Converse trading strategies,intrinsic noise and the stylized facts of financial markets

This paper proposes a simple asset pricing model with three groups of traders: chartists who believe in the persistence of bull and bear markets, fundamentalists who bet on a reduction of the observed mispricing, and investors who follow a buy-and-hold strategy. The innovative feature of the model concerns the frequency of trading: rather than remaining constant over time, each agent in a group is only assumed to become active with a certain probability over a given market period. Depending on the trading strategy, part of this elementary kind of intrinsic noise is additive and another part is multiplicative. Using bootstrap and Monte Carlo methods, it is demonstrated that this combination can contribute to explaining the stylized facts of the daily returns on financial markets, such as volatility clustering, fat tails, and the autocorrelation patterns.     

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.     

Asset price and wealth dynamics under heterogeneous expectations

In order to characterize asset price and wealth dynamics arising from the interaction of heterogeneous agents with CRRA utility, a discrete-time stationary model in terms of return and wealth proportions (among different types of agents) is established. When fundamentalists and chartists are the main heterogeneous agents in the model, it is found that in the presence of heterogeneous agents the stationary model can have multiple steady states. The steady state is unstable when the chartists extrapolate strongly and (locally) stable when they extrapolate weakly. The convergence to the steady state follows an optimal selection principle - the return and wealth proportions tend to the steady state which has relatively higher return. More importantly, heterogeneity can generate instability which, under the stochastic processes of the dividend yield and extrapolation rates, results in switching of the return among different states, such as steady-state, periodic and aperiodic cycles from time to time. The model that is finally developed displays the essential characteristics of the standard asset price dynamics model assumed in continuous-time finance, in that the asset price is fluctuating around a geometrically growing trend. The model also displays the volatility clustering that is an essential feature of empirically observed asset returns.     

Earnings and Expected Returns

The aggregate dividend payout ratio forecasts excess returns on both stocks and corporate bonds in postwar U.S. data. High dividends forecast high returns. High earnings forecast low returns. The correlation of earnings with business conditions gives them predictive power for returns; they contain information about future returns that is not captured by other variables. Dividends and earnings contribute substantial explanatory power at short horizons. For forecasting long-horizon returns, however, only (scaled) stock prices matter. Forecasts of low long-horizon stock returns in the mid-1990s are caused not by earnings or dividends, but by high stock prices.     

The impact of FX central bank intervention in a noise trading framework

In this paper, we analyse the effectiveness of the direct central bank interventions using a new effectiveness criterion. To this aim, we investigate the effects of central bank interventions (CBI) in a noise trading model with chartists and fundamentalists. We first estimate a model in which chartists extrapolate past returns and fundamentalists forecast a mean reverting dynamics of the exchange rate towards a fundamental value. Then, we investigate the role of central bank interventions for explaining the switching properties between the two types of agents. We find evidence that in the medium run, interventions increase the proportion of fundamentalists and therefore exert some stabilizing influence on the exchange rate.     

Consumption, Aggregate Wealth, and Expected Stock Returns

This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption–wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption–aggregate wealth (human capital plus asset holdings) ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and that deviations from this shared trend summarize agents' expectations of future returns on the market portfolio.     

Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models

We compare forecasts of the realized volatility of the pound, mark and yen exchange rates against the dollar, calculated from intraday rates, over horizons ranging from one day to three months. Our forecasts are obtained from a short memory ARMA model, a long memory ARFIMA model, a GARCH model and option implied volatilities. We find intraday rates provide the most accurate forecasts for the one-day and one-week forecast horizons while implied volatilities are at least as accurate as the historical forecasts for the one-month and three-month horizons. The superior accuracy of the historical forecasts, relative to implied volatilities, comes from the use of high frequency returns, and not from a long memory specification. We find significant incremental information in historical forecasts, beyond the implied volatility information, for forecast horizons up to one week.     

Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?

We investigate the prediction of excess returns and fundamentals by financial ratios, which include dividend‐price ratios, earnings‐price ratios, and book‐to‐market ratios, by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess returns and fundamentals. Cyclical components predict increases in future stock returns, while stochastic trend components predict declines in future stock returns in long horizons. This helps explain previous findings that financial ratios in the absence of decomposition find weak predictive power in short horizons and some predictive power in long horizons. We also find both components predict fundamentals.     

Consumption Volatility Risk

We show that time variation in macroeconomic uncertainty affects asset prices. Consumption volatility is a negatively priced source of risk for a wide variety of test portfolios. At the firm level, exposure to consumption volatility risk predicts future returns, generating a spread across quintile portfolios in excess of 7% annually. This premium is explained by cross‐sectional differences in the sensitivity of dividend volatility to consumption volatility. Stocks with volatile cash flows in uncertain aggregate times require higher expected returns.     

Financial crises in efficient markets: How fundamentalists fuel volatility

When a financial crisis breaks out, speculators typically get the blame whereas fundamentalists are presented as the safeguard against excessive volatility. This paper proposes an asset pricing model where two types of rational traders coexist: short-term speculators and long-term fundamentalists, both sharing the same information set. In this framework, excess volatility not only exists, but is actually fueled by fundamental trading. Consequently, efficient markets are more volatile with a few speculators than with many speculators. Regulators should therefore be aware that efforts to limit rational speculation might, surprisingly, end up increasing volatility.     

