Combining State-Dependent Forecasts of Equity Risk Premium

Size: px
Start display at page:

Download "Combining State-Dependent Forecasts of Equity Risk Premium"

Transcription

1 Combining State-Dependent Forecasts of Equity Risk Premium Daniel de Almeida, Ana-Maria Fuertes and Luiz Koodi Hotta Universidad Carlos III de Madrid September 15, 216 Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

2 Introduction Motivation Equity premium forecasts are inputs for asset pricing, asset allocation and risk management. In-sample evidence that macroeconomic variables explain future risk premia. Some authors remain skeptical regarding predictability: restricted to in-sample evaluations and inconsistent out-of-sample performance. Welch and Goyal (Review of Financial Studies, 28) conclude that several macroeconomic predictors fail to beat the simple historical average (HA) benchmark. Researchers should explore alternative variables and/or more sophisticated models. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

3 Introduction Figure 1: Cumulative square forecast error for the historical average minus cumulative square forecast error for individual macroeconomic variables. 3 D/Y 3 E/P 3 D/E RVOL 3 DFR 3 INFL Dividend yield, earnings-price ratio, dividend payout ratio, equity premium volatility, default return spread and inflation. Inconsistent out-of-sample performance. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

4 Background literature Introduction Rapach et al. (Review of Financial Studies, 21) - combining forecasts - provide out-sample evidence of predictability. Large body of literature indicating that asset returns follow a process with more than one regime, e.g., Guidolin and Timmermann (Journal of Economic Dynamics and Control, 27), Henkel et al. (Journal of Financial Economics, 211), Zhu and Zhu (Journal of Banking & Finance, 213): Markov-Switching (MS) forecasting strategies. Neely et al. (Management Science, 214) show that technical indicators can predict the equity risk premium and that they are not encompassed by macroeconomic variables by using principal component (PC) analysis. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

5 Introduction Objective To improve existing equity premium forecasts. To propose parsimonious state-dependent (regime-switching with observable state) predictive regressions. To propose new combining approaches: alternative to equal-weight (EW) combination. To compare our forecast method with existing approaches. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

6 Introduction Contribution 1 Combination of parsimonious regime-switching models to forecast equity premia out-of sample. 2 Technical variables as proxy of current state of economy. 3 Sparse EW (SPAR) combining method: mean average of forecasts from models whose slope parameters are jointly significant (it excludes of the combination models whose slope parameters are not significant according to a statistic test). * Consistent statistical and economic gains of our methods in relation to univariate regressions and existing combined forecasts. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

7 Forecasting methodology Traditional predictive variables Equity premia can be explained by macroeconomic variables r t+1 = α i + β i x i,t + ε t+1, t = 1, 2,, T, (1) where r t+1 is the continuously compounded return (including dividends) in excess of the risk-free interest rate and ε t+1 is a zero-mean error. x i is a macroeconomic variable: dividend yield (D/Y), earnings-price ratio (E/P), dividend payout ratio (D/E), equity premium volatility (RVOL), book-to-market ratio (B/M), net equity expansion (NTIS), treasury bill (TBill), long-term rate of returns (LTR), term spread (TMS), default yield spread (DFY), default return spread (DFR), inflation (INFL). 1-month-ahead equity risk premium forecast is ˆr i,t+1 = α i + β i x it. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

8 Forecasting methodology Technical predictive variables Equity risk premium forecasts can also be predicted by technical variables r t+1 = α i + β i TECH i,t + ε t+1, (2) where TECH i,t is the i-th technical indicator at time t. We consider the 14 TECH of Neely et al. (Management Science, 214): takes values (positive trends) or 1 (negative trends). 1 6 combinations of moving average rule with lengths a and b, MA(a,b): a = 1, 2, 3 and b = 9, The moment rule with level l, denoted by MOM(l), l = 9, combinations of the on-balance volume, VOL(a,b), : a = 1, 2, 3 and b = 9, 12. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

9 Forecasting methodology State-dependent prediction Our forecasting strategy consists on modeling the equity premium by a regime-switching regressive model with observable state r t+1 = α i + β 1i x i,t + β 2i S t x i,t + ε t+1, (3) where S t is a dummy state-variable: takes values or 1. Our approach: information from technical indicators as candidates for S t. Advantages: parsimonious, strongly positive correlated with the NBER-cycle, can identify price trends, easily interpretable. We report two alternatives for S t : the moving average MA(2,12) and the agreement variable A 1 such that A 1 = 1 if 14 i=1 TECH i,t 1, and A 1 = if 14 i=1 TECH i,t < 1. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

