derivatives. We also find that those insurers with high loss ratios were found to have inferior financial performance. Our analysis sheds some light on the relationships between financial performance, reinsurance and derivative usage.     

我国上市公司财务困境的预测模型研究

本文以我国上市公司为研究对象 ,选取了 70家处于财务困境的公司和 70家财务正常的公司为样本 ,首先应用剖面分析和单变量判定分析 ,研究财务困境出现前 5年内各年这二类公司 2 1个财务指标的差异 ,最后选定 6个为预测指标 ,应用Fisher线性判定分析、多元线性回归分析和Logistic回归分析三种方法 ,分别建立三种预测财务困境的模型。研究结果表明 :(1 )在财务困境发生前 2年或 1年 ,有 1 6个财务指标的信息时效性较强 ,其中净资产报酬率的判别成功率较高 ;(2 )三种模型均能在财务困境发生前做出相对准确的预测 ,在财务困境发生前 4年的误判率在 2 8%以内 ;(3)相对同一信息集而言 ,Logistic预测模型的误判率最低 ,财务困境发生前 1年的误判率仅为 6 .47%     

What impact do differences in financial structure have on the macro effects of bank capital requirements in the United States and Australia?

We investigate the influence of jurisdictional differences in financial structure on the economic consequences of bank capital regulation. We use two disaggregated financial computable general equilibrium models to compare the impacts of identical increases in bank capital adequacy ratios in the U.S. and Australia. In both models, this raises bank equity financing shares, and lowers banks’ risk-weighted asset holdings. Thereafter however, differences in financial structure drive contrasting outcomes: in the U.S., average costs of capital fall, stimulating real investment, while we find the opposite outcome for Australia. We attribute this to differences in the structure of bank assets (U.S. banks hold more risk-free assets) and the importance of banks as intermediaries (bank finance is more important to capital formation in Australia). This may explain why capital regulations encompass non-banks in the U.S. but not Australia.     

Fiscal deficits,banking crises and policy reversal in a semi-open economy

We study the effect of domestic policies and external shocks in a semi-open economy characterized by incomplete liberalization of the financial sector. We argue that in such transition economies stabilization programs can have a negative impact on the fiscal imbalances, offsetting to some extent the very achievement of the stabilization program. We develop a simple general equilibrium model which allows propagation of shocks in the presence of government guarantees and imperfect capital mobility. We also empirically test the impact of positive foreign interest shock on the Indian economy using a reduced form VAR approach. The econometric evidence, though broadly consistent with the main predictions of the model, suggests no significant impact of foreign interest rate shock on output and credit. We conclude that incomplete liberalization of the financial sector in transition economies has two effects. It reduces i) exposure to external financial shocks (like the current credit crisis) and ii) ability to deal with real sector shocks (which may arise from global recession in the medium term) due to endogenous policy reversals and presence of government guarantees.     

Conditional volatility and correlations of weekly returns and the VaR analysis of 2008 stock market crash

Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

企业短期贷款违约预测Bayes模型构建

目前,企业违约预测模型距离实际应用还具有一定差异,表现在:(1)模型所使用的样本基本都是配对模式,与现实情况不符;(2)模型没有考虑到误判成本的非对称性.针对以上问题,本文运用SAS统计软件对某国有商业银行的2003年全部短期贷款企业的财务数据进行分析,摒弃以往配对模式,采用全样本进行分析,筛选出11个财务比率指标作为企业信用风险评价函数的计量参数.应用Bayes判别原理,引入误判成本和先验概率,构建了一个简明的违约判别模型,经检验模型是统计有效的,判别结果也是较好的.     

Integrated Risk Management with a Filtered Bootstrap Approach

We present a multi-period risk model to measure portfolio risk that integrates market risk, credit risk and, in a simplified way, liquidity risk. Thus, it overcomes the major limitation currently shared by many risk models that are unable to give a complete picture of all portfolio risks according to a single, coherent framework. The model is based on the Filtered Bootstrap approach; hence, it captures conditional heteroskedasticity, serial correlation and non-normality in the risk factors, that is, most of the features of observed financial time series. Being a simulation risk model, it copes in a natural way with derivatives as it allows the full valuation of the probability density function of the contracts. In addition, it is a suitable and flexible way to generate future scenarios on medium‐term horizons, so this model is particularly appropriate for asset management companies.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Financial deepening and economic growth

The core of Shapley–Shubik games and general equilibrium models with a Venn diagram is applied for a theory on the role of real finance in economic growth among advanced economies. Then the dynamic computable general equilibrium (DCGE) models for Germany, France, the UK, Japan and the USA are constructed to assess the validity of the over-financing hypothesis that has reappeared after the financial crisis of 2008. Actual financial deepening ratios observed in the nonconsolidated balance sheet of the OECD exceeded by factors of 3.5, 2.4, 5.1, 11.6 and 4.8 than the optimal financial deepening ratios implied by DCGE models, respectively, in these countries because of excessive leveraging and bubbles up to 19 times of GDP which were responsible for this great recession. Containing such massive fluctuations for macroeconomic stability and growth in these economies are not possible in conventional fiscal and monetary policy models and require a DCGE analysis like this along with adoption of separating equilibrium strategy in line of Miller–Stiglitz–Roth mechanisms to avoid problem of asymmetric information in the process of financial intermediation so that the gaps between actual and optimal ratios of financial deepening remain as small as possible.     

