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This paper emphasizes the need for understanding the interdependencies between the real and financial sides of the economy in macroeconomic models. While the real side of the economy is generally well explained in macroeconomic models, the financial side and its interaction with the real economy remains poorly understood. This paper makes an attempt to model the interdependencies between the real and financial sides of the economy in Denmark while adopting a stock-flow consistent approach. The model is estimated using Danish data for the period 1995–2016. The model is simulated to create a baseline scenario for the period 2017– 30, against which the effects of two standard shocks (fiscal shocks and interest rate shocks) are analyzed. Overall, our model is able to replicate the stylized facts, as will be discussed. While the model structure is fairly simple due to different constraints, the use of the stockflow approach makes it possible to explain several transmission mechanisms through which real economic behavior can affect the balance sheets, and at the same time capture the feedback effects from the balance sheets to the real economy. Finally, we discuss certain limitations of our model.

The Brazilian ports are out of capacity, imposing several restrictions to the sector expansion. Some investments have been made, but without improvements in the situation. Ports around the world seems to have the same problem. One of the aspect that implies in this situation are the forecast methods used to understand the short and long term of ports demand. They are based in variables of the ports neglecting the economic panoramic view. The ports operations are influenced for a lot of macroeconomic variables as soon as transport services are not a final sector but a middle one. These paper aims to apply Factorial Models in ports demands forecasting and compare the results with traditional time series models. We focus in the Stock and Watson (2002, 2008, 2012) papers that show that factorial models can generate more efficient forecasts than standard time series and structural models, and can be normally used to predict macroeconomic variable.

Korean Abstract: 관측주기가 다른 자료를 이용하여 경제를 분석하면서 고빈도(high frequency) 자료를 저빈도(low frequency) 자료로 전환하는 경우 자료가 지닌 정보를 충분히 활용하지 못하는 문제가 발생한다. 대부분의 국가에서 GDP는 분기 단위로 발표되고 있어, GDP를 이용하여 경제분석을 하는 경우 이러한 문제가 흔히 나타나며, 특히 속보성이 떨어지는 경우 최근 데이터를 활용할 수 없어 경제변수 분석이나 전망에 제약이 발생할 수 있다. 본고에서는 이러한 문제를 해결하기 위해 혼합주기자료를 이용하여 관측되지 않는 고빈도 자료를 포함하는 변수로 구성된 은닉인자 상태공간 벡터오차수정모형을 제안하고 깁스표본추출법을 적용하는 베이지안 방식으로 추정하였다. 동 모형을 이용하면 대부분의 국가에서 관측되지 않는 변수인 월별 GDP의 추정뿐 아니라 월별 GDP에 대한 초단기 예측도 가능하다. 월별 GDP가 작성되는 캐나다를 대상으로 본고의 모형을 이용한 월별 GDP 추정치와 실제치를 비교한 결과, 추정된 캐나다 월별 GDP가 실제 발표치와 매우 근사한 것으로 나타났다. 또한 캐나다의 월별 GDP를 대상으로 표본내 예측 및 사후 예측을 실시한 결과, 본고의 모형이 기존 기법에 비해 우수하거나 동등한 수준의 표본내 예측 성과를 보였으며, 사후 예측의 성과는 훨씬 우수하였다. 한편, 우리나라를 대상으로 하여 월별 GDP 추정 및 사후 예측을 수행한 결과, 본고의 모형이 여타 예측모형에 비해 상대적으로 우수한 성과를 보였다. English Abstract: This paper suggests a Gibbs sampling estimation of an unobserved component cointegrated VAR Model to estimate monthly GDP with mixed frequency data. We estimate a Canadian monthly GDP and compare the estimate and actual value of Canadian monthly GDP. The results show that the discrepancy between the estimate and actual value of Canadian monthly GDP is smaller or equivalent to the estimation results of Chow-Lin (1971), Denton (1971), Fernandez (1981), and Litterman (1983). The suggested model can be used as a forecasting model for monthly, therefore quarterly GDP unlike the class of Chow-Lin. This paper ex post forecasts Korean monthly GDP(therefore quarterly GDP) in 6 month horizon and compares the forecast performance with other quarterly forecast models. Compared to Random walk, AR(1), AR(4), VAR(1), and VAR(4) models, the suggested model showed better forecast performance.

The paper discusses two issues related to “growth”. Both have arguably not been dealt with adequately in the literature. The first concerns growth and accruals. Research practice combines two components of accruals, asset accruals and liability accruals. This simplification causes problems because income-increasing asset accruals correlate positively with growth whereas the opposite holds for income-increasing liability accruals and growth. In light of this reality, the paper re-configures Jones’s model. The second issue addresses forward P/E multiples dependence on future growth in expected eps. It is shown that the textbook Gordon-Williams 1/(r-g) approach makes no sense – even as a crude first cut. An alternative modelling, which is based on very straightforward ideas, sets the stage and following the preliminaries the P/E-multiple is shown to depend on the growth in expected earnings, Y2 vs. Y1. This approach avoids the need for a g-parameter a la Gordon-Williams. The development brings out a similar g-parameter dependency in the residual income model (when P = bv + RI/(r-g)) and the Ohlson-Jeuttner (2005) model; both include a 1/(r-g) type of multiplier.

