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.     

Capital adjustment cost and inconsistency in income‐based dynamic panel models with fixed effects

After the seminal work of Nickell (1981), a vast literature demonstrates the inconsistency of ‘conditional convergence’ estimator in income‐based dynamic panel models with fixed effects when the time horizon (T) is short but the sample of countries (N) is large. Less attention is given to the economic root of inconsistency of the fixed effects estimator when T is also large. Using a variant of the Ramsey growth model with long‐run adjustment cost of capital, we demonstrate that the fixed effects estimator of such models could be inconsistent when T is large. This inconsistency arises because of the long‐run adjustment cost of capital which gives rise to a negative moving average coefficient in the error term. Income convergence will be thus overestimated. We theoretically characterize the order of this inconsistency. Our Monte Carlo simulation demonstrates that the size of the bias is substantial and it is greater in economies with higher capital adjustment costs. We show that the use of instrumental variables that take into account the presence of the negative moving average term in the error will overcome this bias.     

Evaluating the Impact of the US–China Trade War

We evaluate the impact of the US–China trade war using a dynamic computable general equilibrium (CGE) model of global trade. We conduct ex ante simulation analysis exploring three scenarios to understand how the trade war affects import tariffs, investment, and productivity. The escalation of the trade war reduces gross domestic product (GDP) in China and the USA by ?1.41% and ?1.35%, respectively. The trade war reduces nearly all sectoral imports and outputs in both countries. To reflect the important role of global value chains (GVCs), we modify the dynamic CGE model with agent‐specific import demands, and we explore the difference between the results for the two models relating to the trade war impacts on GDP and bilateral trade. When GVCs are accounted for, the negative impacts on bilateral trade are more widespread across countries, and world GDP in the modified model is reduced by ?$US450 billion. These results suggest that the GVCs play substantial role in determining trade responses at the disaggregated level.     

Increasing Returns,Financial Capital Mobility and Real Exchange Rate Dynamics*

The late 1990s saw a US IT investment boom, large capital flows into the USA and an appreciation of the US$. At the time, this appeared to be driven by expectations of continued IT‐related knowledge spillover externalities and associated productivity and profit growth. Using a two‐region dynamic general equilibrium model with externalities, we find a once‐off productivity shock leads to capital inflow and a real appreciation only in the short term. In the long term, capital flows stabilise and the real exchange rate depreciates. For a single shock to trigger long‐term growth in capital flows requires unrealistically large externalities.     

Revisiting global economic activity and crude oil prices: A wavelet analysis

Based on the wavelet analysis approach, this paper firstly examines the dynamic relationship between global economic activity (proxied by the Kilian economic index) and crude oil prices in both time- and frequency-domains. Our empirical results demonstrate significant correlation between crude oil prices and global economic activity at high frequencies (in the short run) during the entire sample period; however, the co-movement between the two at low frequencies (in the long run) is weaker and exists only during certain proportions of the sample period. We also document evidence that global economic activity and oil price are positively correlated, with dynamic lead-lag relationships across time. Our findings are robust to alternative choices of oil price indexes and controlling for other confounding factors such as geopolitical risk, armed conflicts, economic policy uncertainty and equity market uncertainty. The current study provides valuable implications for oil market investors based on the information of global economic situation and its dynamic relationships with oil prices.     

Portfolio allocation and international risk sharing

Abstract We show that recent explanations of the consumption‐real exchange rate anomaly that rely on goods and financial market frictions are not robust to introducing just one additional international asset. When portfolios are selected optimally, international trade in two nominal bonds implies a consumption‐real exchange rate correlation that is too high compared with the data even when there are many shocks. Monetary policy specification plays a potentially important role for the degree of risk sharing provided by nominal bonds, both in the benchmark model with only tradable and non‐tradable sector supply shocks and also in the model that allows for news.     

