The effects of competitors on new product launch and market expansion in the hybrid car market

This paper evaluates the effect of competitors on automakers’ new product launch and market expansion decisions in the hybrid car market. Using data from the entire history in US hybrid car market from 2000 to 2014, this paper estimates a Poisson model with endogeneous switching to account for the initial launch and the following expansion decisions. The results indicate that the presence of competing brands’ hybrid vehicle models has a net positive effect on a brand’s initial launch and expansion decisions. This suggests that demand expansion and market learning from competitors’ hybrid model launch and consumer cultivation are very important for this relatively new technology. Further, I find that the impact of competitors varies over time, vehicle origins, and classes.     

TIME TO MARKET AND IMPATIENT CUSTOMERS

We analyse the decision of firms about when to launch their products on the market when they produce differentiated goods and compete on prices. We find two subgame perfect equilibria: one in which the high‐quality firm holds its leadership in quality, and another in which the low‐quality firm leapfrogs its rival. When the initial level of differentiation is high enough, the low‐quality firm always launches first. Finally, we extend this model to analyse commercial piracy. We obtain that pirates are highly unlikely to launch the illegal copy first because they would bear a higher penalty and a higher risk of being detected.     

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.     

The effects of the Paris climate agreement on stock markets: evidence from the German stock market

We investigate the effects of the 2016 Paris Climate Agreement on the German stock market by considering the impact of 20 announcements pertaining to the Agreement on 17 industries. The event study methodology is used for this purpose, together with several robustness tests, such as the nonparametric rank test and non-parametric conditional distribution approach. The change in systematic risk following the announcements is captured by using various risk models. In general, we find that the Paris Climate Agreement is achieving its objectives in the short run. Our results show that the announcements affected polluting industries in terms of risk and return. Furthermore, we observe two distinct diamond risk structures when (1) Conference of the Parties (COP) 21 took place, and (2) the Agreement came into force.     

The financial consequences of abolishing a carbon trading system

We investigate the impact of 61 announcements of environmental regulations on the equities listed on the Australian Stock Exchange over the period 2009–2015. In particular, our study focuses on how the stock market reacts to announcements of the abolishment of carbon trading/pricing system. Using event study methodology, we assess whether these announcements create or destroy wealth of equity investors. Furthermore, we estimate changes in systematic risk following the announcements. In general, we find that the abolishment of the carbon pricing system has a positive effect on 18 sectors and the process of removing the carbon pricing system appears to affect the systematic risk of businesses leading to diamond risk structures. We also document negative reactions of polluting sectors to the announcements of green policies.     

平均相关系数与系统性风险：来自中国市场的证据

本文研究了平均相关系数与系统性风险的关系,拓展了Pollet and Wilson(2010)的模型,找到了资产预期收益率与股票、债券平均相关系数的关系,更好地解决了系统性风险的度量问题。实证中,我们首先发现股票与债券市场的平均相关系数反映了系统性风险,而股票市场的波动并不能反映系统性风险;其次,股票投资者是风险偏好,但债券市场的投资者是风险厌恶的;最后,股票与债券的市场间相关系数未被定价,二者还具有较大的独立性。     

Long-Memory,Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets

In this paper, we address the question of whether long memory, asymmetry, and fat-tails in global real estate markets volatility matter when forecasting the two most popular measures of risk in financial markets, namely Value-at-risk (VaR) and Expected Shortfall (ESF), for both short and long trading positions. The computations of both VaR and ESF are conducted with three long memory GARCH-class models including the Fractionally Integrated GARCH (FIGARCH), Hyperbolic GARCH (HYGARCH), and Fractionally Integrated Asymmetric Power ARCH (FIAPARCH). These models are estimated under three alternative innovation’s distributions: normal, Student, and skewed Student. To test the efficacy of the forecast, we employ various backtesting methodologies. Our empirical findings show that considering for long memory, fat-tails, and asymmetry performs better in predicting a one-day-ahead VaR and ESF for both short and long trading positions. In particular, the forecasting ability analysis points out that the FIAPARCH model under skewed Student distribution turns out to improve substantially the VaR and ESF forecasts. These results may have several potential implications for the market participants, financial institutions, and the government.

     

The wealth effect and diamond risk structure of financial regulation

The objective of this article was to evaluate the effect of announcements of financial regulation on risk and return in the Vietnamese equity market. The techniques used for the purpose of analysing risk and return include event study and non-parametric tests, as well as asset pricing models supplemented with interaction variables and a variety of ARCH-like specifications such as GARCH, TARCH, EGARCH and PARCH. We find evidence for the wealth effect, the presence of delayed response and a risk shifting behaviour in the form of diamond risk structure. Our results show that abnormal returns are present around the announcements of operating rules and other stock market regulations. Abnormal returns can also be obtained after considering legal documents such as circulars and decisions.     

