Return Dynamics Research Articles

What Goliaths and Davids among Swiss firms tell us about expected returns on Swiss asset markets

Motivated by recent US evidence, we evaluate the predictive power of changes in the weight of large firms in the aggregate stock market (“Goliath vs David” (GVD)) for Swiss stock market returns and bond market returns. Previous research suggests that the asset return dynamics in the US and Switzerland differ markedly. Forecasting Swiss asset returns hence constitutes a challenging “out-of-sample” test for GVD. Over the sample period from January 1999 to December 2017, we find that the Swiss version of GVD exhibits predictive power for Swiss stock and bond market returns even in the presence of global predictors. However, Swiss bond market returns are best predicted by the US term spread.

Incorporating time‐varying jump intensities in the mean‐variance portfolio decisions

AbstractThis paper examines the role of time‐varying jump intensities in forming mean‐variance portfolios. We find that compared with the no‐jump or constant‐jump models, the model which incorporates time‐varying jump intensities better fits the dynamics of the assets returns, and yields mean‐variance portfolios with higher Sharpe ratios. Our research suggests that using a better econometric model that captures non‐normal features in the data has benefits for portfolio allocation even for a mean‐variance investor.

On the Inflation Risks Embedded in Sovereign Bond Yields: A Revisit

We revisit the concept of the cost of hedging inflation risks put forward in Bodie (1976). When doing so, we employ a time-varying vector autoregressive model to describe the dynamics of asset returns. We estimate this model by means of the kernel-based methods discussed in Giraitis et al. (2018), but relying on the estimation approach put forward in Morf et al. (1978), which enforces the stability of the estimated VAR. Our modelling framework allows to disentangle the time-varying compensation for expected and unexpected inflation shocks embedded in the sovereign bond yields of Germany, France, Japan and the United States. Our empirical results suggest that the current environment of very low nominal sovereign bond yields, is a reflection of a low real risk-free rate, low inflation expectations and a low cost for hedging inflation risks. We have not encountered similar past episodes in the sample under study. We have tentatively searched for a similar pattern in episodes characterised by recessionary phases of the business cycle coupled with low inflation expectations. However, we failed to robustly associate those episodes with either low real risk-free rates, or with low costs for hedging inflation risks.

Extreme returns and intensity of trading

SummaryAsymmetric information models of market microstructure claim that variables such as trading intensity are proxies for latent information on the value of financial assets. We consider the interval‐valued time series (ITS) of low/high returns and explore the relationship between these extreme returns and the intensity of trading. We assume that the returns (or prices) are generated by a latent process with some unknown conditional density. At each period of time, from this density, we have some random draws (trades) and the lowest and highest returns are the realized extreme observations of the latent process over the sample of draws. In this context, we propose a semiparametric model of extreme returns that exploits the results provided by extreme value theory. If properly centered and standardized extremes have well‐defined limiting distributions, the conditional mean of extreme returns is a nonlinear function of the conditional moments of the latent process and of the conditional intensity of the process that governs the number of draws. We implement a two‐step estimation procedure. First, we estimate parametrically the regressors that will enter into the nonlinear function, and in a second step we estimate nonparametrically the conditional mean of extreme returns as a function of the generated regressors. Unlike current models for ITS, the proposed semiparametric model is robust to misspecification of the conditional density of the latent process. We fit several nonlinear and linear models to the 5‐minute and 1‐minute low/high returns to seven major banks and technology stocks, and find that the nonlinear specification is superior to the current linear models and that the conditional volatility of the latent process and the conditional intensity of the trading process are major drivers of the dynamics of extreme returns.

A Model Free Approach to the Pricing of Downside Risk in Argentinean Stocks

The return dynamics of Argentina's main stock index, the SP Mer.Val., show a high level of volatility, signaling a higher degree of downside risk. To hedge against that specific risk, investors could buy put options. However, the Argentinean capital market slacks variety of hedging contracts. The basic availability of putoptions depends on the possibility of short selling the underlying security, i.e. transfer risk to a third party, something not properly developed in the domestic market. Since data processing power has geometrically increased in the last decades and some mathematic formulas that were helpful for calculation had been surpassed by data gathering and processing that helps to find a better estimate when necessary, in this paper we show the point calculating protection against downside risk in the Argentinean stock market, using real data and programming an algorithm to perform calculations instead of resorting the standard Black-Scholes-Merton formulae, by means of a model free approach to acknowledge the issue.

