Trading volume,time-varying conditional volatility,and asymmetric volatility spillover in the Saudi stock market

Despite the well known importance of volatility–volume relationship, there is a paucity of research on this topic in emerging markets. We attempt to partially fill this gap by investigating volatility–volume relationship in the most important exchange market in the Middle East. We test the effect of trading volume on the persistence of the time-varying conditional volatility of returns in the Saudi stock market. Overall our results support the mixture of distribution hypothesis at the firm level. We also use two different proxies for information arrival, intra-day volatility, and overnight indicators. We find that these are good proxies for information and are important as contemporaneous volume in explaining conditional volatility. We also test for the volatility spillover direction between large- and small-cap portfolios. Our results show that the spillover effect is larger and statistically significant from large to small companies.     

Online learning of time-varying stochastic factor structure by variational sequential Bayesian factor analysis

Investment tasks include forecasting volatilities and correlations of assets and portfolios. One of the tools widely utilized is stochastic factor analysis on a set of correlated time-series (e.g. asset returns). Published time-series factor models require either sufficiently wide time windows of observed data or numeric solutions by simulations. We developed a ‘variational sequential Bayesian factor analysis’ (VSBFA) algorithm to make online learning of time-varying stochastic factor structure. The VSBFA is an analytic filter to estimate unknown factor scores, factor loadings and residual variances. The covariance matrix of the time-series predicted by the VSBFA can be decomposed into loadings-based covariance and specific variances, and the former can be expressed by ‘explanatory factors’ such as systematic components of various financial market indices. We compared the VSBFA with the most practiced factor model relying on wide data windows, the rolling PCA (principal components analysis), by applying them to 9-year daily returns of 200 simulated stocks with the ‘true’ daily data-generating model completely known, and by using them to forecast volatilities of long-only and long/short global stock portfolios with 25-year monthly returns of more than 800 stocks worldwide. Accuracy of the forecast covariance matrices is measured by a (symmetrized) Kullback–Leibler distance, and accuracy of the forecast portfolio volatilities is measured by bias statistic, log-likelihood, Q-statistic, and portfolio volatility minimization. The factor-based covariance and specific variances predicted by the best VSBFA are significantly more accurate than those by the best rolling PCA.     

Testing conditional factor models

Using nonparametric techniques, we develop a methodology for estimating and testing conditional alphas and betas and long-run alphas and betas, which are the averages of conditional alphas and betas, respectively, across time. The estimators and tests can be implemented for a single asset or jointly across portfolios. The traditional Gibbons, Ross, and Shanken (1989) test arises as a special case of no time variation in the alphas and factor loadings and homoskedasticity. As applications of the methodology, we estimate conditional CAPM and multifactor models on book-to-market and momentum decile portfolios. We reject the null that long-run alphas are equal to zero even though there is substantial variation in the conditional factor loadings of these portfolios.     

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.     

Testing financial contagion on heteroskedastic asset returns in time-varying conditional correlation

We suggest that there is a significant relationship between cross-market comovement and time varying volatility. The time-varying component of cross-market dependence is attributed to the intertemporal risk-return adjustment by rational, risk-averse investors who systematically revise their expectation in response to changing volatility. To reflect the time-varying component of cross-market dependence, we propose a time-varying correlation test for contagion. Our results show that out of the countries reporting contagion evidence under the constant correlation test, none of the countries exhibits contagion evidence from the 1997 Asian crisis. We conclude that a high level of cross-market correlation during a crisis reported as contagion evidence under the standard constant correlation test is mostly due to the high level of cross-market co-movement resulting from the intertemporal risk-return adjustment.     

Size,time-varying beta,and conditional heteroscedasticity in UK stock returns

The purpose of this study is to examine the relationship between firm size and time-varying betas of UK stocks. We extend the Schwert and Seguin (1990)(Journal of Finance 45, 1120–1155) methodology by explicitly modeling conditional heteroscedasticity in the market model residual returns. Our results show that the time-varying coefficient is not statistically significant for both small and large firm stock indexes. We also find that accounting for GARCH effects in the Schwert-Seguin market model yields beta estimates that are markedly differently from those when conditional heteroscedasticity is ignored. Event studies that ignore conditional heteroscedasticity may bias the abnormal returns of small and large firms, thereby leading to a different conclusion regarding the significance of an information event.     