10 Forecasting methodology Comparison to alternative methods PC analysis of Neely et al. (Management Science, 214). Combination of individual MS models of the form r t+1 = α i,st + β i,st x i,t + ε i,st, (4) where s t is the regime at time t, following a Markov chain with transition probabilities between regimes at times t and t + 1 given by ( ) p11 1 p Π = 11, (5) 1 p 22 p 22 where p ij are transition probabilities from regime j to i, i, j = 1, 2. * We consider MS model in (4) with switching in the intercept, slope and/or variance of errors (7 cases in total). * The MS models are estimate by the maximum likelihood estimator with the unobserved transition matrix Π being estimated by the Hamilton s Filter. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

11 Forecasting methodology Figure 2: Recession months for the MA(2,12) over the full sample: Dec/195 to Dec/ MA(2,12) means recession and means expansion. Shaded areas indicate NBER-dated recession months. More than 8% of agreement the NBER business cycle and less then 7.5% of transitions. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

12 Combining forecasts Forecasting methodology Combined forecast is a weighted average of N individual forecasts N ˆr t+1 C = ω i,tˆr i,t+1, (6) where ˆr i,t+1 is the i-th individual forecast of r t+1, with the corresponding loading or weight ω i,t. EW combining forecasts is the most popular: ω i,t = 1/N. i=1 Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

13 Combining forecasts Forecasting methodology Elliot et al. (Journal of Econometrics, 213) combine forecasts from all linear regression models with a fixed number of predictors, k. For k = 2, e.g., total of ( N 2) models with two variables r t+1 = α i + β i x i,t + β j x j,t + ε t+1, (7) such that i j. Combining one-step ahead forecast: average of all the ( ) N 2 forecasts. Here we consider k = 1, 2, 3. For all k, we compare combining forecasts of one-state and state-dependent models. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

14 Forecasting methodology Sparse combining forecasts Sparse EW combination (SPAR): mean average of forecasts from models whose slope parameters are jointly significant. Hard thresholding: statistical test to determine if the i-th model is significant without regard for the other predictors being considered (Bai and Ng, Journal of Econometrics, 28). Kostakis et al. (Review of Financial Studies, 215) test: robust to regressors degree of persistence and possible to multiple regression models (k > 1). The SPAR forecast is i E ˆr i,t+1, if E, ˆr t+1 SPAR M = T t=1 r t, if E =, T where E = {i; predictive variable(s) (is)are (jointly) significant at 1% level; i = 1,, ( N k) } and M is the cardinality of E. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28 (8)

15 Out-of-sample forecast evaluation Statistical measures of forecast accuracy The out-of-sample R 2 OOS is R 2 OOS = 1 H h=1 (ˆr T+h r T+h ) 2 H h=1 ( r T+h r T+h ) 2, (9) where h = 1,, H are out-of-sample months, ˆr T+h is 1-ahead forecast r T+h = T+h 1 t=1 r t T+h 1 is the HA forecast. R 2 OOS > ˆr T+h is better than HA. R 2 OOS ˆr T+h is not better than HA. Separately for expansionary (EXP) and recessionary (REC) months: NBER dating. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

16 Out-of-sample forecast evaluation Statistical measures MSE-adjusted statistic of the Clark and West (Journal of Econometrics, 27). * Extension of the Diebold and Mariano (Journal of Business & Economic Statistics, 1995) and West (Econometrica, 1996) tests. * Allows to compare forecast of nested models. Variation in cumulative square forecast error (CSE) given by CSE t = t ( r T+h r T+h ) 2 (ˆr T+h r T+h ) 2 (1) h=1 * Curves are positively sloped ˆr T+h outperforms the r T+h. * Negatively sloped curve r T+h is better. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

17 Out-of-sample forecast evaluation Asset allocation exercise Investors who at time t allocate ω t % to stocks and 1-ω t % to risk-free bills. Total wealth at month t + 1 is W t+1 = [(1 ω t )exp(r f t+1 ) + ω texp(r f t+1 + r t+1)]w t, (11) where r t and the risk-free interest rate, r f t+1, are continuously compounded. Portfolio weights for the period t are the solution of ω t = arg max ω t E t [U(W t+1 )], (12) where U(W t+1 ) is the utility function, mean-variance (MV) or constant relative risk aversion (CRRA), and ω t 1.5 to exclude short sales and leverage above 5%. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

18 Out-of-sample forecast evaluation CER gain Certainty equivalent return (CER): the risk-free rate of return that an investor is willing to accept instead of adopting the given risky portfolio. CER gain ( ): CER of a investor who uses a forecasting model minus CER of a investor that assumes no predictability. Annualized by multiplying it by 12. Interpretation: annualized fee that an investor would be willing to pay to have access to the forecasting model. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

