Is British output growth related to its uncertainty? Evidence using eight centuries of data

We examine the empirical relationship between output variability and output growth for Britain using data for eight centuries covering the 1270 to 2014 period. Drawing on the economic history literature, we split the full sample period into four subperiods and use GARCH models to measure output growth uncertainty and estimate its effect on average growth. Within each sub-sample, we allow output growth to depend on the state of the system, for example 2-regime switching model would switch between high-growth and low-growth regimes. We find that the effect of uncertainty on growth differs depending on the existing growth regime. Low-growth regimes are associated with a negative effect of uncertainty on growth, and medium or high-growth regimes are associated with a positive effect. These findings are consistent across the four states of economic development. Our results indicate why the empirical literature to date has found mixed results when examining the effect of uncertainty on growth.     

Garch模型族在上海股市风险计量中的应用

BELLERSLEV在1986年提出了GARCH模型,该模型反映了经济变量之间的特殊的不确定形式:方差随时间变化而变化,因而在金融市场的预测和风险管理方面有着重要的应用。以上证指数的收益作为研究对象,运用GARCH-N模型、GARCH-T模型和GARCH-GED模型分析上海股市日收益率的条件异方差性,计算出日VaR值,结果表明GARCH模型对于我国的股市风险管理有较好效果。     

U.S. Banking Sector Risk in an Era of Regulatory Change: A Bivariate GARCH Approach

This paper assesses the impact of regulatory change on the risk and returns of the U.S. banking industry. The impact of five major regulatory changes on banking sector risk was assessed using daily data for eighteen major U.S. regional banks, money center banks and savings and loan type depository institutions. Risk in this case was proxied via the use of an M-GARCH model which generates time dependent conditional beta estimates. The evidence obtained suggests that the impact of deregulation and reregulation on banking sector risk is case specific. Further, the results obtained show that the market model incorporating dummy variables, which has proven so popular amongst existing studies, discards important information about the variability of beta which the time varying conditional betas capture.     

Korean Currency Crisis and Regime Change:A Multivariate GARCH Model with Bayesian Approach

Regarding the question of when the Korean currency crisis actually started,several financial time series areexamined in a multivariate time series framework with the GeneralizedAutoregressive Conditional Heteroskedastic (GARCH) process.The likelihood ratio (LR) test is used to find a structural break with aselection of breakpoints.The posterior probability function of a breakpoint is also derived.Individual time series data show the existence of several breaks since July1997.A statistically significant structural break using the multivariate GARCHmodelwas found prior to 8 November 1997, the date when the domestic currencystarted to massively devalue.     

货币增长不确定性与通货膨胀不确定性——“波动溢出”假说与实证检验

文章基于通货膨胀——通货膨胀不确定性关系的理论研究,提出货币增长不确定性向通货膨胀不确定性波动溢出的计量检验假说,并利用中国数据,运用多元GARCH模型进行实证检验。结果发现,存在货币增长不确定性显著向通货膨胀不确定性波动溢出的效应。这意味着,货币增长不确定性具有提供有关预测通货膨胀不确定性信息的能力。同时也表明,货币增长不确定性是通货膨胀不确定性的重要解释变量,其重要性不应被忽视。实证结论的政策含义是:减少货币增长不确定性是降低通货膨胀不确定性的重要途径,我国20世纪90年代中后期稳健的货币政策所带来的通货膨胀不确定性显著降低的现实支持了这个观点。     

基于VAR方法的我国股市风险管理实证研究

基于我国风险管理的实际情况,介绍了风险价值（VAR）、GARCH模型及在其框架下计算波动率和估计分布临界值的过程和步骤,在此基础上,使用GARCH模型拟合沪深300综指收益率序列的波动率,并用以预测日VAR,估计结果显示我国股市的日VAR值仍相对较高,与发达国家金融市场的发展水平仍然有很大差距,监管部门应该具备风险管理意识,提高风险管理能力,更好地防范市场风险。     

基于GARCH模型对房地产板块和金融板块的风险分析

本文运用了基于不同分布假定下的GARCH模型的VAR方法对上证指数中房地产和金融板块的风险进行了分析。结果表明金融板块比地产板块有更大的风险;正态分布分布假定下的GARCH模型能更好地反映出地产和金融板块收益率的风险特性。     

Stock market uncertainty and interest rate behaviour: a panel GARCH approach

Using a dynamic panel GARCH model for Asian countries, we find that interest rates are significantly lower when stock market uncertainty is high. Evidence of a positive relationship between stock market uncertainty and interest rate volatility is also provided.     

Volatility spillovers to the emerging financial markets during taper talk and actual tapering

On 22 May 2013, Fed chairman, Ben Bernanke surprised markets by indicating to the media that the US Fed may taper its quantitative easing programme. This set out financial volatility across the globe over the next several months that spilled over to the financial markets of emerging market economies (EMEs). It prompted many EME central banks to take varied policy actions. Looking into this widely known event, this article presents formal empirical evidence establishing that (i) conditional volatility during taper talk exceeded that during actual tapering and (ii) volatility spillovers took place ‘contemporaneously’ from the US markets to the key EMEs during this period. The results suggest importance of careful communications by advanced economy central banks and the possibility of establishing ‘rules of the monetary game’. They also suggest that in the absence of international policy coordination to contain spillovers, EME central banks should build adequate buffers and reinforce financial stability ahead of the reversal of the global interest rate cycle.     

Breaks and outliers when modelling the volatility of the U.S. stock market

This study analyses volatility persistence of the U.S. stock market, after taking into account the role of breaks and outliers. By employing a wavelet-based algorithm, it identifies several outliers which are comfortably associated with major events such as the ‘Black Monday’ and the Asian crisis. There is also evidence of clustering of breaks and a substantial variation in the properties of the identified segments.