Modeling common volatility characteristics and dynamic risk premia in European equity markets

The paper applies a Factor-GARCH model to evaluate the impact of the market portfolio, as a single common dynamic risk factor, on conditional volatility and risk premia for the returns on size-based equity portfolios of three major European markets; France, Germany and the United Kingdom. The results show that for the size-based portfolios the factor loading for the dynamic market factor is significant and positive but the association between the risk premia and the conditional market volatility is weak. However, the dynamic market factor is shown to explain common characteristics in the conditional variance such as asymmetry and persistence. This finding is consistent across markets and portfolio sizes.     

Following the leaders? A study of co-movement and volatility spillover in BRICS currencies

The paper investigates return co-movement and volatility spillover among the currencies of Brazil, Russia, India, China, and South Africa (the BRICS member countries) and four major developed countries from April 2006 to October 2019. Using Bloomberg daily data on exchange rates, the study employs a flexible multivariate generalized autoregressive conditional heteroskedasticity (MGARCH)–dynamic conditional correlation (DCC) model and a vector autoregressive (VAR)–based spillover index, as the empirical strategy. Along with evidence of exchange rate volatility in BRICS currencies, among which the Russian ruble and the Chinese yuan are explosive, the econometric estimation results show the presence of significant return co-movement and volatility spillover among the foreign exchange markets across different countries. The currency markets in developed countries, as leaders, are found to transmit volatility mostly to BRICS currency markets, which are net receivers. The degree of spillover, however, varies across countries, with Brazil and Russia passing on volatility to the developed countries whereas India, China, and South Africa receive volatility from their developed counterparts.     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.     

Common shocks,common transmission mechanisms and time-varying connectedness among Dow Jones Islamic stock market indices and global risk factors

The purpose of this paper is twofold. First, we examine the importance of permanent versus transitory shocks as well as their domestic and foreign components in explaining the business cycle fluctuations of seven Dow Jones Islamic stock markets (DJIM), namely U.S., U.K., Canada, Europe, Asia-Pacific, Japan and GCC, over the period from April 2003 to November 2018, using the permanent-transitory (P-T) decompositions approach of Centoni et al. (2007). Second, we investigate the spillover mechanisms of these shocks across Islamic stock markets and a set of global risk factors, using the Diebold and Yilmaz (DY) (2012) approach. The P-T decomposition results show that the DJIM U.S., U.K., Europe and GCC indices are sensitive to both domestic and foreign shocks, while the DJIM Canada, Japan and Asia-Pacific are most sensitive to domestic shocks. The empirical results of the DY approach indicate that: (i) the return and volatility spillover intensity increase during financial turmoil, supporting evidence of the contagion phenomenon, (ii) the DJIM U.S. is the main transmitter of return and volatility spillovers, while the DJIM GCC is identified as the main receiver of both return and volatility spillovers, (iii) the seven Dow Jones Islamic stock indices are weakly linked to movements of global risk factors, and (iv) there is evidence of possible portfolio diversification between the selected Islamic stock markets and the oil commodity market.     

Modelling Regime‐Specific Stock Price Volatility*

Single‐state generalized autoregressive conditional heteroscedasticity (GARCH) models identify only one mechanism governing the response of volatility to market shocks, and the conditional higher moments are constant, unless modelled explicitly. So they neither capture state‐dependent behaviour of volatility nor explain why the equity index skew persists into long‐dated options. Markov switching (MS) GARCH models specify several volatility states with endogenous conditional skewness and kurtosis; of these the simplest to estimate is normal mixture (NM) GARCH, which has constant state probabilities. We introduce a state‐dependent leverage effect to NM‐GARCH and thereby explain the observed characteristics of equity index returns and implied volatility skews, without resorting to time‐varying volatility risk premia. An empirical study on European equity indices identifies two‐state asymmetric NM‐GARCH as the best fit of the 15 models considered. During stable markets volatility behaviour is broadly similar across all indices, but the crash probability and the behaviour of returns and volatility during a crash depends on the index. The volatility mean‐reversion and leverage effects during crash markets are quite different from those in the stable regime.     

