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- How does contagion affect general equilibrium asset prices? : [Version: March 13, 2013] (2013)
- This paper analyzes the equilibrium pricing implications of contagion risk in a Lucas-tree economy with recursive preferences and jumps. We introduce a new economic channel allowing for the possibility that endowment shocks simultaneously trigger a regime shift to a bad economic state. We document that these contagious jumps have far-reaching asset pricing implications. The risk premium for such shocks is superadditive, i.e. it is 2.5\% larger than the sum of the risk premia for pure endowment shocks and regime switches. Moreover, contagion risk reduces the risk-free rate by around 0.5\%. We also derive semiclosed-form solutions for the wealth-consumption ratio and the price-dividend ratios in an economy with two Lucas trees and analyze cross-sectional effects of contagion risk qualitatively. We find that heterogeneity among the assets with respect to contagion risk can increase risk premia disproportionately. In particular, big assets with a large exposure to contagious shocks carry significantly higher risk premia.

- When do jumps matter for portfolio optimization? : [Version 29 April 2013] (2013)
- We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces the jumps by a diffusion and solve the resulting problem analytically. Furthermore, we provide explicit bounds on the true optimal strategy and the relative wealth equivalent loss that do not rely on results from the true model. We apply our method to a calibrated affine model and fine that relative wealth equivalent losses are below 1.16% if the jump size is stochastic and below 1% if the jump size is constant and γ ≥ 5. We perform robustness checks for various levels of risk-aversion, expected jump size, and jump intensity.

- Partial information about contagion risk, self-exciting processes and portfolio optimization : [Version 18 April 2013] (2013)
- This paper compares two classes of models that allow for additional channels of correlation between asset returns: regime switching models with jumps and models with contagious jumps. Both classes of models involve a hidden Markov chain that captures good and bad economic states. The distinctive feature of a model with contagious jumps is that large negative returns and unobservable transitions of the economy into a bad state can occur simultaneously. We show that in this framework the filtered loss intensities have dynamics similar to self-exciting processes. Besides, we study the impact of unobservable contagious jumps on optimal portfolio strategies and filtering.

- Asset pricing under uncertainty about shock propagation : [version 18 november 2013] (2013)
- We analyze the equilibrium in a two-tree (sector) economy with two regimes. The output of each tree is driven by a jump-diffusion process, and a downward jump in one sector of the economy can (but need not) trigger a shift to a regime where the likelihood of future jumps is generally higher. Furthermore, the true regime is unobservable, so that the representative Epstein-Zin investor has to extract the probability of being in a certain regime from the data. These two channels help us to match the stylized facts of countercyclical and excessive return volatilities and correlations between sectors. Moreover, the model reproduces the predictability of stock returns in the data without generating consumption growth predictability. The uncertainty about the state also reduces the slope of the term structure of equity. We document that heterogeneity between the two sectors with respect to shock propagation risk can lead to highly persistent aggregate price-dividend ratios. Finally, the possibility of jumps in one sector triggering higher overall jump probabilities boosts jump risk premia while uncertainty about the regime is the reason for sizeable diffusive risk premia.

- Pricing two trees when mildew infests the orchard: how does contagion affect general equilibrium asset prices : [version: March 11, 2011) (2011)
- This paper analyzes the equilibrium pricing implications of contagion risk in a two-tree Lucas economy with CRRA preferences. The dividends of both trees are subject to downward jumps. Some of these jumps are contagious and increase the risk of subsequent jumps in both trees for some time interval. We show that contagion risk leads to large price-dividend ratios for small assets, a joint movement of prices in the case of a regime change from the calm to the contagion state, significantly positive correlations between assets, and large positive betas for small assets. Whereas disparities between the assets with respect to their propensity to trigger contagion barely matter for pricing, the prices of robust assets that are hardly affected by contagion and excitable assets that are severely hit by contagion differ significantly. Both in absolute terms and relatively to the market, the price of a small safe haven increases if the economy reaches the contagion state. On the contrary, the price of a small, contagion-sensitive asset exhibits a pronounced downward jump.