Discrete-choice models such as the multinomial-logit (MNL) model are increasingly being used to model customer purchase behaviour in hotels, airlines, fashion retail and e-commerce. Their estimation however has proved difficult in practice. One reason for this difficulty is well-known: most firms in retail do not, or unable to, record customers who were interested in purchasing but did not buy the product (``no-purchasers"). Estimating demand even with the simplest discrete-choice model such as the MNL becomes challenging then as we do not know the fraction that have chosen an outside option (not purchased). Indeed, the parameters of the MNL model may not be identifiable with such data. Some previous approaches have proposed using ``market-share" to pin down the parameter associated with the outside option. However, in many industries, market-share data is difficult to obtain, and for some, such as fashion products, has little meaning. In this paper we point out an additional difficulty that arises in practice: Many firms constantly monitor sales and optimize their prices and assortments based on partially observed demand. This leads to an optimization-induced endogeneity as the input used for estimation has been processed by optimization that takes both past data as well as future demand trends in setting controls. As we demonstrate, methods that work well on randomly generated assortments may do badly on optimized assortment data. In this paper we propose a robust method when the firm cannot observe no-purchases, has no market-share information, and the optimization-induced endogeneity exists in the data. The method is a two-step GMM (Generalized Method of Moments) procedure for which we show the estimates are consistent, and we give intuition for its robustness. In Monte-Carlo simulations the performance of our method matches existing methods on randomly generated controls, and is superior in accuracy and robustness when optimization-induced endogeneity is present. On a large real-world data set from the fashion industry --- subject to markdowns as well as stock-outs and unknown management controls --- our method provides very reasonable and robust estimates compared to existing methods.

We develop and implement asymptotic theory to conduct inference on continuous-time asset pricing models using individual equity returns sampled at high frequencies over an increasing time horizon. We study the identification and estimation of risk premia for the continuous and jump components of risks. Our results generalize the Fama-MacBeth two-pass regression approach from the classical discrete-time factor setting to a continuous-time factor model with general dynamics for the factors, idiosyncratic components and factor loadings, while accounting for the fact that the inputs of the second-pass regression are themselves estimated in the first pass. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We measure the role of firm heterogeneity in counterfactual predictions of monopolistic competition trade models without parametric restrictions on the distribution of firm fundamentals. We show that two bilateral elasticity functions are sufficient to nonparametrically compute the counterfactual aggregate impact of trade shocks, and recover changes in economic fundamentals from observed data. These functions are identified from two semiparametric gravity equations governing the impact of bilateral trade costs on the extensive and intensive margins of firm-level exports. Applying our methodology, we estimate elasticity functions that imply an impact of trade costs on trade flows that falls when more firms serve a market because of smaller extensive margin responses. Compared to a baseline where elasticities are constant, firm heterogeneity amplifies both the gains from trade in countries with more exporter firms and the welfare gains of European market integration in 2003-2012. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper studies a dynamic discrete choice model including an unobservable state, which evolves by a hidden action of agents. First, this paper shows new nonparametric identification results for observable and unobservable conditional choice probabilities (CCPs) and an initial type probability in both stationary and nonstationary model. The new identification results show two periods of data (T = 2) is sufficient and invertibility assumption on model dynamics is not necessary when there are proxies for the unobservable state variable which satisfies certain conditions. The result contrasts with Hu and Shum (2012) which required at least four periods of data (T >= 4) and required the invertibility assumption. The subjective measures of attitudes and expectations can be examples of such proxy variables. If proxy variables are missing due to rotating module design, which is the common cause of missing attitude and expectation variables in Panel data, the identification can be obtained under stronger assumptions on the model dynamics. Second, this paper proposes a two-step estimator using proxy variables, which generalizes Arcidiacono and Miller (2011) to endogenize the dynamics of the unobservable state variable. The proposed estimator is root-n consistent and asymptotically normal, conditional on that the first stage EM algorithm is initialized not too far from the global optimum of the objective function. The Monte Carlo experiment shows that the estimator performs well even when proxy variables are missing due to rotating module design.

This paper has analyzed the movement of US stock market during the COVID-19 pandemic. The paper has used time series analysis using Vector Autoregression (VAR) model using data from Jan 23, 2020 to June 19, 2020. The finding suggests that Standard and Poor Index which has been used as reference for capital market has shown negative causality with increase in number of new cases at global level.

This paper employs a GARCH (1,1) model to investigate the impact of COVID-19 cases and related deaths in the US exchange rate volatility. Results show that an increase of the number of cases and the deaths (both in logs) in the US has a positive impact on the USD/EUR, USD/Yuan and USD/LivreSterling. Moreover, this paper uses GARCH (1,1) model to forecast the daily volatility of three exchange rates series with respect to American dollar. These results are useful for anyone needing forecasts of exchange rate futures volatility.

Most empirical papers in economics focus on two aspects of their results: whether the estimates are statistically significantly different from zero and the interpretation of the point estimates. This focus obscures important information about the implications of the results for economically interesting hypotheses about values of the parameters other than zero, and in some cases, about the strength of the evidence against values of zero. This limitation can be overcome by reporting confidence intervals for papers' main estimates and discussing their economic interpretation.

