Stocks, Bonds, and Long-Run Consumption Risks

Hasseltoft, Henrik

doi:10.1017/s0022109012000075

Cited by 57 publications

(29 citation statements)

References 74 publications

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“…See e.g. Piazzesi and Schneider (2006), Hasseltoft (2012), Bansal and Shaliastovich (2013) and others who provide empirical evidence for this channel for a single non-durable consumption good. Recently, Eraker, Shaliastovich, and Wang (2014) extended the literature in the context of durable consumption as well.…”

Section: Introductionmentioning

confidence: 99%

Macroeconomic Effects of Inflationary Shocks with Durable and Non-Durable Consumption

Mallick

Mohsin

2016

Open Econ Rev

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In this paper we show that inflation, both in the short and long run, negatively affects durable and non-durable consumption and output, and positively influences the current account balance. In particular, the impact of inflation is more pronounced on durable relative to non-durable goods. Using quarterly data from Canada, the UK and the USA, we demonstrate that these findings are consistent and robust across different econometric specifications. An open economy model with durable and non-durable consumption, households with labor/leisure choice and money introduced through cash-in-advance (CIA) constraint on consumption expenditure alone could meaningfully explain these empirical observations. Our empirical findings of non-neutral and negative effects of inflation on real growth are significant for many economic models in the asset pricing literature that support a negative co-movement of stock prices and expected inflation. Moreover, such positive inflation premium in our model could provide economic explanation for a positive slope of the nominal term structure in the data.

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Section: Introductionmentioning

confidence: 99%

Macroeconomic Effects of Inflationary Shocks with Durable and Non-Durable Consumption

Mallick

Mohsin

2016

Open Econ Rev

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“…The first estimation strategy in Panel A is the SMM approach adapted from Hasseltoft (2012), the second estimation strategy in Panel B is the GMM approach by Constantinides and Ghosh (2011). Panel 1 lists the moments of macroeconomic LRR variables, Panel 2 contains the moments of financial LRR variables, and Panel 3 lists the moments from a prediction relationship.…”

Section: Tables and Figuresmentioning

confidence: 99%

“…For that purpose, they extend the base model with an inflation process; additional complexity is introduced by two independent SV processes. Hasseltoft (2012) also includes inflation into the LRR framework to model stock and bond markets jointly. Constantinides and Ghosh (2011) make use of the possibility to express the latent model variables as functions of observables, which in turn permits the use of the generalized method of moments (GMM).…”

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confidence: 99%

Give Me Strong Moments and Time - Combining GMM and SMM to Estimate Long-Run Risk Asset Pricing Models

Grammig

Schaub

2014

SSRN Journal

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The long-run consumption risk (LRR) model is a promising approach to resolve prominent asset pricing puzzles. The simulated method of moments (SMM) provides a natural framework to estimate its deep parameters, but caveats concern model solubility and weak identification. We propose a twostep estimation strategy that combines GMM and SMM, and for which we elicit informative macroeconomic and financial moment matches from the LRR model structure. In particular, we exploit the persistent serial correlation of consumption and dividend growth and the equilibrium conditions for market return and risk-free rate, as well as the model-implied predictability of the risk-free rate. We match analytical moments when possible and simulated moments when necessary and determine the crucial factors required for both identification and reasonable estimation precision. A simulation study-the first in the context of long-run risk modeling-delineates the pitfalls associated with SMM estimation of a non-linear dynamic asset pricing model. Our study provides a blueprint for successful estimation of the LRR model. Key words:asset pricing, long-run risk, simulated method of moments JEL: C58, G10, G12 * We are grateful to H. Hasseltoft for sharing his Matlab code for the computation of the endogenous LRR parameters and to J. Krause for providing a Matlab implementation of the CMAES algorithm. We retain the responsibility for all remaining errors.

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“…Examples are the papers by Bansal, Gallant, and Tauchen (2007), Kiku (2006), Malloy, Moskowitz, andVissing-Jorgensen (2009), Bansal andShaliastovich (2011), Kaltenbrunner and Lochstoer (2010) and Colacito and Croce (2011), to name just a few. Bansal, Dittmar, and Lundblad (2005) and Hansen, Heaton, and Li (2008) apply LRR models in order to explain cross-sectional variation in equity returns, while Hasseltoft (2012) and Bansal and Shaliastovich (2013) use the LRR framework to explain return dynamics in bond markets. 4 Most relevant to our research question are two papers which apply the LRR framework to derivative securities.…”

Section: Introductionmentioning

confidence: 99%

Level and Slope of Volatility Smiles in Long-Run Risk Models

Branger¹,

Rodrigues

Schlag

2017

SSRN Journal

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Non-Technical SummaryUnderstanding the fundamental economic risks that drive asset prices is a central research question in finance. Derivatives markets, in conjunction with their underlying stock markets, provide us with a rich set of data to study this topic. It is well established that there are two main types of risks driving asset prices. First, there is diffusive volatility, which measures the amount of uncertainty generated by 'usual' movements in economic fundamentals In asset pricing models, this risk is mostly represented by normally distributed innovations. Second, there are large, and infrequent shocks which generate rapid (and mostly adverse) changes in these fundamentals. These are usually represented by jumps. Due to the sheer size of the movements in state variables (and consequently in prices) conditional on the occurrence of a jump, this type of risk is usually much more dramatic than that generated by diffusive factors, it is harder to hedge and thus has much severer implications for wealth dynamics and risk premia. In many models it is assumed that the risk of such a jump occurring is the higher, the higher the current level of diffusive volatility, which in these models implies a perfect correlation of both risk types. We show in this paper that a simple separation of volatility and jump risk suffices for the model to match two basic and easily observable characteristics of the implied volatility (IV) smile for S&P 500 index options, namely its level and its slope. This improvement in explanatory power is achieved in addition to the matching of basic asset pricing moments like the equity premium, the volatility of the equity premium, the variance premium, and excess return predictability by the price-dividend ratio and the variance risk premium. The important feature of our model from an economic standpoint is therefore that the probability of a large shock is not perfectly correlated with the 'usual' uncertainty represented by diffusive variance. Roughly speaking, the level of the IV smile represents the amount of volatility, while the slope measures jump risk. In contrast to the hypotheses implied by standard models, the correlation between level and slope is far from perfectly positive, and it is even negative with a value around -0.3 for our sample for the S&P 500 index from 1996 to 2015, and even in a general nonlinear sense, the goodness of fit for slope as a function of level is rather low. Our model with separate processes for volatility and jump risk is able to explain these key properties of the data, while standard models are clearly not. The weak link between level and slope is not just of technical interest in the context of the asset pricing model discussed in our paper, but the two risk types related to level and slope are also fundamentally different economically. Overall, we find that both level and slope are related to macro variables, but the degree to which level can be explained by a collection of such variables is much higher than for slope. To sum up, we propose a simp...

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Stocks, Bonds, and Long-Run Consumption Risks

Cited by 57 publications

References 74 publications

Macroeconomic Effects of Inflationary Shocks with Durable and Non-Durable Consumption

Macroeconomic Effects of Inflationary Shocks with Durable and Non-Durable Consumption

Give Me Strong Moments and Time - Combining GMM and SMM to Estimate Long-Run Risk Asset Pricing Models

Level and Slope of Volatility Smiles in Long-Run Risk Models

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