Time-varying asymmetric volatility spillover between global markets and China’s A,B and H-shares using EGARCH and DCC-EGARCH models

This paper investigates the volatility spillover and dynamic conditional correlation between three types of China’s shares including A, B and H-shares with 12 major emerging and developed markets from 2002 to 2017 using EGARCH and multivariate DCC-EGARCH models. Both models found that Chinese equities are more related with their neighbouring countries such as Singapore, Japan, Australia and ASEAN-5 than with US, Germany and UK. The EGARCH model, with an auxiliary term added to capture the volatility spillover, found no volatility spillover between A-share markets and other advanced and emerging markets during the GFC and extended-crisis periods while this behaviour is not observed for B-share and H-share markets. However, the multivariate DCC model found strong evidence of contagion effect in both return correlations and volatility spillover for all China’s markets. In addition, both models found increased regional and global integration in A-share and B-share markets but not the H-share market. Finally, the results from both models provide clear evidence of distinct behaviours associated with return and volatility spillover in these three share types, suggesting foreign investors should consider the heterogeneity in volatility spillover and return correlations of these Chinese share types when forming investment strategies.     

An examination of the intraday behavior of the yen/dollar exchange rate: The relationship between trading activity and returns volatility

This paper provides additional empirical evidence of the relationship between the volatility of returns and trading activity in foreign exchange markets. Five-minute yen/dollar returns exhibit significant skewness, kurtosis, negative first-order autocorrelation and heteroskedasticity. Market activity (as measured by the intensity of quote arrivals) has a positive and statistically significant effect on conditional returns volatility. Such evidence is consistent with predictions of mixture of distrubutions models.     

Alternative econometric implementations of multi-factor models of the U.S. financial markets

This paper analyzes the empirical performance of two alternative ways in which multi-factor models with time-varying risk exposures and premia may be estimated. The first method echoes the seminal two-pass approach introduced by Fama and MacBeth (1973). The second approach is based on a Bayesian latent mixture model with breaks in risk exposures and idiosyncratic volatility. Our application to monthly, 1980–2010 U.S. data on stock, bond, and publicly traded real estate returns shows that the classical, two-stage approach that relies on a nonparametric, rolling window estimation of time-varying betas yields results that are unreasonable. There is evidence that most portfolios of stocks, bonds, and REITs have been grossly over-priced. On the contrary, the Bayesian approach yields sensible results and a few factor risk premia are precisely estimated with a plausible sign. Predictive log-likelihood scores indicate that discrete breaks in both risk exposures and variances are required to fit the data.     

Expectations Formation and Risk in Three Financial Markets: Surveying What the Surveys Say

This paper attempts to provide a logical overview of the literature which exploits survey data to examine issues of expectations formation and risk aversion in financial markets. Our survey suggests that: short term expectations are excessively volatile and exhibit bandwagon effects, while longer term expectations appear to be regressive and therefore stabilising; in bond and foreign exchange markets the standard result of forward rate biasedness is due in part to time-varying premia; recent research using disaggregate foreign exchange survey data demonstrates the importance of heterogeneous expectations.     

Volatility contagion: A range-based volatility approach

This article proposes a new approach to evaluate volatility contagion in financial markets. A time-varying logarithmic conditional autoregressive range model with the lognormal distribution (TVLCARR) is proposed to capture the possible smooth transition in the range process. Additionally, a smooth transition copula function is employed to detect the volatility contagion between financial markets. The approach proposed is applied to the stock markets of the G7 countries to investigate the volatility contagion due to the subprime mortgage crisis. Empirical evidence shows that volatility is contagious from the US market to several markets examined.     

Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

In this paper, we investigate the relation between time-varying risk aversion and renminbi exchange rate volatility using the conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model. The CARR-MIDAS model is a range-based volatility model, which exploits intraday information regarding the intraday trajectory of the price. Moreover, the model features a MIDAS structure allowing for time-varying risk aversion to drive the long-run volatility dynamics. Our empirical results show that time-varying risk aversion has a significantly negative effect on the long-run volatility of renminbi exchange rate. Moreover, we observe that both intraday ranges and time-varying risk aversion contain important information for forecasting renminbi exchange rate volatility. The range-based CARR-MIDAS model incorporating time-varying risk aversion provides more accurate out-of-sample forecasts of renminbi exchange rate volatility compared to a variety of competing models, including the return-based GARCH, GARCH-MIDAS and GARCH-MIDAS incorporating time-varying risk aversion as well as range-based CARR, CARR-MIDAS and heterogeneous autoregressive (HAR), for forecast horizons of 1 day up to 3 months. This result is robust to alternative risk aversion measure, alternative MIDAS lags as well as alternative out-of-sample periods. Overall, our findings highlight the value of incorporating intraday information and time-varying risk aversion for forecasting the renminbi exchange rate volatility.     

