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 Capital Gains Taxation and Asset Price Cycles                                                              Download!

Abstract. Can capital gains taxes prevent asset price cycles? Using an asset pricing model with learning I show that the capital gains tax dampens a key volatility engine -the price-expectations feedback loop- by reducing the sensitivity of prices to expectations fluctuations and by anchoring expectations around their fundamental value. With this theory in hand, I show that the decline in taxes observed the last decades in the United States can explain a substantial part of the increase in the Price-Dividend ratio fluctuations along with the surge in stock market valuations and the equity premium. In fact, the model suggests that the rise in the Price-Dividend ratio volatility would have been entirely avoided in the absence of tax cuts despite the fall in safe real interest rates. Furthermore, I characterize the optimal policy in an economy where asset price volatility leads to business cycles. The optimal tax counteracts too optimistic/pessimistic beliefs, preventing beliefs-driven macroeconomic fluctuations. In some cases, it equals 100%.

Presented at: SAEe2020, EcoMod2021, UC3M, U. Mannheim, UIB, UAB, BSE, ENTER,  SAEe2022*.

Work in progress

The Fiscal Channel of Quantitative Easing (with Eddie Gerba and Luis E. Rojas)

Abstract. Central Bank’s power to affect the economy through asset purchases depends heavily on the associated fiscal policy. The common assumption is that fiscal policy reacts by keeping the pre-existing allocation of resources in the economy unchanged. However, is that what a rational government would do? In realistic environments with distortionary taxation, productive spending or limited participation in financial markets, it is optimal for governments to use Quantitative Easing (QE) gains to reallocate resources. Anticipating that, forward looking agents would adjust their current savings-consumption choices, influencing aggregate demand and asset prices. This is the fiscal channel of QE. Looking backward, we document the quantitative relevance of this channel in different QE rounds. Looking forward, this channel sheds light on new uses of QE involving real resource reallocation as green corporate bond programs or fiscal transfers within a monetary union. 

Presented at: Bank of England, T2M Conference London 2022, UAB, 53rd  MMF conference, SAEe2022*, 2023 ASSA meeting*.

Dematerialization in part explained by the burst of the housing bubble (with Marina Requena)

Abstract. We document that a set of countries grew their GDP while decreasing their Material Footprint (MF) over the 2007-2017 decade, breaking the previous trend. This paper analyzes the drivers of this absolute dematerialization. In accounting terms, it simply reflects a reduction in Material Intensity (MI) typically associated with technology. Nonetheless, we show that a wide range of variables related to technology can only explain between 2% and 10% of the MI variation. Alternatively, we hypothesize that the observed dematerialization is in part a cyclical phenomenon resulting from the housing prices bust that depresses construction activity and, under some conditions, hits MF harder than GDP. Indeed, the data analysis reveals that housing prices and construction explain between 19% and 46% of the dematerialization variance. Besides, we show that the absence of the housing boom would have accelerated the dematerialization, although being insufficient to bring MF within their sustainable limits.

The Anatomy of Stock Market Cycles (with Adrian Ifrim)

Abstract. This paper shows that a model of learning about capital gains with heterogeneous expectations can jointly explain several old and new facts about stock prices, portfolio adjustments and survey expectations. Our key innovation is to model the whole distribution of expectations in a way consistent with many survey stylized facts: perpetual disagreement, procyclical expectations/disagreement and forecast error predictability. Using this model we replicate hard-to-reconcile facts regarding market volatility, expected returns, disagreement and trading. A typical boom would follow this sequence: i) an income or sentiment shock make investors more willing to invest in equities, driving up prices ii) the initial price increase make all investors more optimistic, reinforcing the cycle iii) however, certain conservative investors are reluctant to be drawn by this wave of optimism iv) this heterogeneous reaction of expectations raises disagreement and trading. Therefore, disagreement and trading appear as a consequences of a bullish market and not as a driving force.

Solving asset pricing models with learning through the Parameterized Expectations Algorithm