Pricing Liquidity Risk with Heterogeneous Investment Horizons

We develop an asset pricing model with stochastic transaction costs and investors with heterogeneous horizons. Depending on their horizon, investors hold different sets of assets in equilibrium. This generates segmentation and spillover effects for expected returns, where the liquidity (risk) premium of illiquid assets is determined by investor horizons and the correlation between liquid and illiquid asset returns. We estimate our model for the cross-section of U.S. stock returns and find that it generates a good fit, mainly due to a combination of a substantial expected liquidity premium and segmentation effects, while the liquidity risk premium is small

Generalized asset pricing: Expected Downside Risk-Based Equilibrium Modelling

We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns. On the other hand, our asset pricing model is based on loss-averse investors of Prospect Theory, through which we implement the risk-seeking behaviour of investors in a dynamic setting. By including EDR in our proposed model unrealistic assumptions of commonly used equilibrium models - such as the exclusion of risk-seeking or price-maker investors and the assumption of unlimited leverage opportunity for a unique interest rate - can be omitted. Therefore, we argue that based on more realistic assumptions our model is able to describe equilibrium expected returns with higher accuracy, which we support by empirical evidence as well.Comment: 55 pages, 15 figures, 1 table, 3 appandices, Econ. Model. (2015

Does the Failure of the Expectations Hypothesis Matter for Long-Term Investors

We consider the consumption and portfolio choice problem of a long-run investor when the term structure is affine and when the investor has access to nominal bonds and a stock portfolio. In the presence of unhedgeable inflation risk, there exist multiple pricing kernels that produce the same bond prices, but a unique pricing kernel equal to the marginal utility of the investor. We apply our method to a three-factor Gaussian model with a time-varying price of risk that captures the failure of the expectations hypothesis seen in the data. We extend this model to account for time-varying expected inflation, and estimate the model with both inflation and term structure data. The estimates imply that the bond portfolio for the long-run investor looks very different from the portfolio of a mean-variance optimizer. In particular, the desire to hedge changes in term premia generates large hedging demands for long-term bonds.

Why a simple herding model may generate the stylized facts of daily returns: Explanation and estimation

The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model's goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified. --structural stochastic volatility,method of simulated moments,autocorrelation pattern,fat tails,bootstrapped p-values

Ambiguity in asset pricing and portfolio choice: a review of the literature

A growing body of empirical evidence suggests that investors’ behavior is not well described by the traditional paradigm of (subjective) expected utility maximization under rational expectations. A literature has arisen that models agents whose choices are consistent with models that are less restrictive than the standard subjective expected utility framework. In this paper we conduct a survey of the existing literature that has explored the implications of decision-making under ambiguity for financial market outcomes, such as portfolio choice and equilibrium asset prices. We conclude that the ambiguity literature has led to a number of significant advances in our ability to rationalize empirical features of asset returns and portfolio decisions, such as the empirical failure of the two-fund separation theorem in portfolio decisions, the modest exposure to risky securities observed for a majority of investors, the home equity preference in international portfolio diversification, the excess volatility of asset returns, the equity premium and the risk-free rate puzzles, and the occurrence of trading break-downs.Capital assets pricing model ; Investments

Dynamic Trading and Asset Prices: Keynes vs. Hayek

We investigate the dynamics of prices, information and expectations in a competitive, noisy, dynamic asset pricing equilibrium model. We look at the bias of prices as estimators of fundamental value in relation to traders' average expectations and note that prices are more (less) biased than average expectations if and only if traders over- (under-) rely on public information with respect to optimal statistical weights. We find that prices are biased in relation to average expectations whenever traders speculate on short-run price move- ments. In a market with long term traders, over-reliance on public information obtains if noise trade increments are correlated enough and/or there is low enough residual uncertainty in the payoff. This defines a “Keynesian” region; the complementary region is “Hayekian” in that prices are less biased than average expectations in the estimation of fundamental value. The standard case of no residual uncertainty and noise trading following a random walk is on the frontier of the two regions. With short-term traders there typically are two equilibria, with the stable (unstable) one displaying over- (under-) reliance on public information.Price bias, long and short-term trading, multiple equilibria, average expectations, higher order beliefs, over-reliance on public information.

Dealers' hedging of interest rate options in the U.S. dollar fixed-income market

Despite investors' willingness to hold a variety of financial assets and risks, a significant share of interest rate options exposures remains in the hands of dealers. This concentration of risk makes the interest rate options market an ideal place to explore the effects of dealers' dynamic hedging on underlying markets. Using data from a global survey of derivatives dealers and other sources, this article estimates the potential impact of dynamic hedging by interest rate options dealers on the fixed-income market. The author finds that for short-term maturities, turnover volume in the most liquid hedging instruments is more than large enough to absorb dealers' dynamic hedges. For medium-term maturities, however, an unusually large interest rate shock could lead to hedging difficulties.Hedging (Finance) ; Options (Finance)

The Factor-Spline-GARCH Model for High and Low Frequency Correlations

We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns. High frequency correlations mean revert to slowly varying functions that characterize long-term correlation patterns. We associate such term behavior with low frequency economic variables, including determinants of market and idiosyncratic volatilities. Flexibility in the time varying level of mean reversion improves the empirical fit of equity correlations in the US and correlation forecasts at long horizons.Yield curve, forecasting, economic activity