Testing conditional factor models: A nonparametric approach

Some recent studies of conditional factor models do not specify conditioning information but use data from small windows to estimate the time series of conditional alphas and betas. In this paper, we propose a nonparametric method using an optimal window to estimate time-varying coefficients. In addition, we offer two empirical tests of a conditional factor model. Using our new method, we examine the performance of the conditional CAPM and the conditional Fama-French three-factor model in explaining the return variations of portfolios sorted by size, book-to-market ratios, and past returns, for which recent literature has generated controversial results. We find that, although in general the conditional FF model outperforms the conditional CAPM, both models fail to explain well-known asset-pricing anomalies. Moreover, for both models, the failure is more pronounced for the equally-weighted portfolios than for the value-weighted ones.     

A time-varying common risk factor affecting corporate yield spreads

A time-varying common risk factor affecting corporate yield spreads is modelled by extending a panel data model. The panel data model accommodates a common factor, which is associated with time-varying individual effects. The factor multiplied by a bond-specific unobservable is identified as a systematic risk premium. In disentangling the systematic risk premium, both credit and liquidity risks are evaluated; the credit risk is assessed by bond rating, and the liquidity risk is indirectly measured by discrepancy in quoted yields by brokerage firms. Parameters are estimated by the generalized method of moments procedure. The model is tested on the corporate bond market in Japan. Empirical results show that the time-varying common risk factor is successfully estimated together with credit and liquidity risks.     

The structure of information release and the factor structure of returns

We model how firms releasing information on different dates causes the CAPM to fail, requiring an additional factor based on the information structure to price assets. We exemplify this mechanism’s empirical relevance using quarterly earnings announcements, which cluster across months along size and book-to-market. Seventy percent of the alpha reduction from including SMB and HML occurs in the four main earnings announcement months. The information structure factor accounts for all of SMB and HML’s seasonal alpha reduction and one third of their overall alpha reduction. Controlling for size and book-to-market, exposures to SMB and HML vary with firms’ earnings announcement month.     

The specification of conditional expectations

This paper explores different specifications of conditional expectations. The most common specification, linear least squares, is contrasted with nonparametric techniques that make no assumptions about the distribution of the data. Nonparametric regression is successful in capturing some nonlinearities in financial data, in particular, asymmetric responses of security returns to the direction and magnitude of market returns. The technique is ideally suited for empirically modeling returns of securities that have complicated embedded options. The conditional mean and variance of the NYSE market return are also examined. Forecasts of market returns are not improved with the nonparametric techniques which suggests that linear conditional expectations are a reasonable approximation in conditional asset pricing research. However, the linear model produces a disturbing number of negative expected excess returns. My results also indicate that the relation between the conditional mean and variance depends on the specification of the conditional variance. Furthermore, a linear model relating mean to variance is rejected and these tests are not sensitive to the expectation generating mechanism nor the conditioning information. Rejections are driven by the distinct countercyclical variation in the ratio of the conditional mean to variance.     

Term structure determinants of time-varying risk of 1-year bond returns

Term structure drivers of 1-year bond premia and conditional bond return risk are distinct. Consequently, the Cochrane–Piazzesi factor captures aggregate price of risk and not the amount of risk in 1-year bond returns. One linear combination of forward rates captures most of the variation in bond return risk across maturities. Interest rate level captures substantial amount of variation in the conditional return risk, a finding consistent with rising inflation uncertainty with level of inflation and interest rates. The 4-5 yield spread, an important positive predictor of bond return premia, has an opposing but limited impact on the conditional volatility.     

The transmission of international shocks to the UK. Estimates based on a time-varying factor augmented VAR

The aim of this paper is to gauge the importance of foreign demand, supply and interest rate shocks on the UK economy and assess how their role has changed over time. To that end we devise a time-varying factor augmented VAR model that captures the relationship between 17 industrialised countries and the UK and accounts for any temporal evolution in this relationship. The response of UK macroeconomic variables to a foreign interest rate shock is estimated to have changed significantly since the early 1990s. International demand shocks play an important role in driving World and UK real activity, especially during the recent recession.     

Assessing dependence between financial market indexes using conditional time-varying copulas: applications to Value at Risk (VaR)

We analyse the dynamic dependence structure between broad stock market indexes from the United States (S&P500), Britain (FTSE100), Brazil (BOVESPA) and Mexico (PCMX). We employ Patton’s [Int. Econ. Rev., 2006, 2, 527–556] conditional copula setting and additionally observe the impact of different copula functions on Value at Risk (VaR) estimation. We conclude that the dependence between BOVESPA and the other indexes has intensified since the beginning of 2007. In our case the particular copula form is not crucial for VaR estimation. A goodness-of-fit test based on the parametric bootstrap is also applied. The best fits are obtained via time constant Student-t and time-varying Normal copulas.     