Precious metals under the microscope: a high-frequency analysis

Taking advantage of a trades-and-quotes high-frequency database, we document the main stylized facts and dynamic properties of spot precious metals, i.e. gold, silver, palladium and platinum. We analyse the behaviours of spot prices, returns, volume and selected liquidity measures. We find clear evidence of periodic patterns matching the trading hours of the most active markets round-the-clock. The time series of spot returns have, thus, properties similar to those of traditional financial assets with fat tails, asymmetry, periodic behaviours in the conditional variances and volatility clustering. Gold (platinum) is the most (least) liquid and least (most) volatile asset. Commonality in liquidities of precious metals is very strong.     

Financial turbulence,systemic risk and the predictability of stock market volatility

This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility using a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective on market conditions, as they relate directly to portfolio performance metrics from both volatility and co-movement perspectives and, unlike other macro-financial indicators of uncertainty, or risk, can be integrated into diversification models within forecasting and portfolio design settings. Since the data for the two predictors are available at a weekly frequency, and our focus is to produce forecasts at the daily frequency, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models over both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared with the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also in correlation patterns play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes, in that financial indicators, which are directly associated with portfolio diversification performance metrics, can also be utilized for forecasting purposes, with significant implications for dynamic portfolio allocation strategies.     

Stock market overreactions to bad news in good times: a rational expectations equilibrium model

This article presents a dynamic, rational expectations equilibriummodel of asset prices where the drift of fundamentals (dividends)shifts between two unobservable states at random times. I showthat in equilibrium, investors' willingness to hedge againstchanges in their own 'uncertainty' on the true state makes stockprices overreact to bad news in good times and underreact togood news in bad times. I then show that this model is betterable than conventional models with no regime shifts to explainfeatures of stock returns, including volatility clustering,'leverage effects,' excess volatility, and time-varying expectedreturns.     

A multi-horizon comparison of density forecasts for the S&P 500 using index returns and option prices

We compare density forecasts of the S&P 500 index from 1991 to 2004, obtained from option prices and daily and 5-min index returns. Risk-neutral densities are given by using option prices to estimate diffusion and jump-diffusion processes which incorporate stochastic volatility. Three transformations are then used to obtain real-world densities. These densities are compared with historical densities defined by ARCH models. For horizons of two and four weeks the best forecasts are obtained from risk-transformations of the risk-neutral densities, while the historical forecasts are superior for the one-day horizon; our ranking criterion is the out-of-sample likelihood of observed index levels. Mixtures of the real-world and historical densities have higher likelihoods than both components for short forecast horizons.     

Nonparametric Estimation of State-Price Densities Implicit in Financial Asset Prices

Implicit in the prices of traded financial assets are Arrow–Debreu prices or, with continuous states, the state-price density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and we derive its asymptotic sampling theory. This estimator provides an arbitrage-free method of pricing new, complex, or illiquid securities while capturing those features of the data that are most relevant from an asset-pricing perspective, for example, negative skewness and excess kurtosis for asset returns, and volatility "smiles" for option prices. We perform Monte Carlo experiments and extract the SPD from actual S&P 500 option prices.     

Long memory in financial markets: A heterogeneous agent model perspective

During last decades, studies on asset pricing models witnessed a paradigm shift from rational expectation and representative agent to an alternative, behavioral view, where agents are heterogeneous and boundedly rational. In this paper, we model the financial market as an interaction of two types of boundedly rational investors — fundamentalists and chartists. We examine the dynamics of the market price and market behavior, which depend on investors' behavior and the interaction of the two types of investors. Numerical simulations of the corresponding stochastic model demonstrate that the model is able to replicate the stylized facts of financial time series, in particular the long-term dependence (long memory) of asset return volatilities. We further investigate the source of the long memory according to asset pricing mechanism of our model, and provide evidences of long memory by applying the modified R/S analysis. Our results demonstrate that the key parameter that has impact on the long memory is the speed of the price adjustment of the market maker at the equilibrium of demand and supply.     

Feedback Effects and Asset Prices

Feedback effects from asset prices to firm cash flows have been empirically documented. This finding raises a question for asset pricing: How are asset prices determined if price affects fundamental value, which in turn affects price? In this environment, by buying assets that others are buying, investors ensure high future cash flows for the firm and subsequent high returns for themselves. Hence, investors have an incentive to coordinate, which may generate self‐fulfilling beliefs and multiple equilibria. Using insights from global games, we pin down investors' beliefs, analyze equilibrium prices, and show that strong feedback leads to higher excess volatility.     

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.     

Two Trees

We solve a model with two i.i.d. Lucas trees. Although the correspondingone-tree model produces a constant price-dividend ratio andi.i.d. returns, the two-tree model produces interesting asset-pricingdynamics. Investors want to rebalance their portfolios afterany change in value. Because the size of the trees is fixed,prices must adjust to offset this desire. As a result, expectedreturns, excess returns, and return volatility all vary throughtime. Returns display serial correlation and are predictablefrom price-dividend ratios. Return volatility differs from cash-flowvolatility, and return shocks can occur without news about cashflows.     

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.