I am very happy to share that my paper "Optimal Taxation with Home Ownership and Wealth Inequality" with Pietro Reichlin has been now accepted for publication at the @RevEconDyn [1/n] #EconTwitter
In the paper we consider optimal taxation in a model with wealth-poor and wealth-rich households, where wealth derives from business capital and home ownership, and investigate the consequences of a rising wealth inequality at steady state on these tax rates [2/n]
We find that the optimal tax structure includes some taxation of labor, zero taxation of financial and business capital, and critically a housing wealth tax on the wealth-rich households and a housing subsidy on the wealth-poor households [3/n]
We also find that the source of increasing wealth matters in the optimal balance between labor and housing wealth taxes [4/n]
When wealth rises because of a rising public debt, then the optimal income tax rises with inequality, the housing subsidy is flat while the housing tax on rich households falls substantially [5/n]
When instead wealth rises because of a falling real rate, then the optimal income tax falls with inequality, the housing tax on the rich households rises strongly, and the housing subsidy falls a bit [6/n]
In both scenarios, housing subsidies are small, while the hosuing tax rates are large (see figure) [7/n]
Many more details and economic intuition in the paper (working paper available at ssrn.com/abstract=34891…) [8/n]
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I am very excited to share my new working paper titled “#Crypto Risk Premia” (with Daniele Massacci, @RubinMirco and Dario Ruzzi). A short 🧵 follows. Please, share it if you like it. Comments are very welcome [1/n]
Before “crypto winter” hit markets at the beginning of 2022, cryptocurrency was getting “boring” as some of the craziness of the earlier times was fading out and institutional investors had started to pour in, allocating a part of their large portfolios to crypto. [2/n]
To inform investment and risk management decisions, and guide portfolio allocation to crypto assets, it is fundamental to i) identify the set of risk factors driving crypto returns and ii) correctly quantify the prices associated with these sources of risk. [3/n]
The paper shows that sudden and large price moves in bitcoin prices (jumps) explain a large portion in the variation in bitcoin returns [2/n]
Study tail-risk in crypto markets is important for at least two reasons 1/ is tail-risk priced similarly to that in equity markets? 2/ to characterize the SDF of the marginal investor and price alternative cryptocurrencies and tokens and do risk-management [3/n]
The narrative of the rollercoaster day for cryptocurrency markets centers around the fears of stricter regulation in China (which might want to push its future CBDC). I shamlessy take the opportunity to advertise some of my prior work [1/n] #EconTwitter ft.com/content/c4c29b…
In Borri and Shakhnov (FRL 2019) we look at a similar big shock when China de facto ordered the closing of cryptocurrency exchanges. [2/n]
The shock had a huge effect on the global share of trading volume that took place on Chinese cryptocurrency exchanges: in a matter of months it went from 90% to less than 1% (caveat: part of it could have been wash trading) [3/n]
Our paper is motivated by recent work by @HannoLustig et al. (AER 2019) who found that currency carry trade strategies with T-bonds are different from those with T-bills because local currency term premia offset currency premia 2/n
Results in Lustig et al. (AER 2019) are for advanced economies with no/low default risk and imply that the volatility of the permanent component of investors’ SDF must be equalized across countries 3/n
We focus on Italy -- one of the first country struck by #COVIDー19 -- where the lockdown design offers a source of exogenous variation in the intensity of the lockdown at a granular level 2/n
In the second (economic) lockdown (March 22) the Italian government defined a detailed list of essential economic activities. All other activities were either suspended or allowed to operate only remotely 3/n
My paper with K. Shakhnov on Regulation spillovers across cryptocurrency markets is now available on FRL at this link authors.elsevier.com/c/1bjLs5VD4Kcw… (with 50 days free access) #EconTwitter [thread 1/n]
In this paper we look at the unprecedented drop in trading volume on the Chinese cryprocurrency market after a significant regulatory change that de facto banned bitcoin in early 2017 in China [thread 2/n]
We find large spillovers of this regulatory shock on other cryptocurrency markets: 1) we observe a large increase in trading volume for bitcoin vs. Korean won, Japanese yen and U.S. dollars; ... [3/n]