Forward-looking and myopic regional Computable General Equilibrium models: How significant is the distinction?

We present a stylized intertemporal forward-looking model that accommodates key regional economic features, an area where the literature is not well developed. The main difference, from the standard applications, is the role of saving and its implication for the balance of payments. Though maintaining dynamic forward-looking behaviour for agents, the rate of private saving is exogenously determined and so no neoclassical financial adjustment is needed. Also, we focus on the similarities and the differences between myopic and forward-looking models, highlighting the divergences amongst the main adjustment equations and the resulting simulation outcomes.     

Detecting Some Dynamic Properties of the Euro/Dollar Exchange Rate

Financial prices and returns have been the subject of empirical and theoretical analysis for many years, and their dynamic properties and other characteristics are still of interest. Many different tools have been applied to describe financial markets. The presented paper is addressed to examine the euro/dollar exchange rate and the related financial returns in the context of detecting exact and stochastic unit roots, and in the consequence, modelling them using time varying parameters model. The estimated STUR models are compared with standard ARMA-GARCH representations. We also examine causal relationships in the Granger sense. Upon the results of causality testing, some ADL-GARCH models are built, which are further used to examine their forecasting performance.     

Asset prices and business cycles with costly external finance

This paper asks whether the asset pricing fluctuations induced by the presence of costly external finance are empirically plausible. To accomplish this, we incorporate costly external finance into a dynamic stochastic general equilibrium model and explore its implications for the properties of the returns on key financial assets, such as stocks, bonds and risky loans. We find that the mean and volatility of the equity premium, although small, are significantly higher than those in comparable adjustment cost models. However, we also show that these results require a procyclical financing premium, a property that seems at odds with the data.     

Firm investment in transition

In this paper a model based on the Euler equation of optimal capital accumulation in the presence of convex adjustment costs is developed and estimated. The theoretical model explicitly allows for differential financial status across firms. The empirical analysis uses Romanian manufacturing firm panel data to estimate dynamic investment models with the generalized method of moments (GMM‐IV) technique and tests the derived hypotheses. The results indicate that the model based on the perfect market assumptions is rejected. The version of the model that allows for differential financial status of firms by using a theoretically derived sample selection rule is not rejected by the data. Controlling for soft budget constraints, common for transition economies, further improves the performance of the model.     

Global instability in experimental general equilibrium: the Scarf example

Scarf (Int. Econ. Rev. 1 (1960) 157) proposed a model of dynamic adjustment in which the standard tatonnement price adjustment process orbits around, rather than converges to, the competitive equilibrium. Hirota (Int. Econ. Rev. 22 (1981) 461) characterized the price paths. We explore the predictions of Scarf's model in a non-tatonnement experimental double auction. The average transaction prices in each period do follow the path predicted by the Scarf and Hirota models. When the model predicts convergence the data converge; when the model predicts orbits, the data orbit in the direction predicted by the model. Moreover, we observe a weak tendency for prices within a period to follow the path predicted by the model.     

The effect of observables,functional specifications,model features and shocks on identification in linearized DSGE models

The decisions a researcher makes at the model building stage are crucial for parameter identification. This paper contains a number of applied tips for solving identifiability problems and improving the strength of DSGE model parameter identification by fine-tuning the (1) choice of observables, (2) functional specifications, (3) model features and (4) choice of structural shocks. We offer a formal approach based on well-established diagnostics and indicators to uncover and address both theoretical (yes/no) identifiability issues and weak identification from a Bayesian perspective. The concepts are illustrated by two exemplary models that demonstrate the identification properties of different investment adjustment cost specifications and output-gap definitions. Our results provide theoretical support for the use of growth adjustment costs, investment-specific technology, and partial inflation indexation.     

Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models

The restaurant industry has been facing tough challenges because of the recent economic turmoil. Although different industries face different levels of competition and therefore the likelihood of financial distress can differ for firms in different industries, scant attention has been paid to predicting restaurant financial distress. The primary objective of this paper is to examine the key financial distress factors for publicly traded U.S. restaurants for the period from 1988 to 2010 using decision trees (DT) and AdaBoosted decision trees. The AdaBoosted DT model for the entire dataset revealed that financially distressed restaurants relied more heavily on debt; and showed lower rates of increase of assets, lower net profit margins, and lower current ratios than non-distressed restaurants. A larger proportion of debt in the capital structure ruined restaurants' financial structure and the inability to pay their drastically increased debt exposed restaurants to financial distress. Additionally, a lack of capital efficiency increased the possibility of financial distress. We recommend the use of the AdaBoosted DT model as an early warning system for restaurant distress prediction because the AdaBoosted DT model demonstrated the best prediction performance with the smallest error in overall and type I error rates. The results of two subset models for full-service and limited-service restaurants indicated that the segments had slightly different financial risk factors.