中国股市行业系统风险的差异性检验及扩展研究

本文实证检验中国股票市场中的行业系统风险差异性。研究发现,差异性明显存在,这校正了以往学者认为国内行业间系统风险没有差异的认识偏差。分析发现,若干行业经济变量,尤其是某些行业财务指标,对这一差异性具有较好的解释力,表明系统风险的行业结构有其合理性。通过国际比较,发现中国股市系统风险的行业结构与国际市场具有一定的相似性和关联性。在此基础上,本文扩展到实体经济与虚拟经济的对应关系上,并提出一个双面三层结构模式及其实现的政策建议。     

货币冲击、房地产收益波动与最优货币政策选择

与传统资产定价模型中风险收益权衡关系相悖,我国房地产市场存在投资异象和波动长记忆性特征。文章利用泰勒规则(Taylor Rule)的利率缺口,在剔除市场预期之后测度了中国市场的货币政策冲击,并基于房地产投资回报的时序数据波动聚集性和时变性特征构建GARCH(1,1)-M模型,以此度量我国房地产市场投资收益的波动演变路径,解释了央行实施加息的货币政策后当期房价反而上涨的投资现象。文章还立足于房地产市场参与人的投资特征,从行为金融学的全新研究视角出发,建立包含行为资产定价的动态模型经济系统,研究资产价格波动与最优货币政策选择问题,求得相应闭型解,为实施关注资产价格波动的最优货币政策提供理论基础。     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence,information transmission and market co-movement

This paper examines the price and volatility dynamics between China and major stock markets in the Asia-Pacific, investigating the effects of the Chinese stock market crash (2015–2016) for the first time. Employing the Bayesian VAR and BEKK GARCH, we observe that price and volatility spillover behaviours are different during the stable and stress periods. Particularly, price spillovers from China to other regional markets are more significant during a bullish period, showing that ‘good news’ emanating from China has strong impacts on its neighbours during better market condition. In the turbulent period, we observe strong shock spillover effects and enhanced volatility spillovers from China to most Asia-Pacific stock markets. This is because China, as an important trading partner and strategic financial centre shows to exert significant influence on the Asia-Pacific region through various economic channels. We also find that the Asia-Pacific stock markets spill over their shocks to China during the crisis, indicating that China is becoming more integrated with the regional financial markets.     

Modelling the Equity Beta Risk of Australian Financial Sector Companies

In this paper we apply the generalised auto-regressive conditional heteroskedasticity (GARCH) and Kalman Filter approaches to modelling the equity beta risk of a sample of fifteen Australian financial sector companies. A de-regulated environment in which strong competitive forces are at play typifies the period of investigation. Consistent with the existing literature, we find that these modelling techniques perform well and, in particular, that the Kalman Filter approach is preferred. Further, we find that considerable variability of risk occurs throughout the sample period. Thus, extending the evidence of Harper and Scheit (1992); Brooks and Faff (1995) and Brooks, Faff and McKenzie (1997), we find evidence consistent with the hypothesis that deregulation has impacted the risk of banking sector stocks.     

On the effects of rare disasters and uncertainty shocks for risk premia in non-linear DSGE models

This paper studies how rare disasters and uncertainty shocks affect risk premia in DSGE models approximated to second and third order. Based on an extension of the results in Schmitt-Grohé and Uribe (2004) to third order, we derive propositions for how rare disasters, stochastic volatility, and GARCH affect any type of risk premia in a wide class of DSGE models. To quantify the effects, we set up a standard New Keynesian DSGE model where total factor productivity includes rare disasters, stochastic volatility, and GARCH. We find that rare disasters increase the level of the 10-year nominal term premium, whereas a key effect of uncertainty shocks, i.e. stochastic volatility and GARCH, is an increase in the variability of this premium.     

Time-varying synchronization of European stock markets

We study intraday comovements among three developed (France, Germany, and the United Kingdom) and three emerging (the Czech Republic, Hungary, and Poland) European stock markets. When applying a Dynamic Conditional Correlation GARCH model to 5- min tick intraday stock price data (2003–2006), we find a strong correlation between the German and French markets and also between these two markets and the UK stock market. However, very little systematic positive correlation during a trading day can be detected between the developed and emerging stock markets, or within the emerging group itself. Hungary exhibits higher correlation with the developing markets and the emerging markets and its dynamics show an increasing trend; Poland and the Czech Republic produce less clear-cut results.     

