Stability, Asymmetry and Seasonality of Fund Performance: An Analysis of Australian Multi‐sector Managed Funds

Using a sample of Australian Multi-sector trusts we examine selectivity and market timing performance and extend the analysis to include the relatively new measure of volatility timing. This is of particular relevance to our data set, as high levels of volatility persistence are prevalent in Australia. In addition we consider the stability, asymmetry and seasonality of the various performance and risk measures. A survivorship adjustment procedure is also employed in order to assess the impact of survivorship on selectivity, market timing and volatility timing performance.     

The Investment Performance of U.S. Equity Pension Fund Managers: An Empirical Investigation

This paper presents an empirical examination of the selectivity and market timing performance of a sample of U.S. equity pension fund managers. Regardless of the choice of benchmark portfolio or estimation model, the average selectivity measure is positive and the average timing measure is negative. However both selectivity and timing appear to be somewhat sensitive to the choice of a benchmark when managers are classified by investment style. Meta-analysis revealed some real variation around the mean values for each measure. The 80 percent probability intervals for selectivity revealed that the best managers produced substantial risk-adjusted excess returns. We also found a negative correlation between selectivity and timing, but we argue that the observed negative correlation in our data is largely an artifact of negatively correlated sampling errors for the two estimates.     

Macroeconomic fundamentals,jump dynamics and expected volatility

In this paper, we develop a new volatility model capturing the effects of macroeconomic variables and jump dynamics on the stock volatility. The proposed GARCH-Jump-MIDAS model is applied to the S&P 500 index. Our in-sample results indicate that macroeconomic activities have important impacts on aggregate market volatility. Out-of-sample evidence suggests that our model with macroeconomic variables significantly outperform a wide range of competitors including the original GARCH(1,1), GARCH-MIDAS and GJR-A-MIDAS models. The volatility timing results also show that the information from jumps and macroeconomic activity is helpful for improving the portfolio performance.     

Estimating the Dynamics of Mutual Fund Alphas and Betas

This article develops a Kalman filter model to track dynamicmutual fund factor loadings. It then uses the estimates to analyzewhether managers with market-timing ability can be identifiedex ante. The primary findings are as follows: (i) Ordinary leastsquares (OLS) timing models produce false positives (nonzeroalphas) at too high a rate with either daily or monthly data.In contrast, the Kalman filter model produces them at approximatelythe correct rate with monthly data; (ii) In monthly data, thoughthe OLS models fail to detect any timing among fund managers,the Kalman filter does; (iii) The alpha and beta forecasts fromthe Kalman model are more accurate than those from the OLS timingmodels; (iv) The Kalman filter model tracks most fund alphasand betas better than OLS models that employ macroeconomic variablesin addition to fund returns.     

Performance evaluation of balanced pension plans

This paper examines the ability of balanced pension plan managers to successfully time the equity and bond market and select the appropriate assets within these markets. In order to evaluate both market timing abilities in these balanced pension plans, we extend the traditional equity market timing models to also account for bond market timing. As far as we know, we are among the first to apply this multifactor timing model to investigate equity and bond market timing simultaneously. This performance evaluation has been conducted on two samples of Spanish balanced pension plans, one with Euro Zone and one with World investment focus. This allows us to decompose managers’ skills into three components: selectivity, equity market timing, and bond market timing. Our findings suggest that the average stock-picking ability of pension plans is positive. World schemes tend to have positive bond timing skills, while Euro Zone pension plans are on average not able to time equity or bond markets.     

Timing and selectivity in Portuguese mutual fund performance

We extend the international evidence on timing and selectivity skills of fund managers by applying the Henriksson and Merton [Henriksson, R., Merton, R., 1981. On market timing and investment performance. II. Statistical procedures for evaluating forecasting skills. J. Bus. 54, 513–533] model to Portuguese based mutual funds investing in local, European and International equity.

The results show that managers do not exhibit selectivity and timing abilities, and there is even some evidence of negative timing. Furthermore, we observe a distance effect on stock selection performance, since fund managers that invest locally seem to perform better that those who invest in foreign markets. However, this effect is reverted with respect to market timing skills of fund managers, suggesting that International fund managers are more focused in market timing strategies.     

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.     