The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR

This paper investigates the role of high-order moments in the estimation of conditional value at risk (VaR). We use the skewed generalized t distribution (SGT) with time-varying parameters to provide an accurate characterization of the tails of the standardized return distribution. We allow the high-order moments of the SGT density to depend on the past information set, and hence relax the conventional assumption in conditional VaR calculation that the distribution of standardized returns is iid. The maximum likelihood estimates show that the time-varying conditional volatility, skewness, tail-thickness, and peakedness parameters of the SGT density are statistically significant. The in-sample and out-of-sample performance results indicate that the conditional SGT-GARCH approach with autoregressive conditional skewness and kurtosis provides very accurate and robust estimates of the actual VaR thresholds.     

The time-varying cost channel of monetary transmission

This paper studies the time-varying role of the cost channel of monetary transmission, i.e. the supply-side effect of monetary policy based on firms' costs of holding working capital. For that purpose, we provide rolling-window estimates of an augmented New Keynesian Phillips curve and show that the cost channel exhibits important time-varying dynamics. We find, as a general pattern, that the cost channel was most important in the pre-Volcker period and less important in the Volcker–Greenspan era. Recently, however, the cost channel regained importance. Since the cost channel is based on the transmission of policy impulses through bank lending, it is likely that the time-varying cost channel reflects the cyclical nature of financial frictions.     

Monetary policy shocks: : Testing identification conditions under time-varying conditional volatility

We propose an empirical procedure, which exploits the conditional heteroscedasticity of fundamental disturbances, to test the targeting and orthogonality restrictions imposed in the recent VAR literature to identify monetary policy shocks. Based on U.S. monthly data for the post-1982 period, we reject the non-borrowed-reserve and interest-rate targeting procedures. In contrast, we present evidence supporting targeting procedures implying more than one policy variable. We also always reject the orthogonality conditions between policy shocks and macroeconomic variables. We show that using invalid restrictions often produces misleading policy measures and dynamic responses. These results have important implications for the measurement of policy shocks and their temporal effects as well as for the estimation of the monetary authority's reaction function.     

The day-of-the-week effect in conditional correlation

The day-of-the-week effect in the first and second moments of the return distribution is a well researched area. However, not many studies have attempted to identify this effect in the comovement or correlation of the markets. This paper models the day-of-the-week effect in the returns and the conditional correlation for some Asia-Pacific equity markets. The paper finds a Monday, Wednesday and Friday effects in the returns for some of the markets. The effect is totally absent in the returns for Australia, Japan and Korea. For the fifteen conditional correlation series estimated, a predominant Tuesday effect is detected for five series. Three series exhibit a Monday effect. A Thursday effect is detected between the Singapore market and the markets of Australia, Hong Kong and Thailand. The paper finds no consistent day-of-the-week effect in the returns and the correlations for this region. JEL Classification G15 · G14     

The time-varying diversifiability of corporate foreign exchange exposure

Estimating comovement measures for a large set of bilateral foreign exchange (FX) rates, I explore the relation between firm-level FX exposure and its time-varying diversifiability. For a sample of U.S. firms, the magnitude of FX exposure appears to increase during periods of low currency risk diversifiability. Additional results suggest that the introduction of the euro exacerbated the effect of diversifiability on developed market currency exposure. Moreover, the low diversifiability of emerging market currencies seems to have a stronger effect on FX exposure than the low diversifiability of developed market currencies.     

The conditional relation between dispersion and return

The main goal of this paper is to examine the conditional pricing effect of return dispersion on the cross section of returns. We observe a systematic conditional relation between dispersion and return even after controlling for market, size and book-to-market factors. However, we find that return dispersion risk is asymmetrically priced with a significantly positive premium observed during periods of large market gains only. The findings are found to be robust to alternative conditional specifications of market returns, suggesting asymmetric pricing effect of the return dispersion factor. We provide alternative explanations for the systematic risk captured by the return dispersion factor and discuss implications for portfolio management and corporate decisions.     

Evaluating stock returns with time-varying risk aversion driven by trend deviations from the consumption-to-wealth ratio: An analysis conditional on income levels

Based on the cointegrating relationship between consumption and wealth, we estimate the long run consumption-to-wealth ratio for each of five consumer income quintiles as well as national data for benchmarking purposes. Short run deviations from the consumption-to-wealth ratio for each quintile are examined for their ability to forecast changes in future consumption, income, housing values, and especially stock returns. We demonstrate that these trend deviations when combined with consumption growth in a multifactor model, significantly improve the ability of the dividend-to-price ratio to forecast future market returns over short and intermediate horizons for consumers in the highest-income quintile. This paper contributes to the financial economic literature by showing that the highest-income consumers are forecasting future stock returns with the help of the persistence in the dividend-to-price ratio and are modifying their consumption accordingly.