The purpose of this paper is to document and summarize factor returns related to the decline in the stock market due to COVID-19 (“the Coronavirus Crash”). Factor spreads from January 1, 2020 through March 31, 2020 are multiples of historical spreads. For example, the Value-Growth spread observed in March, 2020 is -14.2%, which is the second lowest spread observed from 1951 to present. The paper also expands the research literature by documenting results for factor spreads within twelve large sectors. For example, the Value-Growth spread within 8 of the 12 sectors described herein is -20% or lower. The text of the paper includes highlights of key findings available from Trendline’s proprietary factor database of over 5,000 factor indexes. The Appendix includes returns for over 800 factor indexes across the broad market and key sectors.

Although typically overlooked, many purchase datasets exhibit a high incidence of products with zero sales. We propose a new estimator for the Random-Coefficients Logit demand system for purchase datasets with zero-valued market shares. The identification of the demand parameters is based on a pairwise-differencing approach that constructs moment conditions based on differences in demand between pairs of products. The corresponding estimator corrects non-parametrically for the potential selection of the incidence of zeros on unobserved aspects of demand. The estimator also corrects for the potential endogeneity of marketing variables both in demand and in the selection propensities. Monte Carlo simulations show that our proposed estimator provides reliable small-sample inference both with and without selection-on- unobservables. In an empirical case study, the proposed estimator not only generates different demand estimates than approaches that ignore selection in the incidence of zero shares, it also generates better out-of-sample fit of observed retail contribution margins. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper provides a theoretical and empirical analysis of alternative discount rate concepts for computing LGDs using historical bank workout data. It benchmarks five discount rate concepts for workout recovery cash flows to derive observed Loss rates Given Default (LGDs) in terms of economic robustness and empirical implications: contract rate at origination, loan weighted average cost of capital, return on equity, market return on defaulted debt, and market equilibrium return. The paper develops guiding principles for LGD discount rates and argues that the Weighted Average Cost of Capital (WACC) and market equilibrium return dominate the popular contract rate method. The empirical analysis of data provided by Global Credit Data (GCD) shows that declining risk-free rates are in part offset by increasing market risk premiums. Common empirical discount rates are between the risk-free rate and the return on equity. The variation of empirical LGDs is moderate for the various discount rate approaches. Furthermore, a simple correction technique for resolution bias is developed and increases observed LGDs for all periods, particularly recent periods.

Accounting standards require that financial institutions must measure default risk with respect to the full maturity of a financial instrument. This requires forecasting of future default probabilities. The forecast of future default probabilities concerns two aspects: forecasting macroeconomic scenarios and future average (with respect to the macro-economy) default probabilities. The present paper addresses the modelling of future average default probabilities. Due to the small number of defaults and observations this poses a difficult problem in the corporate area and requires a parsimonious model to deal with the sparse data situation. The degree of difficulty is made greater by the fact that default probabilities change for corporations via different patterns over time, with the pattern being depend on the initial rating grade. For initial investment-grade ratings the risk of a default increases, while for the bottom of the rating scale default probabilities decrease. To model these different patterns the paper proposes to extend the existing discrete-time survival model and to incorporate an additional time- and co-variate-dependent shape parameter into the hazard function. Using data from Standard & Poor's the paper shows that the shape parameter is able to reproduce the different patterns. The proposed model is bench-marked against a model which does not employ a shape parameter and the results show that the shape parameter improves in-sample and out-of-sample prediction.

The traditional futures hedge ratio (hT) is calculated ex post via economically structureless statistical analysis. Its lack of an economic foundation makes it inefficient and elevates its risk of error due to a regime shift. This paper proposes an ex ante, more efficient, carry cost rate (c) based hedge ratio (hc). While the paper shows that hc is biased, it demonstrates that c underlies both hT and hc, such that hT/hc is stationary. Consequently, a prior period’s hT/hc ratio, even if from the distant past, or a different c regime, works well as an ex ante bias adjustment multiplier for the current hc to mitigate its bias. Finally, it shows that the hedge effectiveness for both hc and the bias-adjusted version of hc exceed that for hT, though the excess hedge effectiveness of hc over hT is not always statistically significant.

This Paper describes the theoretical explanation of the Solow model with capital accumulation, using a data-driven empirical analysis. First, we focus on reusing the technological investment to increase the rate of output growth. Further discusses an expelling theory of the aggregate production characteristics, the constant retunes to scale in state-level production functions, the diminishing returns to the factor of production, and steady-state condition of the Solow Model. Secondly, we analyze reasonable empirical evidence to explain the close economy with the Solow growth model and the technological capital accumulation. Finally, we illustrate impotency of the technological contribution in the Solow growth and capital accumulation for the economic growth. According to the results explained, during 1959-99. For the period 1959-73, computer inputs contributed less than 0.1 percentage point to annual U.S. But economic growth after 1995, the price decline for computers had accelerated, reaching nearly 28 percent per year from 1995 to 1998. Therefore, investment in computers had increased, and the rising contribution of computer hardware has increased more than a fivefold, to 0.46 percentage point per year in the late 1990s. Software and communications equipment, two other types of IT assets, contributed an additional 0.30 percentage point per year during 1995-98. Preliminary estimates through 1999 reveal further increases in these contributions for all three high technology assets. Therefore, this paper concludes, technological accumulation is the most significant instrument for the exogenesis Solow growth in USA between 1959-1999.