The linkages,persistence, asymmetry in the volatility,the price discovery and efficiency,and the effect of the US subprime mortgage financial crisis on the spot and the futures market’s returns: the case of India

This article examines the effects of persistence, asymmetry and the US subprime mortgage crisis on the volatility of the returns and also the price discovery, efficiency and the linkages and causality between the spot and futures volatility by using various classes of the ARCH and GARCH models, and through the Granger’s causality. We have used two indices: one for spot and the other for futures, for the daily data from 12 June 2000 to 30 September 2013 from Nifty stock indices. We have then tested for ARCH effects, and subsequently employed various models of the ARCH and GARCH conditional volatility. The GARCH(1,1) model is found to be significant, and it implies that the returns are not autocorrelated and have ‘short memory’. It supports the hypothesis of the efficiency of the markets. The negative ‘news’ has more significant effect on volatility, corroborating the ‘leverage impact’ in finance on market volatility. We have also tested the volatility spillover effects. The two methods we employed support the spillover effects and the causality is bidirectional. We also have used the dummy variable for the US subprime mortgage financial crisis and found that they are statistically significant. Indian stock market is thus integrated to the world stock markets.     

Dynamic correlation and equicorrelation analysis of global financial turmoil: evidence from emerging East Asian stock markets

This study investigates the dynamic conditional correlations (DCCs) between eight emerging East Asian stock markets and the US stock market and analyses the dynamic equicorrelation among these nine stock markets. We find a significant increase in the conditional correlations and equicorrelation in the first phase of the global financial crisis. We refer to this finding as contagion from the US stock market to the emerging East Asian markets. We also find an additional significant process of increasing correlations and equicorrelation (herding) in the second phase of the global financial crisis. Further, we employ two new models, namely DCCX-MGARCH (a DCC Multivariate GARCH model with exogenous variables) and DECOX-MGARCH (a dynamic equicorrelation multivariate GARCH model with exogenous variables), to identify the channels of contagion. We find that an increase in the VIX Index increases the conditional correlations and equicorrelation, while increases in TED spreads decrease the conditional correlations of six emerging East Asian countries with the USA. We compare the accuracy of the conditional correlation estimates of the DCC and DCCX models (or DECO and DECOX models) by constructing a loss function. We find that the DCCX (DECOX) model provides more accurate conditional correlation estimates than the DCC (DECO) model by extracting additional information from exogenous variables.     

Early warning signals using AVaRs of infinitely divisible GARCH models — evidence from stock index markets

Classical time series models have failed to properly assess the risks that are associated with large adverse stock price behaviour. This article contributes to autoregressive moving average model–GARCH (ARMA–GARCH) models with standard infinitely divisible innovations and assesses the performance of these models by comparing them with other time series models that have normal innovation. We discuss the limitations of value at risk (VaR) and aim to develop early warning signal models using average value at risk (AVaRs) based on the ARMA–GARCH model with standard infinitely divisible innovations. Empirical results for the daily Dow Jones Industrial Average Index, the England Financial Times Stock Exchange 100 Index and the Japan Nikkei 225 Index reveal that estimating AVaRs for the ARMA–GARCH model with standard infinitely divisible innovations offers an improvement over prevailing models for evaluating stock market risk exposure during periods of distress in financial markets and provides a suitable early warning signal in both extreme events and highly volatile markets.     

Performance and diversification benefits of foreign-equity ETFs in emerging markets

This study provides a comprehensive review of the risk-return characteristics, performance and international diversification benefits of an uncharted fast-growing segment of the global exchange-traded fund (ETF) market by examining 17 foreign-equity ETFs traded in 6 emerging markets. The results indicate that the sample ETFs domiciled in these economies perform poorly providing relatively low returns while exposing emerging market investors to substantial total and systematic risks. In addition, these ETFs are found to be more sensitive to downside risk, making them relatively more vulnerable to market downturns. Although the foreign-equity ETFs are designed to provide investors with full international diversification benefits, we find that they are significantly affected by their local market conditions and sentiments, making them ineffective international diversification tools.     

The effects of inflation uncertainty on interest rates: a nonlinear approach

In this article, we investigate the effects of inflation variability on short-term interest rates within a nonlinear smooth transition regression framework. The test results suggest that only the conditional mean of the inflation is a nonlinear process whereas the conditional variance is time variant but linear. Using the square root of conditional variance as a proxy for inflation risk, we estimate Fisher equation augmented with inflation risk. Although the estimated Fisher equations suggest that inflation risk reduces short-term interest rates, we find that the effects of inflation risk on interest rates are regime-dependent. Particularly, we find that the negative effects of inflation variability on nominal rates are greater in low-inflationary regimes when compared to high-inflationary regimes. On the other hand, it is found that both inflation and inflation uncertainty raise the expected inflation effect.