19 Empirical analysis Description of the data set Equity risk premium: log return of S&P 5 (including dividends) minus log return of risk-free bill. Predictive variables: 12 macroeconomic and 14 technical variables. Period: from December 195 to December 214. We generated out-of-sample forecasts by expanding windows. First in-sample period: from December 195 to December H = 588 one-step ahead predictions. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

20 Empirical analysis Figure 1: Cumulative square forecast error for the historical average minus cumulative square forecast error for individual macroeconomic variables. 3 D/Y 3 E/P 3 D/E RVOL 3 DFR 3 INFL Dividend yield, earnings-price ratio, dividend payout ratio, equity premium volatility, default return spread and inflation. Inconsistent out-of-sample performance. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

21 Empirical analysis Figure 3: Cumulative square forecast error for HA minus cumulative square forecast error for 1-regime model combination. EW Combination of single state models SPAR Combination of single state models PC combination of all variables PC ALL k=1 2 k=1 2 3 k=2 k=3 3 k=2 k= One-state combinations: predictability deteriorates after the second half of 199 s. PC models do not deliver consistent forecasts. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

22 Empirical analysis Figure 4: Cumulative square forecast error for HA minus cumulative square forecast error for 2-regime model combination. EW Combination of two state models MA(2,12) as state SPAR Combination of two state models MA(2,12) as state k=1 k=2 k=3 2 3 k=1 k=2 k= Predominantly upward slopes. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

23 Empirical analysis Statistical results of combining forecasts Equal weight combinations Sparse combinations Predictor R 2 OOS (%) R2 OOS (%) ALL EXP REC ALL EXP REC Panel A: One-state predictive variables k = k = k = Panel B: Two-state predictive variables - MA(2,12) k = k = k = Panel C: Two-state predictive variables - A 1 k = k = k = Forecasts of combining regressions with 2 or 3 predictors are better than combinations of individual macroeconomic variables. Forecasts of combining 2-states models are better than nested combining forecasts based on 1-state model. The predictability is substantially larger for recessions vis-à-vis expansions. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

24 Empirical analysis Figure 5: Scatterplot of forecast variances and squared forecast biases in the out-of-sample period B/M 19.8 D/E 19.7 E/P INFL DFR DFY TBL NTIS Forecast variance D/Y MOM(12) VOL(2,12) MA(2,12) LTR RVOL TMS 19.4 EW(1 state) SPAR(1 state) 19.3 EW(2 states) SPAR(2 state) Squared forecast bias Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

25 Empirical analysis Economic evaluation (asset allocation, γ = 5) of combining forecasts Equal weight SPAR MV CRRA MV CRRA Predictor (%) (%) (%) (%) Panel A: One-state predictive variables k = k = k = Panel B: Two-state predictive variables - MA(2,12) k = k = k = Panel C: Two-state predictive variables - A 1 k = k = k = Combination of forecasts from 1-regime models does not beat the MA(2,12). The 2-regime model combinations outperform (larger CER gains) the 1-regime model combinations. Combination of 2-regime models using the SPAR approach further improves the forecast accuracy. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

26 Empirical analysis Comparison with another methods The R 2 OOS are larger for combining forecasts from regime-switching models with observable states compared to PC forecasts and combining MS models. The asset allocation exercise confirm the better performance of combining forecasts from parsimonious regime-switching models in relation to PC models and all combining forecasts from MS models (mean-variance CRRA or CRRA gains). Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

27 Empirical analysis Robust analysis Same conclusions for: * Risk aversions γ = 3, 1; * OOS period starting on January 1976 (after oil crisis) or on Jan/2. OOS on January 1976 or on Jan/2: * R 2 OOS of Rapach et al. (Review of Financial Studies, 21) model and its extension of Elliott et al. (Journal of Econometrics, 213) are not significantly 1% level, whereas combining two-state models are in all the cases; * The CER gains when considering combining two-state models rather than combining one-state models are, in average, 25 and 65 basis points, respectively. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

28 Concluding remarks Main findings The traditional combination of forecasts from one-state model outperforms statistically and economically the HA benchmark. Combination of forecasts from the regime-switching models with observable states have statistical out-of sample gains in relation to other competitors. Results confirmed by asset allocation exercise with two types of investor s preferences (economic gains). Asset allocation exercise suggests notably better performance of SPAR strategy compared to the EW combined forecasts. Almeida, Fuertes and Hotta (UC3M) Predictability of equity premium September 15, / 28

Online Appendix to Bond Return Predictability: Economic Value and Links to the Macroeconomy. Pairwise Tests of Equality of Forecasting Performance

Online Appendix to Bond Return Predictability: Economic Value and Links to the Macroeconomy. Pairwise Tests of Equality of Forecasting Performance Online Appendix to Bond Return Predictability: Economic Value and Links to the Macroeconomy This online appendix is divided into four sections. In section A we perform pairwise tests aiming at disentangling

More information

Equity premium prediction: Are economic and technical indicators instable?