Conditional value-at-risk forecasts of an optimal foreign currency portfolio

This study provides daily conditional value-at-risk (C-VaR) forecasts for a foreign currency portfolio comprising the USD/EUR, USD/JPY, and USD/BRL currencies. To do so, we estimate multivariate stochastic volatility models with time-varying conditional correlations using a Bayesian Markov chain Monte Carlo algorithm. Then, given the model-specific currency return density forecasts, we make the optimal portfolio choice by minimizing the C-VaR through numerical optimization. According to out-of-sample experiment, including emerging markets into the currency basket is essential for downside risk management, and considering model uncertainty as well as the parameter uncertainty can improve the portfolio performance.     

FOREIGN EXCHANGE RATE EXPECTATIONS: SURVEY AND SYNTHESIS

Abstract.  This paper reviews the empirical literature on foreign exchange rate expectations. Prominent issues are the forward premium puzzle, expectations formation in financial markets, heterogeneity of expectations, market microstructure, time-varying risk premiums and forecast performance. Although much has been learned in each field, this survey highlights the areas of research in which our understanding of the mechanism of exchange rate expectations is still incomplete. Our survey suggests that both irrational expectations and time-varying risk premiums account for the forward discount anomaly, that long-term expectations reverse towards their long-run equilibrium values and that heterogeneous behaviour of market participants has the potential of explaining some of the empirical regularities in the international finance literature.     

Exchange rates and oil prices: A multivariate stochastic volatility analysis

This paper uses the multivariate stochastic volatility (MSV) and the multivariate GARCH (MGARCH) models to investigate the volatility interactions between the oil market and the foreign exchange (FX) market, in an attempt to extract information intertwined in the two for better volatility forecast. Our analysis takes into account structural breaks in the data. We find that when the markets are relatively calm (before the 2008 crisis), both oil and FX markets respond to shocks simultaneously and therefore no interaction is detected in daily data. However, during turbulent time, there is bi-directional volatility interaction between the two. In other words, innovations that hit one market also have some impact on the other at a later date and thus using such a dependence significantly improves the forecasting power of volatility models. The MSV models outperform others in fitting the data and forecasting exchange rate volatility. However, the MGARCH models do better job in forecasting oil volatility.     

THE ECONOMICS OF THE UNCOVERED INTEREST PARITY CONDITION FOR EMERGING MARKETS

Abstract Financial account liberalizations since the second half of the 1980s paved the way for the burgeoning literature that investigates foreign exchange market efficiency in emerging markets (EMs) via testing for the uncovered interest parity (UIP) condition. This paper is the first to provide a broad and critical survey on this recent literature. Specifically, we attempt to answer the following questions. First, are the EMs different from the developed economies in the context of the UIP condition? Second, to what extent can these differences contribute to the debate on the UIP literature? Third, what are the empirical challenges specific to the EMs in testing for the UIP condition?     

Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China

We develop a skewness-dependent multivariate conditional autoregressive value at risk model (SDMV-CAViaR) to detect the extreme risk transmission channels between the Chinese stock index futures and spot markets. The proposed SDMV-CAViaR model improves the forecast performance of extreme risk by introducing the high-frequency realized skewness. Specifically, the realized skewness has a significant impact on the spillovers, but the realized volatility and realized kurtosis do not, which implies that the jump component plays an important role in extreme risk spillovers. The empirical results indicate there are bidirectional extreme risk spillovers between the stock index futures and spot markets, the decline of one market has direct and indirect channels to exacerbate the extreme risk of the other market. Firstly, the market decline will directly increase the extreme risk of related markets by decreasing market returns. Besides, the decline will indirectly increase the extreme risk by increasing the negative realized skewness and extreme risk spillovers.     