Stock return predictability: the role of inflation and threshold dynamics

This paper argues that the nature of stock return predictability varies with the level of inflation. We contend that the nature of relations between economic variables and returns differs according to the level of inflation, due to different economic risk implications. An increase in low level inflation may signal improving economic conditions and lower expected returns, while the opposite is true with an equal rise in high level inflation. Linear estimation provides contradictory coefficient values, which we argue arises from mixing coefficient values across regimes. We test for and estimate threshold models with inflation and the term structure as the threshold variable. These models reveal a change in either the sign or magnitude of the parameter values across the regimes such that the relation between stock returns and economic variables is not constant. Measures of in-sample fit and a forecast exercise support the threshold models. They produce a higher adjusted R², lower MAE and RMSE and higher trading related measures. These results help explain the lack of consistent empirical evidence in favour of stock return predictability and should be of interest to those engaged in stock market modelling as well as trading and portfolio management.     

Dynamic effects of trade openness on financial development

This paper employs the Pooled Mean Group (PMG) approach of Pesaran et al. (1999) to study the dynamic effects of trade openness on financial development. The advantage of the PMG estimator over other dynamic panel econometric techniques is that it allows short-run coefficients, speeds of adjustment and error variances to vary across countries, with cross-country homogeneity restrictions only on long-run parameters. Our results spanning 88 countries over 1960–2005 show that a positive long-run relationship between trade openness and financial development coexists with a negative short-run relationship. But when splitting the data into different income or inflation groups, this finding is observed only in relatively low-income countries or high-inflation economies.     

Unionization,international integration,and selection

Abstract We study how unionization affects competitive selection between heterogeneous firms when wage negotiations can occur at the firm or at the profit‐centre level. With productivity specific wages, an increase in union power has: (i) a selection‐softening; (ii) a counter‐competitive; (iii) a wage‐inequality; and (iv) a variety effect. In a two‐country asymmetric setting, stronger unions soften competition for domestic firms and toughen it for exporters. With profit‐centre bargaining, we show how trade liberalization can affect wage inequality among identical workers both across firms (via its effects on competitive selection) and within firms (via wage discrimination across destination markets).     

Equity home bias,incomplete financial markets,and nominal rigidities

Abstract This paper analyzes optimal portfolio decisions in a monetary open‐economy framework. It is found that market completeness and the specific form of nominal rigidities, namely, nominal price vs. nominal wage rigidities, matter for justifying the observed structure of equity holdings. When markets are complete, sticky prices generate a negative correlation between the non‐diversifiable labour income and the profit of domestic firms with respect to the productivity shocks, which explains why households invest little abroad. In contrast, when markets are incomplete, rigidities in goods prices result in a counterfactual ‘super home bias’, because domestic equities provide a good hedge against not only the labour income risk but also the relative price risk. Wage rigidities, however, have the opposite effect. Therefore, nominal rigidities in both goods prices and wage rates are needed to address the empirical composition of gross equity positions under incomplete markets.     

Uncertainty,Learning and Heterogeneity in International Environmental Agreements

This paper concerns the formation of International Environmental Agreements under uncertainty about environmental damage with different models of learning (complete learning, partial learning or no learning). The results of the existing literature are generally pessimistic: the possibility of either complete or partial learning generally reduces the level of global welfare that can be achieved from forming an IEA relative to no learning. That literature regards uncertainty as a parameter common to all countries, so that countries are identical ex ante as well as ex post. In this paper we extend the literature to the case where there is no correlation between damage costs across countries; each country is uncertain about a particular parameter (in our case the benefit-cost ratio) drawn from a common distribution but, ex post, each country’s realized parameter value is independently drawn. Consequently, while countries remain identical ex ante, they may be heterogeneous ex post. We show that this change reinforces the negative conclusions about the effects of partial learning on international environmental agreements, but, under certain conditions, moderates the negative conclusions about the effects of complete learning.     

Does aid affect inequality?

