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30
Ambiguity Aversion: Implications for the Uncovered Interest Rate Parity Puzzle
, 2009
"... Empirically, highinterestrate currencies tend to appreciate in the future relative to lowinterestrate currencies instead of depreciating as uncovered interest rate parity (UIP) states. The explanation for the UIP puzzle that I pursue in this paper is that the agents' beliefs are systemati ..."
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Cited by 25 (3 self)
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Empirically, highinterestrate currencies tend to appreciate in the future relative to lowinterestrate currencies instead of depreciating as uncovered interest rate parity (UIP) states. The explanation for the UIP puzzle that I pursue in this paper is that the agents' beliefs are systematically distorted. This perspective receives some support from an extended empirical literature using survey data. I construct a model of exchange rate determination in which ambiguityaverse agents need to solve a filtering problem to form forecasts but face signals about the timevarying hidden state that are of uncertain precision. In the presence of such uncertainty, ambiguityaverse agents take a worstcase evaluation of this precision and respond stronger to bad news than to good news about the payoffs of their investment strategies. Importantly, because of this endogenous systematic underestimation, agents in the next periods will perceive on average positive innovations about the payoffs which will make them reevaluate upwards the profitability of the strategy. As a result, the model's dynamics imply significant expost departures from UIP as equilibrium outcomes. In addition to providing a resolution to the UIP puzzle, the model predicts, consistent with the data, negative skewness and excess kurtosis for currency excess returns and positive average payoffs even for hedged positions.
Internal rationality, imperfect market knowledge and asset prices
, 2010
"... We present a decision theoretic framework in which agents are learning about market behavior and that provides microfoundations for models of adaptive learning. Agents are ‘internally rational’, i.e., maximize discounted expected utility under uncertainty given dynamically consistent subjective beli ..."
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Cited by 23 (3 self)
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We present a decision theoretic framework in which agents are learning about market behavior and that provides microfoundations for models of adaptive learning. Agents are ‘internally rational’, i.e., maximize discounted expected utility under uncertainty given dynamically consistent subjective beliefs about the future, but agents may not be ‘externally rational’, i.e., may not know the true stochastic process for payoff relevant variables beyond their control. This includes future market outcomes and fundamentals. We apply this approach to a simple asset pricing model and show that the equilibrium stock price is then determined by investors ’ expectations of the price and dividend in the next period, rather than by expectations of the discounted sum of dividends. As a result, learning about price behavior affects market outcomes, while learning about the discounted sum of dividends is irrelevant for equilibrium prices. Stock prices equal the discounted sum of dividends only after making very strong assumptions about agents ’ market knowledge.
Learning in macroeconomics
, 2001
"... Expectations play a central role in modern macroeconomic theories. The econometric learning approach models economic agents as forming expectations by estimating and updating forecasting models in real time. The learning approach provides a stability test for rational expectations and a selection ..."
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Cited by 13 (2 self)
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Expectations play a central role in modern macroeconomic theories. The econometric learning approach models economic agents as forming expectations by estimating and updating forecasting models in real time. The learning approach provides a stability test for rational expectations and a selection criterion in models with multiple equilibria. In addition, learning provides new dynamics if older data is discounted, models are misspecified or agents choose between competing models. This paper describes the Estability principle and the stochastic approximation tools used to assess equilibria under learning. Applications of learning to a number of areas are reviewed, including the design of monetary and fiscal policy, business cycles, selffulfilling prophecies, hyperinflation, liquidity traps, and asset prices.
Learning as a Rational Foundation for Macroeconomics and Finance ∗
, 2011
"... Expectations play a central role in modern macroeconomics. The econometric learning approach, in line with the cognitive consistency principle, models agents as forming expectations by estimating and updating subjective forecasting models in real time. This approach provides a stability test for RE ..."