Market efficiency,asset returns,and the size of the risk premium in global equity markets

An important economic insight is that observed equity prices must equal the present value of the cash flows associated with the equity claim. An implication of this insight is that present values of cash flows must also quantitatively justify the observed volatility and cross-correlations of asset returns. In this paper, we show that parametric economic models for present values can indeed account for the observed high ex post return volatility and cross-correlation observed across five major equity markets—the U.S., the U.K., France, Germany, and Japan. We present evidence that cash flow growth rates contain a small predictable long-run component; this feature, in conjunction with time-varying systematic risk, can justify key empirical characteristics of observed equity prices. Our model also has direct implications for the level of equity prices and specific versions of the model can, in many cases, capture observed price levels. Our evidence suggests that the ex ante risk premium on the global market portfolio has dropped considerably—we show that this fall in the risk premium is related to a decline in the conditional variance of global real cash flow growth rates.     

Model‐free evaluation of directional predictability in foreign exchange markets

We examine directional predictability in foreign exchange markets using a model‐free statistical evaluation procedure. Based on a sample of foreign exchange spot rates and futures prices in six major currencies, we document strong evidence that the directions of foreign exchange returns are predictable not only by the past history of foreign exchange returns, but also the past history of interest rate differentials, suggesting that the latter can be a useful predictor of the directions of future foreign exchange rates. This evidence becomes stronger when the direction of larger changes is considered. We further document that despite the weak conditional mean dynamics of foreign exchange returns, directional predictability can be explained by strong dependence derived from higher‐order conditional moments such as the volatility, skewness and kurtosis of past foreign exchange returns. Moreover, the conditional mean dynamics of interest rate differentials contributes significantly to directional predictability. We also examine the co‐movements between two foreign exchange rates, particularly the co‐movements of joint large changes. There exists strong evidence that the directions of joint changes are predictable using past foreign exchange returns and interest rate differentials. Furthermore, both individual currency returns and interest rate differentials are also useful in predicting the directions of joint changes. Several sources can explain this directional predictability of joint changes, including the level and volatility of underlying currency returns. Copyright © 2007 John Wiley & Sons, Ltd.     

Recent developments in financial economics and econometrics: An overview

Research papers in empirical finance and financial econometrics are among the most widely cited, downloaded and viewed articles in the discipline of Finance. The special issue presents several papers by leading scholars in the field on “Recent Developments in Financial Economics and Econometrics”. The breadth of coverage is substantial, and includes original research and comprehensive review papers on theoretical, empirical and numerical topics in Financial Economics and Econometrics by leading researchers in finance, financial economics, financial econometrics and financial statistics. The purpose of this special issue on “Recent Developments in Financial Economics and Econometrics” is to highlight several novel and significant developments in financial economics and financial econometrics, specifically dynamic price integration in the global gold market, a conditional single index model with local covariates for detecting and evaluating active management, whether the Basel Accord has improved risk management during the global financial crisis, the role of banking regulation in an economy under credit risk and liquidity shock, separating information maximum likelihood estimation of the integrated volatility and covariance with micro-market noise, stress testing correlation matrices for risk management, whether bank relationship matters for corporate risk taking, with evidence from listed firms in Taiwan, pricing options on stocks denominated in different currencies, with theory and illustrations, EVT and tail-risk modelling, with evidence from market indices and volatility series, the economics of data using simple model free volatility in a high frequency world, arbitrage-free implied volatility surfaces for options on single stock futures, the non-uniform pricing effect of employee stock options using quantile regression, nonlinear dynamics and recurrence plots for detecting financial crisis, how news sentiment impacts asset volatility, with evidence from long memory and regime-switching approaches, quantitative evaluation of contingent capital and its applications, high quantiles estimation with Quasi-PORT and DPOT, with an application to value-at-risk for financial variables, evaluating inflation targeting based on the distribution of inflation and inflation volatility, the size effects of volatility spillovers for firm performance and exchange rates in tourism, forecasting volatility with the realized range in the presence of noise and non-trading, using CARRX models to study factors affecting the volatilities of Asian equity markets, deciphering the Libor and Euribor spreads during the subprime crisis, information transmission between sovereign debt CDS and other financial factors for Latin America, time-varying mixture GARCH models and asymmetric volatility, and diagnostic checking for non-stationary ARMA models with an application to financial data.