We show that there was a negative correlation between pro-poor aid and inequality in the period before the mid-2000s. This article provides an explanation for this observed relationship. Using panel data on 34 countries for the period 1996–2014, we test the hypothesis that pro-poor aid has an inequality-reducing effect. To demonstrate that the negative correlation is not spurious, we use dynamic panel techniques with alternative specifications. We show that the share of pro-poor aid has a negative effect on inequality. In addition, we show that the interaction term between the poverty rate and the change in pro-poor aid has a significant effect. These empirical results are consistent with our theoretical predictions. The results imply that aid can reduce inequality by altering the allocation of aid, rather than by increasing the amount of aid.     

Potential economic reforms in North Korea: a dynamic general equilibrium model

We use a dynamic general equilibrium model to examine hypothetical market reforms in North Korea. We model partial reform, in which producers choose capital allocations across sectors, with the government still fixing total capital. We also consider two full market reform scenarios. In one, public infrastructure investment remains unchanged, while, in the other, it increases substantially. In all scenarios, we assume a closed economy and a constant military size. Our simulations show little hope for the North Korean economy without boosting infrastructure. Although all of the reforms raise consumption, only significant increases in infrastructure investment bring positive economic growth.     

Cross-border mergers and acquisitions and the role of trade costs

Cross-border mergers and acquisitions (M&As) have increased dramatically over the last two decades. This paper analyses the role of trade costs in explaining the increase in the number of cross-border M&As. In particular, we distinguish horizontal and non-horizontal M&As and investigate whether trade costs affect these two types of mergers differently. We analyse this question using industry data for 23 OECD countries for the period 1990-2001. Our findings suggest that while in the aggregate trade costs affect cross-border merger activity negatively its impact differs importantly across horizontal and non-horizontal mergers. The impact of trade costs is less negative for horizontal mergers, which is consistent with the tariff-jumping argument.     

Disaggregated data and trade policy analysis: The value of linking partial and general equilibrium models

Computable General Equilibrium (CGE) models are now routinely utilized for the evaluation of trade policy reforms, yet they are typically quite highly aggregated, which limits their usefulness to trade negotiators who are often interested in impacts at the tariff line. On the other hand, Partial Equilibrium (PE) models, which are typically used for analysis at disaggregate levels, deprive the researcher of the benefits of an economy-wide analysis, which is required to examine the overall impact of broad-based trade policy reforms. Therefore, a PE–GE, nested modeling framework has the prospect of offering an ideal tool for trade policy analysis. In this paper, we develop a PE model that captures international trade, domestic consumption and output, using Constant Elasticity of Transformation (CET) and Constant Elasticity of Substitution (CES) structures, market clearing conditions and price linkages, nested within the standard GTAP model. In particular, we extend the welfare decomposition of Huff and Hertel (2001) to this PE–GE model in order to contrast the sources of welfare gain in PE and GE analyses. To illustrate the usefulness of this model, we examine the contentious issue of tariff liberalization in the Indian auto sector, using PE, GE and PE–GE models. Both the PE and PE–GE models show that the imports of motorcycles and automobiles change drastically with both unilateral and bilateral tariff liberalization by India, but the PE model does a poor job predicting the overall size and price level in the industry, post-liberalization. On the other hand, the GE model overestimates substitution between regional suppliers due to “false competition” and underestimates the welfare gain, due to the problem of tariff averaging in the aggregated model. These findings are shown to be robust to wide variation in model parameters. We conclude that the linked model is superior to both the GE and PE counterparts.     

Currency hedging with options and futures

This paper examines the optimal hedging decision of a competitive exporting firm which faces concurrently hedgeable exchange rate risk and non-hedgeable price risk. We show that the hedging role of currency options is due to two distinct sources of non-linearity: (i) the multiplicative nature of the price and exchange rate risk; and (ii) the marginal utility function of the firm. In particular, we show that a long put option position is optimal when the price risk is negatively correlated with the exchange rate risk and/or the firm is prudent.     

