Estimating the performance attributes of Australian multi-sector managed funds within a dynamic Kalman filter framework

This paper examines the impact of Kalman filtering as a technique for modeling the risk levels of managed funds. Using a sample of Australian Multi-sector trusts we examine selectivity and market timing performance using conventional performance models alongside Kalman filter models that allow beta to vary via a random walk. Further, we consider the stability and asymmetry of these performance measures together with a measure of volatility timing arising from a cubic model of fund performance. We find that the positive selectivity (negative market timing) that stems from the conventional models is not present with the Kalman filter model. The Kalman filter model tends to show neutral performance for both. However, both models confirm a strong tendency toward negative volatility timing.     

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.     

Volatility timing: How best to forecast portfolio exposures

This paper investigates how best to forecast optimal portfolio weights in the context of a volatility timing strategy. It measures the economic value of a number of methods for forming optimal portfolios on the basis of realized volatility. These include the traditional econometric approach of forming portfolios from forecasts of the covariance matrix. Both naïve forecasts using simple historical averages, and those generated from econometric models are considered. A novel method, where a time series of optimal portfolio weights are constructed from observed realized volatility and direct forecast is also proposed. A number of naïve forecasts and the approach of directly forecasting portfolio weights show a great deal of merit. Resulting portfolios are of similar economic benefit to a number of competing approaches and are more stable across time. These findings have obvious implications for the manner in which volatility timing is undertaken in a portfolio allocation context.     

Conditional volatility in affine term-structure models: Evidence from Treasury and swap markets

We study the ability of three-factor affine term-structure models to extract conditional volatility using interest rate swap yields for 1991–2005 and Treasury yields for 1970–2003. For the Treasury sample, the correlation between model-implied and EGARCH volatility is between 60% and 75%. For the swap sample, this correlation is rather low or negative. We find that these differences in model performance are primarily due to the timing of the swap sample, and not to institutional differences between swap and Treasury markets. We conclude that the ability of multifactor affine models to extract conditional volatility depends on the sample period, but that overall these models perform better than has been argued in the literature.     

Target zones and conditional volatility: The role of realignments

This paper examines the relationship between the conditional volatility of target zone exchange rates and realignments of the system. To investigate this question, modified jump-diffusion Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and absolute value GARCH models are fit to six exchange rates of the Exchange Rate Mechanism (ERM) of the European Monetary System (EMS). Time-varying jump probability and absolute value GARCH models are effective in improving the fit of jump-diffusion models on target zone data. There is some evidence that conditional volatility is higher around the periods of realignments.     

Cash Flow Volatility, Prices and Price Volatility: An Experimental Study

The value of an asset is equal to the present value of its expected future cash flows. It is affected by the magnitude, timing and riskiness, or volatility, of the cash flows. We hypothesize that if the expected values of two assets?? cash flows are equal, the value of the asset with more volatile cash flows will be lower. Furthermore, we examine the impact of the volatility of cash flows on the volatility of prices. We consider a simple experimental environment where subjects trade in an asset which provides dividends from a known probability distribution. The expected value of the dividends is identical in all experimental treatments. The treatments vary with respect to the volatility of dividends. We find that when dividends are more volatile, transaction prices are lower. We also find that the volatility of prices is lower in the treatment with highly volatile dividends. In addition, as expected, trading volume is lower when cash flows are less volatile.     

Volatility‐Managed Portfolios

Managed portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean‐variance investors. We document this for the market, value, momentum, profitability, return on equity, investment, and betting‐against‐beta factors, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in volatility are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions. This rules out typical risk‐based explanations and is a challenge to structural models of time‐varying expected returns.     

Mutual fund volatility timing and management fees

This paper shows that compensation incentives partly drive fund managers’ market volatility timing strategies. Larger incentive management fees lead to less counter-cyclical or more pro-cyclical volatility timing. But fund styles or aggregate fund flows could also account for this relation; therefore, we control for them and find that the relation between fees and volatility timing still holds. Results show that less aggressive fund styles are associated with pro-cyclical volatility timing, and that volatility timing and flow timing are negatively related. We also find that pro-cyclical timing mostly improves funds’ average excess returns, Sharpe ratios, and alphas.     

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.     

The pricing of volatility risk in the US equity market

We analyze whether the pricing of volatility risk depends on the asset pricing framework applied in the tests, the specified volatility proxies, and the portfolio sorts used for spanning the asset universe. For this purpose, we compare the results using a macroeconomic and fundamental based asset pricing model using three proxies of volatility and uncertainty, using size/value sorted and industry sector portfolios. Our results reveal that the marginal pricing effect of the VIX volatility factor is strong and statistically significant throughout the models and specifications, while the effect of an EGARCH-based volatility factor is mixed, mostly smaller but with the correct sign. In most cases, the EGARCH factor does not impair the pricing effect of the VIX. The portfolio sorts have a substantial impact on the volatility premiums in both model frameworks. The size of the volatility risk premium is more uniform across the models if the industry sector portfolio sort is used. Finally, the size/value portfolio sort generates larger volatility risk premiums for both models.     

