Stock and bond market interactions with level and asymmetry dynamics: An out-of-sample application

We model the dynamic interaction between stock and bond returns using a multivariate model with level effects and asymmetries in conditional volatility. We examine the out-of-sample performance using daily returns on the S&P 500 index and 10 year Treasury bond. We find evidence for significant (cross-) asymmetries in the conditional volatility and level effects in bond returns. The out-of-sample covariance matrix forecasts of the model imply that an investor is willing to pay between 129 and 820 basis points per year for using a dynamic trading strategy instead of a passive strategy.     

Economic evaluation of asymmetric and price range information in gold and general financial markets

Gold is widely perceived as a good diversification or safe haven tool for general financial markets, especially in market turmoil. To fully understand the potential, this study constructs an asymmetric multivariate range-based volatility model to investigate the dependence and volatility structures of gold, stock, and bond markets and further to compare the difference between the financial crisis and post-financial crisis periods. We find a striking explanatory ability to volatility structures provided by the price range information and significant evidence of asymmetric dependence across gold, stock, and bond markets. We implement an asset-allocation strategy incorporating asymmetric dependence and price range information to explore their economic importance. The out-of-sample results show that between 35 and 517 basis points and between 90 and 1111 basis points are earned annually when acknowledging asymmetric dependence and price range information, respectively. These economic benefits are inversely related to the level of investors’ risk aversion and are particularly significant in the period of the global financial crisis.     

Dynamic factor long memory volatility

In this paper, we develop a long memory orthogonal factor (LMOF) multivariate volatility model for forecasting the covariance matrix of financial asset returns. We evaluate the LMOF model using the volatility timing framework of Fleming et al. [J. Finance, 2001, 56, 329–352] and compare its performance with that of both a static investment strategy based on the unconditional covariance matrix and a range of dynamic investment strategies based on existing short memory and long memory multivariate conditional volatility models. We show that investors should be willing to pay to switch from the static strategy to a dynamic volatility timing strategy and that, among the dynamic strategies, the LMOF model consistently produces forecasts of the covariance matrix that are economically more useful than those produced by the other multivariate conditional volatility models, both short memory and long memory. Moreover, we show that combining long memory volatility with the factor structure yields better results than employing either long memory volatility or the factor structure alone. The factor structure also significantly reduces transaction costs, thus increasing the feasibility of dynamic volatility timing strategies in practice. Our results are robust to estimation error in expected returns, the choice of risk aversion coefficient, the estimation window length and sub-period analysis.     

Individual Investors and Volatility

We show that retail trading activity has a positive effect on the volatility of stock returns, which suggests that retail investors behave as noise traders. To identify this effect, we use a reform of the French stock market that raises the relative cost of speculative trading for retail investors. The daily return volatility of the stocks affected by the reform falls by 20 basis points (a quarter of the sample standard deviation of the return volatility) relative to other stocks. For affected stocks, we also find a significant decrease in the magnitude of return reversals and the price impact of trades.     

Does the Use of Foreign Currency Derivatives Affect Firms' Market Value? Evidence from Colombia

High returns in emerging markets over the last decade have attracted international investors. This study investigates if and how economic or political news affects stock market activity in two emerging markets: Argentina and Turkey. Our analysis shows that political and economic news influences both the volatility of returns and trading volume in these markets to varying degrees. Results suggest that both economic and political factors, as well as specific market characteristics, should be taken into consideration by international investors when making investment decisions in emerging markets.     

Is unlevered firm volatility asymmetric?

Asymmetric volatility refers to the stylized fact that stock volatility is negatively correlated to stock returns. Traditionally, this phenomenon has been explained by the financial leverage effect. This explanation has recently been challenged in favor of a risk premium based explanation. We develop a new, unlevering approach to document how well financial leverage, rather than size, beta, book-to-market, or operating leverage, explains volatility asymmetry on a firm-by-firm basis. Our results reveal that, at the firm level, financial leverage explains much of the volatility asymmetry. This result is robust to different unlevering methodologies, samples, and measurement intervals. However, we find that financial leverage does not explain index-level volatility asymmetry. We show that this difference between index-level asymmetry and firm-level asymmetry is driven by the asymmetry of the unlevered covariance component of index volatility.     