SELECTIVITY,MARKET TIMING,AND RANDOM BETA BEHAVIOR OF MUTUAL FUNDS: A GENERALIZED MODEL

This paper presents a generalized varying parameter model to investigate the performance of mutual funds. The model allows beta nonstationarity to include both market timing and random beta behavior; therefore, it can be regarded as a general case of previous research. Forty-three funds with a wide range of objectives are examined. The generalized varying parameter results indicate that about 30 percent of the funds show selectivity, 19 percent have random betas, and 14 percent indicate significant, yet negative, market timing performance. Therefore, mutual funds, as a group, show no market timing ability. The apparent ability to select undervalued securities, however, seems to conflict with the efficient markets hypothesis.     

The performance of US bond mutual funds

We evaluate the performance of the US bond mutual fund industry using a comprehensive sample of bond funds over a long time period from January 1998 to February 2017. In this one study, we examine bond fund selectivity, market timing and performance persistence. We evaluate bond funds relative to their self-declared benchmarks and in terms of both gross-of-fee returns and net-of-fee returns. We document considerable abnormal performance among funds both to the fund (gross returns) and to the investor (net returns). Bond fund performance is found to be superior in the post financial crisis period. However, past strong performance cannot be relied upon to predict future performance. Finally, while some funds exhibit market timing ability; we find a predominance of negative market timing among US bond mutual funds.     

Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk

We explore the cross‐sectional pricing of volatility risk by decomposing equity market volatility into short‐ and long‐run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short‐run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long‐run component relates to business cycle risk. Furthermore, a three‐factor pricing model with the market return and the two volatility components compares favorably to benchmark models.     

Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks

Oil markets are subject to extreme shocks (e.g. Iraq’s invasion of Kuwait), causing the oil market price exhibits extreme movements, called jumps (or spikes). These jumps pose challenges on oil market volatility forecasting using conventional volatility dynamic models (e.g. GARCH model) This paper characterizes dynamics of jumps in oil market price using high frequency data from three perspectives: the probability (or intensity) of jump occurrence, the sign (e.g. positive or negative) of jumps, and the concurrence with stock market jumps. And then, the paper exploits predictive ability of these jump-related information for oil market volatility forecasting under the mixed data sampling (MIDAS) modeling framework. Our empirical results show that augmenting standard MIDAS model using the three jump-related information significantly improves the accuracy of oil market volatility forecasting. The jump intensity and negative jump size are particularly useful for predicting future oil volatility. These results are widely consistent across a variety of robustness tests. This work provides new insights on how to forecast oil market volatility in the presence of extreme shocks.     

Financing the green projects: Market efficiency and volatility persistence of green versus conventional bonds,and the comparative effects of health and financial crises

The establishment of the green bond market is an attempt to attain environmental sustainability from the finance perspective. One of the factors that could either hinder or promote this course is the degree of market efficiency of the market. Against this background, this study comparatively examines the market efficiency of the U.S. green and conventional bonds. In addition, the performance of both markets is investigated during global health and financial crises. Applying the fractional integration technique, we find that the overall green and conventional bonds markets are still inefficient, indicating that green and conventional investors can make excess gains by predicting the future trends of bond prices. There is also evidence that the green bond market exhibits higher volatility persistence during the global health crisis than during the global financial crisis. These findings are corroborated by the GARCH-based models, whose results show that volatility shocks are more persistent in the green bond market during the health crisis than during the financial crisis. However, the conventional bond market observes persistence in volatility shocks during both events. Towards sustainable environment goals, these results have relevant implications for green investors and policy makers.     

Multi-Factor Cox-Ingersoll-Ross Models of the Term Structure: Estimates and Tests from a Kalman Filter Model

This paper presents a method for estimating multi-factor versions of the Cox-Ingersoll-Ross (1985b) model of the term structure of interest rates. The fixed parameters in one, two, and three factor models are estimated by applying an approximate maximum likelihood estimator in a state-space model using data for the U.S. treasury market. A nonlinear Kalman filter is used to estimate the unobservable factors. Multi-factor models are necessary to characterize the changing shape of the yield curve over time, and the statistical tests support the case for two and three factor models. A three factor model would be able to incorporate random variation in short term interest rates, long term rates, and interest rate volatility.     