In standard models, economic activity fluctuates symmetrically around a “natural rate” and stabilization policies can dampen these fluctuations but do not affect the average level of activity. An alternative view—labeled the “plucking model” by Milton Friedman—is that economic fluctuations are drops below the economy’s full potential ceiling. If this view is correct, stabilization policy, by dampening these fluctuations, can raise the average level of activity. We show that the dynamics of the unemployment rate in the US display a striking asymmetry that strongly favors the plucking model: increases in unemployment are followed by decreases of similar amplitude, while the amplitude of the increase is not related to the amplitude of the previous decrease. We develop a microfounded plucking model of the business cycle. The source of asymmetry in our model is downward nominal wage rigidity, which we embed in an explicit search model of the labor market. Our search framework implies that downward nominal wage rigidity is consistent with optimizing behavior and equilibrium. In our plucking model, stabilization policy lowers average unemployment and thereby yields sizable welfare gains. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Understanding of fat tail and its importance was very less known when it comes to the economic field. This paper makes notes from other papers and describes what all are done in the field of fat-tail.

This paper builds a novel database on the effects of macro-prudential policy drawing from 58 empirical studies, comprising over 6,000 results on a wide range of instruments and outcome variables. It encompasses information on statistical significance, standardized magnitudes, and other characteristics of the estimates. Using meta-analysis techniques, the paper estimates average effects to find: i) statistically significant effects on credit, but with considerable heterogeneity across instruments; ii) weaker and more imprecise effects on house prices; iii) quantitatively stronger effects in emerging markets and among studies using micro-level data; and iv) statistically significant evidence of leakages and spillovers. Other findings include relatively stronger impacts for tightening than loosening actions and negative effects on economic activity in the near term.

Different ETS allowance distribution method may vary the effeteness of the ETS on the regulated firm’s performance. This paper uses the Propensity Score Matching Difference-in-Differences method found that the China ETS could increase its regulated firm competitiveness from different aspects. Furthermore, we have tested the impact of the carbon price and its volatility on firm financial performance. Different from the traditional view on the impact of the carbon price, this paper has found that growing the carbon price would benefit regulated firm financial performance, but higher volatility is harmful to the performance.

This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques in finite samples. Moreover, the predictors may be endogenous and “imperfect”. In this setting, we provide a cointegration rank test to determine the predictive model framework as well as the latent persistence of returns. This motivates a rank-augmented Local Spectrum (LCM) procedure, which is consistent and delivers asymptotic Gaussian inference. Simulations illustrate the theoretical arguments. Finally, in an empirical application concerning monthly S&P 500 return prediction, we provide evidence for a fractionally integrated conditional mean component. Moreover, using the rank-augmented LCM procedure, we document significant predictive power for key state variables such as the price-earnings ratio and the default spread. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Studies of the dynamics of bond risk premia that do not account for the corresponding dynamics of bond risk are hard to interpret. We propose a new approach to modeling bond risk and risk premia. For each of the US and China, we reduce the government bond market to its first two principal-component bond-factor portfolios. For each bond-factor portfolio, we estimate the joint dynamics of its volatility and Sharpe ratio as functions of yield curve variables, and of VIX in the US. We have three main findings. (1) There is an important second factor in bond risk premia. (2) Time variation in bond return volatility is as important as time variation in bond Sharpe ratios. (3) Bond risk premia are solely compensation for bond risk, as no-arbitrage theory predicts. Our approach also allows us to document interesting cyclical and secular time-variation in the term structure of bond risk premia in both the US and China. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

In this paper, we study convergence in per capita gross regional products across Russian regions in the period from 1996 to 2017. To this purpose, we estimate growth equations, which are directly derived from a neoclassical growth model, augmented with human capital and migration. To our knowledge, this is the first paper that explicitly applies a neoclassical model to analyze regional convergence process in the Russian case. We also take into account possible spatial effects and do a series of other robustness checks. Our main estimates establish a convergence rate of around 2% per year. While we fail to find any role of human capital for regional economic growth, we find that interregional migration and interdependencies of the growth experience of Russian regions contribute to economic convergence between them.

We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency-arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5-10 millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top 6 firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one-third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate latency arbitrage would reduce the market's cost of liquidity by 17%, and that the total sums at stake are on the order of $5 billion per year in global equity markets alone. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper seeks to further the understanding of US university endowment performance building on recent research by both Hammond and Ennis. The paper finds that a Sharpe style analysis, as employed by Ennis, likely understates the true risk of endowments and therefore overstates alpha. This is because approximately 30% of the average endowments’ assets are invested in alternative assets with many of these being non-traded. This allocation to alternative assets serves to significantly reduce measured risk in comparison to true risk. The paper suggests using NACUBO’s reported asset classes and for those assets that are not easily mapped to return factors, to reference published research that provides guidance for estimating the true factor loadings of these non-traditional assets. Employing this methodology, the estimated annual alpha for endowments between 2009 and 2019 is -2.15% with a sensitivity range of -1.26% to -3.15% (all of which are significant at the 99% level) reflecting the uncertainty associated with the true portfolio style.

Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables. However, they fundamentally rely on the well-known monotonicity (threshold-crossing) condition on treatment choice behavior. Recent research has shown that this condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. Based on these findings, we develop a new marginal treatment effect framework under a weaker, partial monotonicity condition. The partial monotonicity condition is implied by standard choice theory and allows for rich heterogeneity even in the presence of multiple instruments. The new framework can be viewed as having multiple different choice models for the same observed treatment variable, all of which must be consistent with the data and with each other. Using this framework, we develop a methodology for partial identification of clearly stated, policy-relevant target parameters while allowing for a wide variety of nonparametric shape restrictions and parametric functional form assumptions. We show how the methodology can be used to combine multiple instruments together to yield more informative empirical conclusions than one would obtain by using each instrument separately. The methodology provides a blueprint for extracting and aggregating information about treatment effects from multiple controlled or natural experiments while still allowing for rich heterogeneity in both treatment effects and choice behavior. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

In a seminal paper, Helpman et al. (2008) demonstrate that accounting for self-selection and firm heterogeneity is crucial for obtaining unbiased estimates in the gravity equation. In this paper, we show how this insight helps solve the trade elasticity puzzle that, hitherto, trade elasticity estimates in the gravity equations have implied very small welfare gains from trade. To apply their approach to product-level data, we propose new exclusion restrictions based on the export learning literature. Our new estimates imply much larger welfare gains from trade than the previous literature suggested.

Analogous to Stambaugh (1999) , this paper derives the small sample bias of estimators in J-horizon predictive regressions, providing a closed-form solution in terms of the sample size, horizon and persistence of the predictive variable. For large J, the bias is linear in J T with a slope that depends on the predictive variable's persistence. The paper offers a number of other useful results, including (i) important extensions to the original Stambaugh (1999) setting, (ii) closed-form bias formulas for popular alternative long-horizon estimators, (iii) out-of-sample analysis with and without bias adjustments, along with new interpretations of out-of-sample statistics, and (iv) a detailed investigation of the bias of the overlapping estimator's standard error based on the methods of Hansen and Hodrick (1980) and Newey and West (1987). The small sample bias adjustments substantially reduce the magnitude of long-horizon estimates of predictability.

In this dissertation the main focus area was to use one factor interest rate models in India to obtain the descriptive nature and risk profile of money markets in India. For the period of 2012-2020 we found that CIR (1985) model fits and describes the interest rate path for India much better than Vasicek model. Both of these models were calibrated to make each of their parameters pseudo time varying and maximum likelihood estimation (MLE) was used to find optimal model parameters. We used daily weighted average of call money market rates as input data for calibration. Under the assumptions of constant relative risk aversion (CRRA) and decreasing absolute risk aversion (DARA) of CIR model, we used the closed form solutions to obtain bond prices and yields on 3 month T-bill. Average 10 year yield curve for the period 2012-20 was also forecasted and it was observed that Indian yield curve has a hump shape, with higher yields on longer dated bonds. We find that there exists higher term premium for longer dated bonds, for example, term premium on 10 year G-Sec has increased at a faster rate, relative to shorter maturity bonds, with the trend getting stronger since 2016 with an exception in year 2019. For risk profiling, the paper uses Expected-shortfall (ES) and Value-at-Risk (VaR) on 3 month bonds. It was found that expected shortfall normalized for yield stood at 25% on average for the period 2012-20. This means that traders in the money market need to constantly look out for price risk, to hedge for this, we propose that the trader uses call options on bonds. We find that fair prices of call options for CIR distribution function under Blacks-Scholes model will be an expensive hedging strategy. But that said, the Greeks for these call options (Rho, Delta and Gamma) show that the call option will not be sensitive to interest rate and bond price changes and hence will be a stable hedging strategy. The paper concludes that this is because of very high speed of mean reversion and low volatility in interest rate paths. This result can be associated with credibility, transparency and clear policy decisions made by the Indian central bank and newly formed Monetary Policy Committee (MPC). It was also seen that MPC has been successful to lower volatility by 9 times since its inception while maintaining the same high levels of speed of reversion in interest rates.

Generous consideration has been pursued to the empirical testing of multi factor assets pricing models. However, literature provides mixed kind of evidences in the support of multi factor assets pricing model. This study reviews 20 research articles based on multi factor assets pricing model and examines 25 research papers based on the empirically testing of multi factor assets pricing model published during 2001 and 2018 to study the multi factor assets pricing model in the Indian context as well as foreign context. CAPM is a popular normative model used by researchers to explain the relationship between risk and expected return of a risky asset which was developed by Sharpe (1964) and Lintner (1965). This model takes only one risk factor which is the excess market portfolio return (Market premium). Because of poor performance of CAPM in explaining realized returns, the Fama and French three factor asset pricing model (1993) was developed. Fama and French (1993) documented the size effect and the value effect that were not included in the CAPM, generally known as CAPM anomalies. Mark M. Carhart (1997) developed the Carhart four factor model. It is an extension of the FF three factor model with one another factor i.e. momentum factor effect for asset pricing of stocks. In view of the limitations of the earlier three-factor model, Fama and French five-factor asset pricing model (2014) was developed. Fama and French (2014) came with profitability pattern and investment pattern in average stock return along with the market premium, size premium and value premium. This paper may be an expedient source of information to the academics, financial analyst and researchers to understand the asset pricing model.