Forecasting time-varying covariance with a range-based dynamic conditional correlation model

This paper proposes a range-based dynamic conditional correlation (DCC) model combined by the return-based DCC model and the conditional autoregressive range (CARR) model. The substantial gain in efficiency of volatility estimation can boost the accuracy for estimating time-varying covariances. As to the empirical study, we use the S&P 500 stock index and the 10-year treasury bond futures to examine both in-sample and out-of-sample results for six models, including MA100, EWMA_, CCC_, BEKK, return-based DCC, and range-based DCC. Of all the models considered, the range-based DCC model is largely supported in estimating and forecasting the covariance matrices.     

THE ECONOMIC GAINS OF TRADING STOCKS AROUND HOLIDAYS

I assess the economic gains of strategies that account for the effect of holiday calendar effects on the daily returns and volatility of the 30 stocks in the Dow Jones Industrial Average index. The dynamic strategies use forecasts from stochastic volatility models that distinguish between regular trading days and different types of holidays. More important, I assess the economic value of conditioning on holiday effects and find that a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy that does not account for the effect of holidays on daily conditional expected returns and volatility to a strategy that does. This result is robust to reasonable transaction costs.     

The role of correlation dynamics in sector allocation

This paper assesses the economic value of modeling conditional correlations for mean–variance portfolio optimization. Using sector returns in three major markets we show that the predictability of models describing empirical regularities in correlations such as time-variation, asymmetry and structural breaks leads to significant performance gains over the static covariance strategy. Investors would be willing to pay a fee of up to 983 basis points to switch from the static to the dynamic correlation portfolio and about 100 basis points more for capturing asymmetries and shifts in correlations. The gains are robust to the crisis, transaction costs and are most pronounced for monthly rebalancing.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

Investor sentiment and return predictability of disagreement

This study examines the influence of investor sentiment on the relationship between disagreement among investors and future stock market returns. We find that the relationship between disagreement and future stock market returns time-varies with the degree of investor sentiment. Higher disagreement among investors’ opinions predicts significantly lower future stock market returns during high-sentiment periods, but it has no significant effect on future stock market returns during low-sentiment periods. Our findings imply that investor sentiment is related to several causes of short-sale impediments suggested in the previous literature on investor sentiment, and that the stock return predictability of disagreement is driven by investor sentiment. We demonstrate that investor sentiment has a significant impact on the stock market return predictability of disagreement through in-sample and out-of-sample analyses. In addition, the profitability of our suggested trading strategy exploiting disagreement and investor sentiment level confirms the economic significance of incorporating investor sentiment into the relationship between disagreement among investors and future stock market returns.     

Time-varying risk The case of the American computer industry

A bivariate GARCH-in-mean model for individual stock returns and the market portfolio is designed to model volatility and to test the conditional Capital Asset Pricing Model versus the conditional Residual Risk Model. We find that a univariate model of volatility for individual stock returns is misspecified. A joint modelling of the market return and the individual stock return shows that a major force driving the conditional variances of individual stocks is the history contained in the market return variance. We find that a conditional residual risk model, where the variance of the individual stock return is used to explain expected returns, is preferred to a conditional CAPM. We propose a partial ordering of securities according to their market risk using first and second order dominance criteria.     

An economic evaluation of stock–bond return comovements with copula-based GARCH models

Owing to their importance in asset allocation strategies, the comovements between the stock and bond markets have become an increasingly popular issue in financial economics. Moreover, the copula theory can be utilized to construct a flexible joint distribution that allows for skewness in the distribution of asset returns as well as asymmetry in the dependence structure between asset returns. Therefore, this paper proposes three classes of copula-based GARCH models to describe the time-varying dependence structure of stock–bond returns, and then examines the economic value of copula-based GARCH models in the asset allocation strategy. We compare their out-of-sample performance with other models, including the passive, the constant conditional correlation (CCC) GARCH and the dynamic conditional correlation (DCC) GARCH models. From the empirical results, we find that a dynamic strategy based on the GJR-GARCH model with Student-t copula yields larger economic gains than passive and other dynamic strategies. Moreover, a less risk-averse investor will pay higher performance fees to switch from a passive strategy to a dynamic strategy based on copula-based GARCH models.     

Locals, foreigners, and multi-market trading of equities: Intraday evidence from Thailand

We study stock market orders and trades in a developing country, Thailand, where foreign ownership limits partially segment local and foreign investors into two distinct markets. Some foreigners forgo voting rights and distributions to trade on the “local board”, while some locals forgo such benefits and pay a price premium to trade on the “foreign board”. Regardless of nationality, these cross-market traders typically submit orders when liquidity is high, fill orders at relatively beneficial prices, exploit patterns in stock prices across markets, display profitable holding-period returns, and enhance price discovery. This suggests that skilled, informed trading that affects market quality does not depend on trader nationality.     