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Cited by 11 (0 self)
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Expectations play a central role in modern macroeconomics. The econometric learning approach, in line with the cognitive consistency principle, models agents as forming expectations by estimating and updating subjective forecasting models in real time. This approach provides a stability test for RE equilibria and a selection criterion in models with multiple equilibria. Further features of learning, such as discounting of older data, use of misspecified models, or heterogeneous choice by agents between competing models, generate novel learning dynamics. Empirical applications are reviewed and the roles of the planning horizon and structural knowledge are discussed. We develop several applications of learning to macroeconomic policy: the scope of Ricardian equivalence, appropriate specification of interestrate rules, implementation of pricelevel targeting to achieve learningstability of the optimal RE equilibrium and whether under learning pricelevel targeting can rule out the deflation trap at the zerolowerbound. Any views expressed are those of the authors and do not necessarily reflect the views
Monetary Policy Design under Imperfect Knowledge: An Open Economy Analysis
"... Abstract. Incorporating adaptive learning into an openeconomy DSGE model, we examine how monetary policy rules should adjust when agents ’ information set deviates from that assumed under the rational expectations paradigm. We find that when agents observe current shocks but don’t know the paramete ..."
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Cited by 4 (0 self)
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Abstract. Incorporating adaptive learning into an openeconomy DSGE model, we examine how monetary policy rules should adjust when agents ’ information set deviates from that assumed under the rational expectations paradigm. We find that when agents observe current shocks but don’t know the parameters governing key macroeconomic dynamics, the resulting distortion is small and the preferred policy under rational expectations works well. However, the welfare cost of imperfect knowledge becomes quite severe when agents also have to learn about the structural shocks to the economy. Monetary policy can play a significant role in mitigating distortions associated with this form of imperfect knowledge. JEL classification: D84; E52; F41
Learning, the forward premium puzzle and market efficiency. Macroeconomic Dynamics, forthcoming
, 2008
"... The Forward Premium Puzzle is widely considered to indicate inefciency in the foreign exchange market. This paper proposes a resolution of the puzzle using recursive least squares learning. Risk neutral agents learn the unknown parameters, underlying the exchange rate generation process, using con ..."
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Cited by 3 (1 self)
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The Forward Premium Puzzle is widely considered to indicate inefciency in the foreign exchange market. This paper proposes a resolution of the puzzle using recursive least squares learning. Risk neutral agents learn the unknown parameters, underlying the exchange rate generation process, using constantgain recursive least squares. Simulations using plausible model parameter values replicate the anomaly along with other observed empirical features of the forward and spot exchange rate data. Estimates of parameter values from data support the model assumptions and justify the use of constantgain learning. The conclusion is that the puzzle is not necessarily a reection of ine ¢ ciency.
Learning generates Long Memory
, 2011
"... We consider a prototypical representativeagent forwardlooking model, and study the low frequency variability of the data when the agent’s beliefs about the model are updated through linear learning algorithms. We find that learning in this context can generate strong persistence. The degree of per ..."
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Cited by 1 (0 self)
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We consider a prototypical representativeagent forwardlooking model, and study the low frequency variability of the data when the agent’s beliefs about the model are updated through linear learning algorithms. We find that learning in this context can generate strong persistence. The degree of persistence depends on the weights agents place on past observations when they update their beliefs, and on the magnitude of the feedback from expectations to the endogenous variable. When the learning algorithm is recursive least squares, long memory arises when the coefficient on expectations is sufficiently large. In algorithms with discounting, long memory provides a very good approximation to the lowfrequency variability of the data. Hence long memory arises endogenously, due to the selfreferential nature of the model, without any persistence in the exogenous shocks. This is distinctly different from the case of rational expectations, where the memory of the endogenous variable is determined exogenously. Finally, this property of learning is used to shed light on some wellknown empirical puzzles.
Modeling exchange rates with incomplete information
 L.Sarno (Eds.), Handbook of Exchange Rates. Wiley Handbook Series
, 2012
"... 1Written for the Handbook of Exchange Rates. We would like to thank an anonymous referee for helpful comments. Recent research has shown that relaxing the assumptions of complete information and common knowledge in exchange rate models can shed light on a wide range of important exchange rate puzzle ..."
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Cited by 1 (0 self)
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1Written for the Handbook of Exchange Rates. We would like to thank an anonymous referee for helpful comments. Recent research has shown that relaxing the assumptions of complete information and common knowledge in exchange rate models can shed light on a wide range of important exchange rate puzzles. In this chapter, we review a number of models we have developed in previous work that relax the strong assumptions on information. We also review some related literature.