Prediction of the Volatility of Ghana Stock Exchange Main Index by Using the Generalised Autoregressive Conditional Heteroskedasticity Model

Since 2014, the Ghana Stock Exchange (GSE) main index has experienced turbulent stock market volatility and its overall performance continues to follow a downward trend. Consequently, this has awakened unprecedented interest in key stakeholders in order to halt the downward performance of the index. The call for further investigations and research into the stock market volatility put this study in the forefront. The first objective of this research was to analyse the statistical properties of GSE main index. The second objective was the determination of relation between stock price movement and volatility. Finally, the study also sought to find out the extent to which the prediction of volatility and returns of GSE main index could minimise the risk incurred by investors. The most empirically proven model called the Generalised Autoregressive Conditional Heteroskedasticity (GARCH), which takes care of all the statistical properties and stochastic dynamics of asset returns was used in capturing the stylish features of GSE main index: volatility clustering, excess kurtosis, leverage effects and unit roots. The distribution innovations of the residuals term considered in the application of the GARCH model were normal, Student't' and Generalised Error Distribution (GED). The result from the study indicated that EGARCH (1, 1) model with GED was the best model fitted for the GSE main index series during the in-sample period estimation. It is also observed surprisingly that higher GARCH orders could not out-perform lower GARCH orders. The prediction of volatility and returns on assets of GSE main index was good. The results of the prediction were validated by tools like Root Mean Square Error, Theil Inequality coefficient and simple regression line. On the whole, investors can possibly rely on the findings of this study to make further financial decisions regarding stock market volatility.

Return dynamics during periods of high speculation in a thinly traded commodity market

AbstractThis article studies the effects of speculation in a thinly traded commodity futures market, paying particular attention to periods characterized by high‐speculative activity of long–short speculators. Using the speculation ratio as a daily measure for long–short speculation, we employ generalized autoregressive conditional heteroscedasticity regressions to study its impact on return dynamics. Our results for the Chicago Mercantile Exchange feeder cattle futures market suggest that futures returns are predominantly explained by fundamentals, but their volatility is significantly driven by the speculation ratio. This relationship holds for periods of high‐ and low‐speculative activity alike.

Statistical estimation of time-varying complexity in financial networks

In this paper, we propose a method to characterize the relation between financial market instability and the underlying complexity by identifying structural relationships in dynamics of stock returns. The proposed framework is amenable to statistical and econometric estimation techniques, and at the same time, provides a theoretical link between stability of a financial system and the embedded heterogeneity, in line of the May-Wigner result. We estimate the interaction matrix of stock returns through a vector autoregressive structure and compute heterogeneity in the strength of connections for time periods covering periods before the 2007–08 crisis, during the crisis and post-crisis recovery. We show that the empirically estimated heterogeneity increased substantially during time of financial crisis and subsequently tapered off, demonstrating concurrent rise and fall in the degree of instability.

Does trade interdependency lead linkages between stock markets? A case of South Asian countries

PurposeThe purpose of this paper is to test the dynamic linkages among the stock markets of four South Asian countries (India, Pakistan, Bangladesh and Sri Lanka) in the backdrop of trade interdependency.Design/methodology/approachListed indices are used to serve the proxy of stock markets of four countries for the period: January 2000–December 2018. The study uses the autoregressive distributed lag model and Granger causality techniques in multivariate frameworks while focusing on intraregional trade as an exogenous factor for testing the long- and short-run causality in the given data set, hence raising the quality of statistical inference.FindingsThe results highlight that India and Pakistan are net exporters to the South Asian region, while Bangladesh and Sri Lanka are net importers from the region. While testing the stock markets linkages, the expanded intraregional trade volumes (exports plus imports) have occurred with the significant cointegration of stock markets of India and Pakistan with the other stock markets in the long run. In the short run, the stock markets of India, Pakistan and Sri Lanka report bidirectional causality without having significant spillovers of intraregional trade on the stock prices.Research limitations/implicationsThe study relies on the multivariate techniques with stock prices and regional trade share as the exogenous variables. Further the regulatory, political and economic conditions of sample countries are fundamentally different which in turn affect their degree of trade interdependency and integration between the stock markets.Practical implicationsNonsignificant cointegration of the stock markets of Sri Lanka and Bangladesh highlights the possibility of portfolio diversification in the long run, while the significant bidirectional causalities between the stock markets highlight the lesser degree of portfolio diversifications in the short run.Originality/valuePioneer efforts are made to examine the dynamic linkages between the South Asian stock markets while focusing on regional trade interdependency. The results provide new insight in the dynamics of stock returns of South Asian stock markets in the backdrop of intraregional trade.