False discoveries in volatility timing of mutual funds

This paper examines the volatility timing of US mutual funds by controlling the false discovery rate to find out how many funds are truly countercyclical (procyclical) timing funds. Empirical results show that, given the whole universe of our sample funds, the percentages of countercyclical and procyclical volatility timing funds are about equal. We also find that while the standard approach, which simply counts the number of significant positive (negative) timing coefficients, does not incorporate false discoveries in volatility timing, it provides quite accurate volatility timing results. Finally, we find that the performance measures for an equally weighted portfolio of procyclical timing funds are greater than for an equally weighted portfolio of countercyclical timing funds in the in-sample test, consistent with our expectation that procyclical timers earn higher returns because they take on more risk. However, the countercyclical timing portfolio outperforms the procyclical timing portfolio in the out-of-sample test.     

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.     

The economic value of volatility timing with realized jumps

This paper comprehensively investigates the role of realized jumps detected from high frequency data in predicting future volatility from both statistical and economic perspectives. Using seven major jump tests, we show that separating jumps from diffusion improves volatility forecasting both in-sample and out-of-sample. Moreover, we show that these statistical improvements can be translated into economic value. We find that a risk-averse investor can significantly improve her portfolio performance by incorporating realized jumps into a volatility timing based portfolio strategy. Our results hold true across the majority of jump tests, and are robust to controlling for microstructure effects and transaction costs.     

Exploring Metropolitan Housing Price Volatility

This paper uses GARCH models and a panel VAR model to analyze possible time variation of the volatility of single-family home value appreciation and the interactions between the volatility and the economy, using a large quarterly data set that covers 277 MSAs in the U.S. from 1990:1 to 2002:2. We find evidence of time varying volatility in about 17% of the MSAs. Using volatility series estimated with GARCH models, we find that the volatility is Granger-caused by the home appreciation rate and GMP growth rate. On the other hand, the volatility Granger-causes the personal income growth rate but the impact is not economically significant.     

Portfolio analysis of intraday covariance matrix in the Greek equity market

The intraday nonparametric estimation of the variance–covariance matrix adds to the literature in portfolio analysis of the Greek equity market. This paper examines the economic value of various realized volatility and covariance estimators under the strategy of volatility timing. I use three types of portfolios: Global Minimum Variance, Capital Market Line and Capital Market Line with only positive weights. The estimators of volatilities and covariances use 5-min high-frequency intraday data. The dataset concerns the FTSE/ATHEX Large Cap index, FTSE/ATHEX Mid Cap index, and the FTSE/ATHEX Small Cap index of the Greek equity market (Athens Stock Exchange). As far as I know, this is the first work of its kind for the Greek equity market. Results concern not only the comparison of various estimators but also the comparison of different types of portfolios, in the strategy of volatility timing. The economic value of the contemporary non-parametric realized volatility estimators is more significant than this when the covariance is estimated by the daily squared returns. Moreover, the economic value (in b.p.s) of each estimator changes with the volatility timing.     

Timing the volatility risk of beta anomaly: Evidence from hedge fund strategies

Hedge funds are known to engage in the betting-against-beta (BAB) strategy arising from beta-anomaly-related market mispricing. This paper examines if equity-oriented hedge funds time the volatility risk when executing the BAB strategy. We apply realised and downside volatility risk measures to assess the BAB strategy. We show that for top volatility risk timers, older funds tend to be better risk timers, while among the bottom volatility risk timers, younger and larger-sized funds stand out as stronger timers of BAB volatility. We observe that the Long/Short Equity funds show evidence as the strongest volatility risk timers of BAB strategy when the market condition turned bad. This is supported by their other effective timing strategies at the same time, including timing the market sentiment. Our findings provide important references for private investors when selecting hedge funds as risk management is crucial to the success/failure of any investments.     

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.     

Optimal portfolios when volatility can jump

We consider an asset allocation problem in a continuous-time model with stochastic volatility and jumps in both the asset price and its volatility. First, we derive the optimal portfolio for an investor with constant relative risk aversion. The demand for jump risk includes a hedging component, which is not present in models without volatility jumps. We further show that the introduction of derivative contracts can have substantial economic value. We also analyze the distribution of terminal wealth for an investor who uses the wrong model, either by ignoring volatility jumps or by falsely including such jumps, or who is subject to estimation risk. Whenever a model different from the true one is used, the terminal wealth distribution exhibits fatter tails and (in some cases) significant default risk.     

The Effect of Futures Market Volume on Spot Market Volatility

There has been considerable interest, both academic and regulatory, in the hypothesis that the higher is the volume in the futures market, the greater is the destabilizing effect on the stock market. We show that conventional approaches, such as adding exogenous variables to GARCH models, may lead to false inferences in tests of this question. Using a stochastic volatility model, we show that, contrary to regulatory concern and the results of other papers, contemporaneous informationless futures market trading has no significant effect on spot market volatility.     

Recovery of Real Estate Returns for Portfolio Allocation

Appraisal-based return indexes may not approximate the true real estate return distributions because of understated return volatility. Recovery of returns from reported, appraisal-based returns may be possible by evoking models to correct for appraisal-based smoothing of the second moment. Because recovery intentionally alters the volatility of the reported return distribution and the correlations among assets in the portfolio, the weights to real estate are likely affected. Our examination of the portfolio implications of altering the return distribution indicates that weights may be quite sensitive to the effects of recovery across a reasonable range of correlation regimes. A comparative analysis of several recovery models reveals that all models achieve the objective of inflating the volatility of reported returns. However, the models also change the mean of the return distribution, which either counteracts or magnifies the effect of the volatility change on allocations. These findings bring into question the applicability of recovery models in their current form.