The study of the financial repercussions of low-carbon policy has focused mainly on stocks, leaving bonds out of the picture. The objective of this paper is to assess the impact of low-carbon policy upon European bond returns. This is done by extending the Fama and French (1993) two factor model for bonds with an EU-ETS compliance factor. The paper provides a highly statistically significant proxy for the risk factor in bond returns related to EU-ETS compliance, the GMC factor, and shows the presence of a statistically significant green premium in the European bond market. Furthermore, evidence is found that the addition of an environmental factor improves the performance of the original model in Europe. Lastly, the carbon stress test put forward is able to indicate the effects of a plausible but more severe average EU-ETS carbon price on bond returns.

We extend the original idea of the residual income model and decompose the firm’s cost of equity capital into its two elementary components: The discount rate of normal operating earnings (kNE) and discount rate of residual operating earnings (kRE). The first part of the paper identifies the analytical relation between the firm’s cost of capital, kNE and kRE for the un-levered firm in steady state conditions. The second part of the paper empirically tests this relation by exploiting the cross section of firm’s operating profitability (RNOA) and enterprise price-to-book ratio (PNOA/NOA). Our findings support the idea that residual earnings are the less risky component of income.

This paper uses accounting-based reverse engineering of market expectations to identify potentially mispriced stocks. Building upon the “errors-in-expectations” hypothesis, we develop a theoretically funded yet practical tool for stock screening in this paper. We use the Ohlson (1995) model to apply and extend the framework from the first paper by connecting total stock returns to accounting-based fundamentals and (changes in) expected residual income levels, both in the short-term and long-term future. In a similar manner to Piotroski & So (2012), we construct a scoring index – VScore. VScore includes both fundamental data and short-term market expectations that stem from the theoretical framework we provide beforehand, where short-term expectations perform a verifying function for historical fundamentals for the determination of quality. We document that the book-to-price (B/P) effect is concentrated among firms for which long-term speculation is simultaneously incongruent to the underlying fundamentals and short-term expectation (stocks that combine cheapness with quality), indicating that those stocks are interesting subjects for a more extensive fundamental analysis.

This paper addresses the problem of estimating multiple quantiles from a time series. A flexible class of dynamic multiple quantile (DMQ) models is specified, ensuring that estimated quantiles do not cross and that extreme quantiles are estimated by exploiting information coming from all the regions of the underlying distribution. DMQ models encompass the baseline model by Catania and Luati (2019), based on two main ideas, i.e. quantile spacings and score-type updates, and provide extension along several directions. First, a specification which includes a heterogeneous reaction of the left and right tails to past observations is included. Secondly, the case where all quantiles react differently to past information is considered. Finally, cross-tail effects are modeled, so that information from the right tail influences the left tail, and vice versa. The last specification allows the model to effectively capture the leverage effect in financial returns. Estimation is carried out by means of two stage quasi maximum likelihood estimators (2SQMLE). In the first stage the quantile intercepts are estimated by employing a targeting scheme. The use of empirical targeting versus parametric targeting is discussed. Consistency and asymptotic normality of 2SQMLE are derived. As closed-form expressions for h-step-ahead predictive quantiles are available, empirical results are assessed also based on comparisons in terms of the news impact curve, the quantile response function and the quantile decay, defined in this paper as the decay over time of quantile levels after an extreme shock.

This paper proposes a new reduced-form model for the pricing of VIX derivatives that includes an independent stochastic jump intensity factor and cojumps in the level and variance of VIX, while allowing the mean of VIX variance to be time varying. I fit the model to daily prices of futures and European options from April 2007 through December 2017. The empirical results indicate that the model significantly outperforms all other nested models and improves on benchmark by 21.6% in sample and 31.2% out of sample. The model more accurately portrays the tail behavior of VIX risk-neutral distribution for both short and long maturities, as it better captures the time-varying skew found to be largely independent of the level of the VIX smile. This paper was accepted by Kay Giesecke, finance.

This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the dominant driver of the real price of oil. The focus of this paper is not only on correcting some misunderstandings in the recent literature, but on the substantive and methodological insights generated by this exchange, which are of broader interest to applied researchers.

This paper contains details for implementing credit spread variance pricing methodologies based on credit default swap (CDS) options. A model independent formula for expected volatility is available, based on the prices of vanilla CDS options (VCOs). However, VCOs are currently not traded, and their prices must be inferred from those of actively traded CDS options with exotic payoffs (ECOs). Plugging ECO prices directly into the index formula is not theoretically justified, and the economic significance in the context of variance pricing of the difference in options contract specifications must be examined empirically. The paper develops methodology for converting observed ECO prices into hypothetical VCO prices for the purpose of index calculation, and assesses the economic impact of using ECOs and VCOs on index values under realistic market conditions.