Asset Pricing and Welfare Analysis with Bounded Rational Investors

Motivated by the fact that investors have limited ability to process information, I model investors’ bounded rational behavior in processing information and investigate its implications for asset pricing. Investors can make mistakes in processing information and thus have inaccurate estimates of fundamentals. This process generates “bounded rational risk.” I find that the volatility of stock and bond return increases in the presence of bounded rational investors, which can help explain the excessive volatility puzzle. The stock‐bond return correlation becomes time varying and even negative and rational investors benefit from the trading with bounded rational investors.     

Volatility spillover and investment strategies among sustainability-related financial indexes: Evidence from the DCC-GARCH-based dynamic connectedness and DCC-GARCH t-copula approach

This study analyzes the dynamic connectedness between the ESG stock index, the renewable energy stock index, the green bond stock index, the sustainability stock index, and the carbon emission futures by employing a novel method: the DCC-GARCH-based dynamic connectedness approach. Given the strong volatility spillover among these indexes, we adopt the DCC-GARCH t-copula model to calculate these indexes' hedging ratios and portfolio weights. Our findings show that the carbon emission futures are the volatility transmitter, and the green bond is the volatility receiver. The total dynamic connectedness is affected by international political, economic, and other events. Furthermore, for stock market volatility investors, taking the long position in carbon emission futures and the short position in renewable energy stock can achieve the highest hedging effect.     

Investor type trading behavior and trade performance: Evidence from the Thai stock market

This paper examines the trading behavior and decomposes the trading performance of foreign, individual and institutional investors as well as proprietary traders in a dynamic emerging stock market, the Stock Exchange of Thailand. Foreign investors follow a positive feedback, momentum strategy and are good short term market timers but have poor security selection performance in poor markets, thus suggesting that they have a macro (market timing) but not a micro (security selection) informational advantage relative to local investors. Institutions and proprietary traders have poor security selection trading performance. Individuals display herding behavior and have fairly good security selection performance, but individual investors appear to compensate proprietary traders for the provision of short term liquidity by proprietary traders, so individuals' security selection gains are canceled out by market timing losses.     

Causal and Dynamic Relationships among Stock Returns,Return Volatility and Trading Volume: Evidence from Emerging markets in South-East Asia

This paper examines the causal and dynamic relationships among stock returns, return volatility and trading volume for five emerging markets in South-East Asia—Indonesia, Malaysia, Philippines, Singapore and Thailand. We find strong evidence of asymmetry in the relationship between the stock returns and trading volume; returns are important in predicting their future dynamics as well as those of the trading volume, but trading volume has a very limited impact on the future dynamics of stock returns. However, the trading volume of some markets seems to contain information that is useful in predicting future dynamics of return volatility.     

Bubbles,Fads and Stock Price Volatility Tests: A Partial Evaluation

This is a summary and interpretation of some of the literature on stock price volatility that was stimulated by Leroy and Porter 28 and Shiller 40 . It appears that neither small-sample bias, rational bubbles nor some standard models for expected returns adequately explain stock price volatility. This suggests a role for some nonstandard models for expected returns. One possibility is a “fads” model in which noise trading by naive investors is important. At present, however, there is little direct evidence that such fads play a significant role in stock price determination.     

Reversal of Monday returns

This paper elaborates an interesting aspect of the Monday anomaly: Monday returns are relatively more likely to reverse over the subsequent days. We document that, although the Monday low-return anomaly disappeared, the subsequent reversal of Monday returns remains robust to date. The reversals, measured over a five-day horizon, are pervasive across international stock markets, reasonably stable over time, significant following both positive and negative Monday returns, and not confined to extreme Monday returns. Trading strategies designed to exploit these reversals earn economic profits. We examine potential explanations for the reversal of Monday returns using trading flows data of investor types from Korea. All predictions of the Foster and Viswanathan [J. Finance, 1993, 48, 187–211] model are confirmed: volatility is higher, trading volume is lower, market depth is lower and price impact costs are higher on Mondays. The model implies lower price quality on Mondays, but does not specifically predict reversal of Monday returns. We show that the trading intensity of international/institutional investors is lower on Mondays. This appears to make the market relatively more susceptible to individual investors’ trading, which is negatively correlated with international/institutional investors. Thus, Monday returns are relatively more likely to reverse during the subsequent days of the week when institutional investors trade more aggressively.