Score-driven Markov-switching EGARCH models: an application to systematic risk analysis

We introduce new Markov-switching (MS) dynamic conditional score (DCS) exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models, to be used by practitioners for forecasting value-at-risk (VaR) and expected shortfall (ES) in systematic risk analysis. We use daily log-return data from the Standard & Poor’s 500 (S&P 500) index for the period 1950–2016. The analysis of the S&P 500 is useful, for example, for investors of (i) well-diversified US equity portfolios; (ii) S&P 500 futures and options traded at Chicago Mercantile Exchange Globex; (iii) exchange traded funds (ETFs) related to the S&P 500. The new MS DCS-EGARCH models are alternatives to of the recent MS Beta-t-EGARCH model that uses the symmetric Student’s t distribution for the error term. For the new models, we use more flexible asymmetric probability distributions for the error term: Skew-Gen-t (skewed generalized t), EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverse Gaussian) distributions. For all MS DCS-EGARCH models, we identify high- and low-volatility periods for the S&P 500. We find that the statistical performance of the new MS DCS-EGARCH models is superior to that of the MS Beta-t-EGARCH model. As a practical application, we perform systematic risk analysis by forecasting VaR and ES.

Abbreviation Single regime (SR); Markov-switching (MS); dynamic conditional score (DCS); exponential generalized autoregressive conditional heteroscedasticity (EGARCH); value-at-risk (VaR); expected shortfall (ES); Standard & Poor's 500 (S&P 500); exchange traded funds (ETFs); Skew-Gen-t (skewed generalized t); EGB2 (exponential generalized beta of the second kind); NIG (normal-inverse Gaussian); log-likelihood (LL); standard deviation (SD); partial autocorrelation function (PACF); likelihood-ratio (LR); ordinary least squares (OLS); heteroscedasticity and autocorrelation consistent (HAC); Akaike information criterion (AIC); Bayesian information criterion (BIC); Hannan-Quinn criterion (HQC).     

Home and regional biases and border effects in Armington type models

We discuss biases in preferences and their trade effects in terms of impacts on non-neutral trade flows motivated by recent literature on both home bias and the border effect. These terms take on multiple definitions in the literature and are often used interchangeably even though they differ. The border effect refers to a higher proclivity to trade behind rather than across national borders and is usually defined by the coefficients of regional dummies from an estimated gravity model. It can be present both in data and in counterfactual model solutions. Sometimes the reduced form of the gravity model used is asserted to reflect an Armington type model. For the border effect to occur as a model outcome, a structural model with at least 2 home regions and 1 country abroad is needed. In contrast to current literature, we offer a characterization of various forms of preference bias in trade models and measures of their associated trade effects based on a concept we term trade neutrality. These effects go beyond conventional border effects, and can be both across and within borders. Home bias is typically specified as an Armington preference for domestic over comparable foreign products in a trade model where goods are heterogeneous across countries. It is reflected in both model structure and parameterization, but defined in several different ways in the literature. We assess the contribution of each form of bias to the set of possible trade effects using a calibrated model with 3 Canadian regions, the U.S., and the rest of the world using 2001 data. We also evaluate how much of the conventional border effect is accounted for when model biases are modified in various ways.     

Variety‐Controlling Public Policy Under Addiction and Saturation

This paper constructs a tractable general equilibrium model for investigating the dissimilar effects of addiction and saturation on consumption and public policy. By introducing an industry‐specific intertemporal consumption externality, we provide clear analytical results that a lump‐sum subsidy for firms can increase welfare in the presence of a negative externality (saturation). A tax can accomplish the same given a positive externality (addiction). Unlike existing studies of cultural goods, these results are not based on assumptions concerning exogenous different preferences across groups, but rather on conventional monopolistic competition and consumption habit formation models in macroeconomics.     

Agglomeration,backward and forward linkages: evidence from South Korean investment in China

Abstract With a firm‐level data set, we study the location decision of South Korean multinationals across China's regions. Our conditional logit estimates confirm agglomeration effects along industry and along national lines. We add an upstream and downstream (backward and forward) linkage effect. We find that the presence of upstream and downstream South Korean affiliates significantly increases the likelihood that a South Korean multinational invests in a particular region. However, linkages that do not differentiate by nationality do not seem to matter much. As such, our analysis of investors' location choice brings together two perspectives: linkages and agglomeration along national lines.