A wealth of evidence shows that individuals are biased and firms can often exploit consumers' behavioral biases in their contract designs. In this paper, we study how vulnerable biased individuals are to their own behavioral biases in market equilibrium, and focus on the role of risk aversion and intertemporal elasticity of substitution (IES). We measure consumer vulnerability by the percentage loss in a consumer's equilibrium certainty equivalent from a market with non-biased consumers to that with biased ones. We examine several important behavioral biases that have been extensively studied in the literature, including the impact of biased beliefs (either over- or under-confidence) in an insurance market, the impact of present bias and naivete about present bias in a dynamic model of credit contract design, the impact of projection bias about habit formation, and the impact of expectation-based loss aversion on an investor's portfolio choice. We show that consumer vulnerability to all four commonly studied behavioral biases has a non-monotonic relationship with risk aversion or IES. This is in striking contrast to the deviations in the equilibrium allocations from the rational benchmark, which are often monotonic to the risk aversion or IES. We also consider a setting of biased agents with Epstein-Zin preferences to isolate the effect of risk aversion from that of IES. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper develops a method for forecasting a nonstationary time series, such as GDP, using a set of high-dimensional panel data as predictors. To this end, we use what is known as a factor augmented regression [FAR] model that contains a small number of estimated factors as predictors; the factors are estimated using time series data on a large number of potential predictors. The validity of this method for forecasting has been established when all the variables are stationary and also when they are all nonstationary, but not when they consist of a mixture of stationary and nonstationary ones. This paper fills this gap. More specifically, we develop a method for constructing an asymptotically valid prediction interval using the FAR model when the predictors include a mixture of stationary and nonstationary factors; we refer to this as mixture-FAR model. This topic is important because typically time series data on a large number of economic variables is likely to contain a mixture of stationary and nonstationary variables. In a simulation study, we observed that the mixture-FAR performed better than its competitor that requires all the variables to be nonstationary. As an empirical illustration, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production [IP], using the quarterly panel data on US macroeconomic variables, known as FRED-QD. We observed that the mixture-FAR model proposed in this paper performed better than its aforementioned competitors.

This paper presents a real valuation performed by a well-known investment bank, with two common errors and with two very different values for the equity of a firm: a) €6,9 million calculating the Present Value of expected free cash flows (FCF) discounted with the WACC rate and then, subtracting the value of debt; b) €4,2 million calculating the Present Value of expected equity cash flows (ECF) discounted with the Ke rate (required return to equity). We identify the two main errors of the valuation of the investment bank and calculate the “right solution”: €5,9 million. The paper also contains 210 answers and 56 comments from readers to the questions of the previous paper (The Most Common Error in Valuations using WACC https://ssrn.com/abstract=3512739 that did not contain the “right solution”.)

Multivariate dynamic time series models are widely encountered in practical studies, e.g., modelling policy transmission mechanism and measuring connectedness between economic agents. To better capture the dynamics, this paper proposes a wide class of multivariate dynamic models with time — varying coefficients, which have a general time — varying vector moving average (VMA) representation, and nest, for instance, time — varying vector autoregression (VAR), time — varying vector autoregression moving--average (VARMA), and so forth as special cases. The paper then develops a unified estimation method for the unknown quantities before an asymptotic theory for the proposed estimators is established. In the empirical study, we investigate the transmission mechanism of monetary policy using U.S. data, and uncover a fall in the volatilities of exogenous shocks. In addition, we find that (i) monetary policy shocks have less influence on inflation before and during the so — called Great Moderation, (ii) inflation is more anchored recently, and (iii) the long — run level of inflation is below, but quite close to the Federal Reserve's target of two percent after the beginning of the Great Moderation period.

Using tax data, this paper exploits a discontinuous increase in retirement contribution limits based on exact date of birth. This paper finds clear evidence that constrained individuals increase their retirement saving when so eligible, but finds no evidence suggesting that non-retirement saving falls. In the baseline specifications, one can rule out crowd-out greater than approximately 0.38 to 0.57 at the 95% confidence level depending on the measure used. This suggests that the additional contributions induced by catch-up eligibility represent an increase in total private saving.

Financial contagion is explored in a large and growing number of network-based models with a high variety of assumptions on network geometry, bank balance sheets, loan settlement, etc. In this paper, we present “ContagionGraphs”, a tool for researches to explore the seemingly endless possibilities in network-based models of financial contagion. We showcase its abilities by reproducing and extending on hallmark papers in the field such as Allen and Gale (2000), Nier et al. (2007) and Acemoglu et al. (2015). On top of the predominant analysis of average default ratios in average node degrees, we show that the net credit position of the shocked bank is a strong predictor of network stability and that equity and liquidity are substitutes for interbank network stability.