Conditional risk and performance evaluation: Volatility timing, overconditioning, and new estimates of momentum alphas

Unconditional alphas are biased when conditional beta covaries with the market risk premium (market timing) or volatility (volatility timing). We demonstrate an additional bias (overconditioning) that can occur any time an empiricist estimates risk using information, such as a realized beta, that is not available to investors ex ante. Calibrating to U.S. equity returns, volatility timing and overconditioning can plausibly impact alphas more than market timing, which has been the focus of prior literature. To correct market- and volatility-timing biases without overconditioning, we show that incorporating realized betas into instrumental variables estimators is effective. Empirically, instrumentation reduces momentum alphas by 20-40%. Overconditioned alphas overstate performance by up to 2.5 times. We explain the sources of both the volatility-timing and overconditioning biases in momentum portfolios.     

Time-varying fund manager skills of socially responsible investing (SRI) funds in developed and emerging markets

We examine stock selectivity and timing abilities in the market-wide return, volatility and liquidity of SRI fund managers. We find that multi-dimensional fund manager skills are time-varying and persistent in the short run, with developed market funds exhibiting longer persistence in all dimensions. Fund manager skills tend to be affected by fund characteristics (i.e., expense ratio, fund size, turnover and management tenure) and market characteristics (i.e., ESG market capitalization, mandatory ESG regulation and 10–2 yield spread). Fund managers of developed (emerging) market funds outperform (underperform) the market indices. For both fund types, fund managers possess exceptional volatility and liquidity timing despite poor return timing. Moreover, fund managers focus more (less) on timing the market’s return and less (more) on picking stocks when the prospect of recession keeps increasing (decreasing). Interestingly, if fund managers attempt to time the market-wide return or liquidity, stock selectivity will be worsened by their timing behavior.     

Combining long memory and level shifts in modelling and forecasting the volatility of asset returns

We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.     

The dynamics of the volatility skew: A Kalman filter approach

Much attention has been devoted to understanding and modeling the dynamics of implied volatility curves and surfaces. This is crucial for both trading, pricing and risk management of option positions. We suggest a simple, yet flexible, model, based on a discrete and linear Kalman filter updating of the volatility skew. From a risk management perspective, we assess whether this model is capable of producing good density forecasts of daily returns on a number of option portfolios. We also compare our model to the sticky-delta and the vega–gamma alternatives. We find that it clearly outperforms both alternatives, given its ability to easily account for movements of different nature in the volatility curve.     

Spillover effects of the 2008 global financial crisis on the volatility of the Indian equity markets: Coupling or uncoupling? A study on sector-based data

This paper focuses on the following question: has the global financial stress in the US markets during the subprime crisis induced a persistent volatility of Indian equity stocks? We answer this question using sector-based data and we propose a simple stochastic volatility model augmented with exogenous inputs (financial stress indicators in the US market). We derive analytically the autocorrelation of the squared returns using cross-moments and estimate the impact of several variables such as the CDS spreads, the ABCP spreads, market liquidity, the volatility of the S&P 500 using a Kalman filter approach with the impact captured through Almon polynomials. We find a strong evidence of persistent volatility irrespective of the sector and interpret this finding as the result of two factors: the lower liquidity of the Indian equity markets during the subprime crisis and a wake-up call effect.     

A global approach to mutual funds market timing ability

In this paper, we globally investigate market timing abilities of mutual fund managers from the three perspectives: market return, market-wide volatility and aggregate liquidity. We propose a new specification to study market timing. Instead of considering an average market exposure for mutual funds, we allow mutual fund market betas to follow a random walk in the absence of market timing ability. As a consequence, we capture market exposure dynamics which is really due to manager market timing skills while allowing dynamics to come from other sources than market timing. We find that on average 6% of mutual funds display return market timing abilities while this percentage amounts to respectively 13% and 14% for volatility and liquidity market timing. We also analyze market timing by investment strategies and for surviving and dead funds. Dead funds exhibit lower volatility and liquidity timing skills than live funds.     

Estimating continuous-time stochastic volatility models of the short-term interest rate: a comparison of the generalized method of moments and the Kalman filter

This paper examines a model of short-term interest rates that incorporates stochastic volatility as an independent latent factor into the popular continuous-time mean-reverting model of Chan et al. (J Financ 47:1209–1227, 1992). I demonstrate that this two-factor specification can be efficiently estimated within a generalized method of moments (GMM) framework using a judicious choice of moment conditions. The GMM procedure is compared to a Kalman filter estimation approach. Empirical estimation is implemented on US Treasury bill yields using both techniques. A Monte Carlo study of the finite sample performance of the estimators shows that GMM produces more heavily biased estimates than does the Kalman filter, and with generally larger mean squared errors.