Fitting Private Equity into the Total Portfolio Framework

In this article, the authors propose a risk estimation model that addresses both smoothness and idiosyncratic risk dynamics of narrow private equity portfolio returns. The authors subsequently apply the model to a broad set of private equity return streams with tightly controlled diversification properties. They show that increased model complexity is detrimental to the model’s forecasting power and that idiosyncratic risks increase as one goes between hypothetical investment in a broad (albeit noninvestable) private equity index and a real-life, narrowly diversified portfolio of private equity funds. The authors also find that private equity returns have demonstrated a level of exposure to public equity markets that may be considered surprisingly low and offer a possible qualitative justification for this phenomenon. Finally, the authors incorporate the newly obtained private equity risk model into a general portfolio construction framework and demonstrate how to study the inevitable trade-offs between the costs and benefits of increased portfolio diversification. <b>TOPICS:</b>Portfolio theory, portfolio construction, equity portfolio management <b>Key Findings</b> • By applying a somewhat novel econometric technique to a proprietary dataset, we offer a way to separately estimate systematic and idiosyncratic risks of private equity (PE) programs. Our methodology controls for data overfitting and program concentration. • Observed volatility and equity beta of PE are much lower than that of public equity. We believe this is not an aberration but instead a fundamental consequence of PE being less exposed to the excess volatility of public equity markets. • With the exception of the tumultuous period of the global financial crisis (GFC), risk properties of private equity have been surprisingly persistent; if anything, systematic risks of private equities decreased slightly after the GFC.

Can tail risk explain size, book‐to‐market, momentum, and idiosyncratic volatility anomalies?

AbstractWe examine the impact of tail risk on the return dynamics of size, book‐to‐market ratio, momentum and idiosyncratic volatility sorted portfolios. Our time‐series analyses document significant portfolio return exposures to aggregate tail risk. In particular, portfolios that contain small, value, high idiosyncratic volatility and low momentum stocks exhibit negative and statistically significant tail risk betas. Our cross‐sectional analyses at the individual stock level suggest that tail risk helps in explaining the four pricing anomalies, particularly size and idiosyncratic volatility anomalies.

The Impact of Macroeconomic News on US Interest Rates and Stock Indices Conditional on Their Volatility

This paper investigates the impact of macroeconomic news on the dynamics of interest rates and stock returns during "low" and "high" volatility periods. These periods are determined by estimating asset dynamics using a SWARCH process. Our results suggest that securities volatility is higher during periods of financial or economic instability. We use these results to evaluate the impact of news during "low" and "high" volatility periods using a GARCH model. News effects, especially “good” and “large” news, on interest rates are amplified during "high" uncertainty periods. The effect on stock returns is moderate. GARCH parameters differ strongly during both periods.

Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data

• Our empirical analysis makes use of high-frequency data from the futures markets. • We show that the introduction of both leverage and jumps in the futures return dynamics improves the ability to forecast volatility. • It also provides the best forecast for risk in both a VaR and a CVaR sense. • It benefits speculative firms who are short natural gas futures while minimizing tail risk in a VaR sense. • And it also benefits speculative firms who are long S&P500 futures and use either VaR or CVaR as financial risk management criteria. In this paper, we propose a model for futures returns that has the potential to provide both individual investors and firms who have positions in financial and energy commodity futures a valid tail risk management tool. In doing so, we also aim to explore the commonalities between these markets and the degree of financialization of energy commodities. While empirical studies in energy markets embed either leverage or jumps in the futures return dynamics, we show that the introduction of both features improves the ability to forecast volatility as an indicator for risk for both the S&P500 and natural gas futures markets.

Has Co-Movement Dynamics in Brazil, Russia, India, China and South Africa (BRICS) Markets Changed After Global Financial Crisis? New Evidence from Wavelet Analysis

We investigate the changes in the co-movement dynamics in the stock market returns of Brazil, Russia, India, China and South Africa (BRICS) with that of US during pre and post-global financial crisis (GFC). The stock returns of BRICS and the US markets over the period of 1999A±2016 are analysed using wavelet transformation, with equal time phase of eight years on both sides of GFC. We find the existence of co-movement at both high and low frequencies. In addition, the contagion effect is also noted around the GFC year 2008. Further we also report that despite the high correlation of BRICS portfolio, it facilitates asset diversification benefits in the medium run. Finally, there is significant changes in correlation dynamics for Russia and China during post-GFC period, whereas the multiple correlations dynamics amongst BRICS markets remain unchanged.