Estimates of loss aversion in housing sales prices may be biased because expected losses correlate with housing and borrower unobservables. We provide new evidence of loss aversion in sales price by differencing loss aversion estimates between sellers who exhibit focal point bias in their initial mortgage amount and those who do not. Although focal point bias and loss aversion are associated with different families of behaviors, recent evidence suggests links between these biases. Revisiting experimental data, subjects with high levels of loss aversion were more likely to use round numbers. Using housing data, estimates of loss aversion are 10 percentage points higher for sellers with round number mortgage amounts as a share of expected loss. Differences in expected loss are balanced over both housing and mortgage attributes, are stable as additional of controls are added, and are robust to using a discontinuity style regression centered on mortgage amounts of round numbers. On the other hand, traditional estimates of loss aversion fall by as much as 73 percent as additional controls are added. This study provides unique evidence that loss aversion and focal point bias are found together, and presents new, more robust evidence that loss aversion influences housing sales prices. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Equity returns are typically higher correlated during market downturns than during bullish times. This paper develops a novel approach how investor expectations for such correlation asymmetries can be quantified from forward-looking data. Based on option implied volatilities, it is found that the correlation asymmetry is significant, rejecting the use of the classic mono-correlation assumption. Further, the spread between expected down and up correlations is time-varying and positively dependent on the current market mood: stock diversification is more difficult when it is needed the most. Thus, the three main advantages of the proposed model are (ⅰ) the distinction between up- and down-correlations, (ⅱ) it actually captures investor expectations as traded in current market prices and (ⅲ) the immediate response to the current market outlook. Practical relevance of this paper is highlighted by the computation of expected up-/down CAPM betas.

We examine how policymakers should react to a pandemic when there is significant uncertainty regarding key parameters relating to the disease. In particular, this paper explores how optimal mitigation policies change when incorporating uncertainty regarding the Case Fatality Rate (CFR) and the Basic Reproduction Rate (R0) into a macroeconomic SIR model in a robust control framework. This paper finds that optimal policy under parameter uncertainty generates an asymmetric optimal mitigation response across different scenarios: when the disease’s severity is initially underestimated the planner increases mitigation to nearly approximate the optimal response based on the true model, and when the disease’s severity is initially overestimated the planner maintains lower mitigation as if there is no uncertainty in order to limit excess economic costs.

In this paper we address how returns on financial assets vary across the population. Exploiting rich administrative data, we are able to neatly describe the heterogeneity across all parts of the distribution of wealth. We find compelling evidence that the rich benefit from higher returns. Likely, this is due to two different effects that have been called scale dependence and type dependence. The former is due to an observed positive correlation between net worth and returns. The latter describes a high persistence of returns for each individual, most possibly due to better information and market access advantages. We find evidence that both channels play an important role. Further, with respect to inequality, our results suggest that there is a wide heterogeneity across different socio-demographic dimensions in Switzerland which has been growing over time. Conceptually, this paper contributes by modelling the full distribution of returns. This allows to address the scale effect of net worth on the returns throughout the distribution of the latter. We find that net worth crucially determines the top of the return’s distribution highlighting another channel through which wealth inequality reinforces itself.

This paper uses a macroeconomic agent-based model building on Delli Gatti et al. (2011) to investigate the influence of agents’ expectations and consumption choices on government expenditure multipliers. Following a thorough investigation of the size of the multiplier in the pre-existing baseline model, a modification is introduced, allowing agents to engage in inter-temporal optimization of consumption subject to a budget constraint which is based on estimates of future income. Compared to the baseline, the fiscal multiplier is strongly affected by this alternative consumption behavior, becoming significantly smaller. In a further step, agents’ beliefs about the effects of government expenditure shocks are explicitly introduced. In the case of exogenously imposed beliefs coupled either with adaptation of individual beliefs or switching behavior between different types of beliefs, it is shown that both optimistic and pessimistic expectations can be temporarily self-fulfilling and either increase or decrease the value of the multiplier. Both forms of belief dynamics also allow for the incorporation of announcement effects of fiscal policy. In a final experiment, agents are allowed to engage in least-squares learning in order to gain an estimate of the effect of government expenditure shocks on future income. It is shown that under least squares learning, beliefs are ‘rational’ insofar as they lead to broadly correct predictions on average. The paper hence contributes to addressing aspects of the Lucas critique as applied to macro-ABMs, since agents react systematically (and reasonably) to announcements of changes in fiscal policy.

Many studies use matched employer-employee data to estimate a statistical model of earnings determination where log-earnings are expressed as the sum of worker effects, firm effects, covariates, and idiosyncratic error terms. Estimates based on this model have produced two influential yet controversial conclusions. First, firm effects typically explain around 20% of the variance of log-earnings, pointing to the importance of firm-specific wage-setting for earnings inequality. Second, the correlation between firm and worker effects is often small and sometimes negative, indicating little if any sorting of high-wage workers to high-paying firms. The objective of this paper is to assess the sensitivity of these conclusions to the biases that arise because of limited mobility of workers across firms. We use employer-employee data from the US and several European countries while taking advantage of both fixed-effects and random-effects methods for bias-correction. We find that limited mobility bias is severe and that bias-correction is important. Once one corrects for limited mobility bias, firm effects dispersion matters less for earnings inequality and worker sorting becomes always positive and typically strong. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.