- Equilibrium asset pricing in networks with mutually exciting jumps (2014)
- We analyze the implications of the structure of a network for asset prices in a general equilibrium model. Networks are represented via self- and mutually exciting jump processes, and the representative agent has Epstein-Zin preferences. Our approach provides a exible and tractable unifying foundation for asset pricing in networks. The model endogenously generates results in accordance with, e.g., the robust-yetfragile feature of financial networks shown in Acemoglu, Ozdaglar, and Tahbaz-Salehi (2014) and the positive centrality premium documented in Ahern (2013). We also show that models with simpler preference assumptions cannot generate all these findings simultaneously.

- The dynamics of crises and the equity premium (2014)
- There has been a considerable debate about whether disaster models can rationalize the equity premium puzzle. This is because empirically disasters are not single extreme events, but long-lasting periods in which moderate negative consumption growth realizations cluster. Our paper proposes a novel way to explain this stylized fact. By allowing for consumption drops that can spark an economic crisis, we introduce a new economic channel that combines long-run and short-run risk. First, we document that our model can match consumption data of several countries. Second, it generates a large equity risk premium even if consumption drops are of moderate size.

- What is the impact of stock market contagion on an investor's portfolio choice? (2009)
- Stocks are exposed to the risk of sudden downward jumps. Additionally, a crash in one stock (or index) can increase the risk of crashes in other stocks (or indices). Our paper explicitly takes this contagion risk into account and studies its impact on the portfolio decision of a CRRA investor both in complete and in incomplete market settings. We find that the investor significantly adjusts his portfolio when contagion is more likely to occur. Capturing the time dimension of contagion, i.e. the time span between jumps in two stocks or stock indices, is thus of first-order importance when analyzing portfolio decisions. Investors ignoring contagion completely or accounting for contagion while ignoring its time dimension suffer large and economically significant utility losses. These losses are larger in complete than in incomplete markets, and the investor might be better off if he does not trade derivatives. Furthermore, we emphasize that the risk of contagion has a crucial impact on investors' security demands, since it reduces their ability to diversify their portfolios.

- Can tests based on option hedging errors correctly identify volatility risk premia? : [This version: May 24, 2004] (2004)
- Tests for the existence and the sign of the volatility risk premium are often based on expected option hedging errors. When the hedge is performed under the ideal conditions of continuous trading and correct model specification, the sign of the premium is the same as the sign of the mean hedging error for a large class of stochastic volatility option pricing models. We show, however, that the problems of discrete trading and model mis-specification, which are necessarily present in any empirical study, may cause the standard test to yield unreliable results.

- Can tests based on option hedging errors correctly identify volatility risk premia? : [Version 15 Januar 2004] (2004)
- This paper provides an in-depth analysis of the properties of popular tests for the existence and the sign of the market price of volatility risk. These tests are frequently based on the fact that for some option pricing models under continuous hedging the sign of the market price of volatility risk coincides with the sign of the mean hedging error. Empirically, however, these tests suffer from both discretization error and model mis-specification. We show that these two problems may cause the test to be either no longer able to detect additional priced risk factors or to be unable to identify the sign of their market prices of risk correctly. Our analysis is performed for the model of Black and Scholes (1973) (BS) and the stochastic volatility (SV) model of Heston (1993). In the model of BS, the expected hedging error for a discrete hedge is positive, leading to the wrong conclusion that the stock is not the only priced risk factor. In the model of Heston, the expected hedging error for a hedge in discrete time is positive when the true market price of volatility risk is zero, leading to the wrong conclusion that the market price of volatility risk is positive. If we further introduce model mis-specification by using the BS delta in a Heston world we find that the mean hedging error also depends on the slope of the implied volatility curve and on the equity risk premium. Under parameter scenarios which are similar to those reported in many empirical studies the test statistics tend to be biased upwards. The test often does not detect negative volatility risk premia, or it signals a positive risk premium when it is truly zero. The properties of this test furthermore strongly depend on the location of current volatility relative to its long-term mean, and on the degree of moneyness of the option. As a consequence tests reported in the literature may suffer from the problem that in a time-series framework the researcher cannot draw the hedging errors from the same distribution repeatedly. This implies that there is no guarantee that the empirically computed t-statistic has the assumed distribution. JEL: G12, G13 Keywords: Stochastic Volatility, Volatility Risk Premium, Discretization Error, Model Error