International Financial US Linkages: Networks Theory and MS-VAR Analyses

This paper aims to examine the impact of the Global Financial Crisis on portfolio investment flows, as well as on stock market activity. Network Theory is used to analyze structural changes of foreign portfolio investment flows (FPI) to a sample of 13 developed countries and 6 emerging Latin American countries. Additionally, using daily data from 2003 to 2015, the dynamics of returns are analyzed to test whether the US market influenced these markets or vice versa; univariate (MS-AR) and multivariate (MS-VAR) regime-switching models are used. The evidence confirms the presence of two different regimes, low volatility and a high volatility for all markets. Findings suggest strengthening local productive and financial institutions in order to anchor FPI. The MS-(V)AR study is limited to stock markets from the Americas and Europe. Previous literature has not applied the innovative and complementary methodologies employed here to analyze financial crisis impacts on FPI flows. We conclude that US financial markets keep a close financial relationship with the most important European and American countries’ stock markets, both by receiving and delivering FPI, and in addition influencing the behavior of stock indexes.

Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks

Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks

Time-varying risk aversion and the predictability of bond premia

We show that time-varying risk aversion captures significant predictive information over excess returns on U.S. government bonds even after controlling for a large number of financial and macro factors. Including risk aversion improves the predictive accuracy at all horizons (one- to twelve-months ahead) for shorter maturity bonds and at shorter forecast horizons (one- to three-months ahead) for longer maturity bonds. Given the role of Treasury securities in economic forecasting models and portfolio allocation decisions, our findings have significant implications for investors, policymakers and researchers interested in accurately forecasting return dynamics for these assets.

Diagnostic Expectations and Stock Returns

ABSTRACTWe revisit La Porta's finding that returns on stocks with the most optimistic analyst long‐term earnings growth forecasts are lower than those on stocks with the most pessimistic forecasts. We document the joint dynamics of fundamentals, expectations, and returns of these portfolios, and explain the facts using a model of belief formation based on the representativeness heuristic. Analysts forecast fundamentals from observed earnings growth, but overreact to news by exaggerating the probability of states that have become more likely. We find support for the model's predictions. A quantitative estimation of the model accounts for the key patterns in the data.

China's “New normal”: Will China's growth slowdown derail the BRICS stock markets?

After four decades of impressive performance, China's economic expansion has begun to slow. Considering the growing China's integration in global financial markets, we examine the effect of heightened uncertainty surrounding the China's transition to the new growth model on the remaining BRICS (in particular, Brazil, Russia, India and South Africa) stock markets. This analysis is novel in its methodological approach, which is conducted to pinpoint the dynamic spillover effects as alternative to the time and frequency. The impact of China's growth slowdown is found to be heterogeneous across the BRICS stock markets, suggesting that this crisis does not affect return dynamics in these markets in a uniform way. More specifically, South Africa hasn't been rattled as badly as Brazil, Russia and India. The intensity of bilateral trade and investment relationships, the position of market in terms of regulation and securities exchanges, the financial system efficiency and the ability of counter-cyclical policies to cope with the severe downturn have been put forward to explain the heterogeneous responses of BRICS equities.

Fat-tailed stochastic volatility model and the stock market returns in China

PurposeThe purpose of this paper is to investigate how the selection of return distribution impacts estimated volatility in China’s stock market.Design/methodology/approachThe authors use a Bayesian analysis of fat-tailed stochastic volatility (SV) model with Student’s t-distribution, and conduct an out-of-sample test with realized volatility.FindingsEmpirical analysis results indicate that fat-tailed SV model performs better in capturing the dynamics of daily returns. The authors find that asymmetry, holiday and day of the week effects are detected in estimated volatility. However, the out-of-sample comparison shows that fat-tailed SV models fail to outperform SV models with normal distribution in fitting and predicting realized volatility.Originality/valueThe contribution of this paper to existing literature is twofold. First, it proves that fat-tailed SV models with Student’s t-distribution perform better than normally distributed SV models in fitting daily returns of China’s stock market. Second, this paper takes asymmetry, holiday and day of the week effects into consideration at the same time in the fat-tailed SV model.