Equity premium prediction: Are economic and technical indicators instable? Equity premium prediction: Are economic and technical indicators instable? by Fabian Bätje and Lukas Menkhoff Fabian Bätje, Department of Economics, Leibniz University Hannover, Königsworther Platz 1,

More information

Optimal Portfolio Choice under Decision-Based Model Combinations

Optimal Portfolio Choice under Decision-Based Model Combinations Optimal Portfolio Choice under Decision-Based Model Combinations Davide Pettenuzzo Brandeis University Francesco Ravazzolo Norges Bank BI Norwegian Business School November 13, 2014 Pettenuzzo Ravazzolo

More information

September 12, 2006, version 1. 1 Data

September 12, 2006, version 1. 1 Data September 12, 2006, version 1 1 Data The dependent variable is always the equity premium, i.e., the total rate of return on the stock market minus the prevailing short-term interest rate. Stock Prices:

More information

Lecture 2: Forecasting stock returns

Lecture 2: Forecasting stock returns Lecture 2: Forecasting stock returns Prof. Massimo Guidolin Advanced Financial Econometrics III Winter/Spring 2018 Overview The objective of the predictability exercise on stock index returns Predictability

More information

Lecture 2: Forecasting stock returns

Lecture 2: Forecasting stock returns Lecture 2: Forecasting stock returns Prof. Massimo Guidolin Advanced Financial Econometrics III Winter/Spring 2016 Overview The objective of the predictability exercise on stock index returns Predictability

More information

Bayesian Dynamic Linear Models for Strategic Asset Allocation

Bayesian Dynamic Linear Models for Strategic Asset Allocation Bayesian Dynamic Linear Models for Strategic Asset Allocation Jared Fisher Carlos Carvalho, The University of Texas Davide Pettenuzzo, Brandeis University April 18, 2016 Fisher (UT) Bayesian Risk Prediction

More information

Predictive Dynamics in Commodity Prices

Predictive Dynamics in Commodity Prices A. Gargano 1 A. Timmermann 2 1 Bocconi University, visting UCSD 2 UC San Diego, CREATES Introduction Some evidence of modest predictability of commodity price movements by means of economic state variables

More information

Forecasting and model averaging with structural breaks

Forecasting and model averaging with structural breaks Graduate Theses and Dissertations Graduate College 2015 Forecasting and model averaging with structural breaks Anwen Yin Iowa State University Follow this and additional works at: http://lib.dr.iastate.edu/etd

More information

Economic Valuation of Liquidity Timing

Economic Valuation of Liquidity Timing Economic Valuation of Liquidity Timing Dennis Karstanje 1,2 Elvira Sojli 1,3 Wing Wah Tham 1 Michel van der Wel 1,2,4 1 Erasmus University Rotterdam 2 Tinbergen Institute 3 Duisenberg School of Finance

More information

Online Appendix to. The Value of Crowdsourced Earnings Forecasts

Online Appendix to. The Value of Crowdsourced Earnings Forecasts Online Appendix to The Value of Crowdsourced Earnings Forecasts This online appendix tabulates and discusses the results of robustness checks and supplementary analyses mentioned in the paper. A1. Estimating

More information

Return Decomposition over the Business Cycle

Return Decomposition over the Business Cycle Return Decomposition over the Business Cycle Tolga Cenesizoglu March 1, 2016 Cenesizoglu Return Decomposition & the Business Cycle March 1, 2016 1 / 54 Introduction Stock prices depend on investors expectations

More information

Forecasting the Equity Risk Premium: The Role of Technical Indicators

Forecasting the Equity Risk Premium: The Role of Technical Indicators Forecasting the Equity Risk Premium: The Role of Technical Indicators Christopher J. Neely Federal Reserve Bank of St. Louis neely@stls.frb.org Jun Tu Singapore Management University tujun@smu.edu.sg David

More information

Forecasting the Equity Risk Premium: The Role of Technical Indicators

Forecasting the Equity Risk Premium: The Role of Technical Indicators Forecasting the Equity Risk Premium: The Role of Technical Indicators Christopher J. Neely Federal Reserve Bank of St. Louis neely@stls.frb.org David E. Rapach Saint Louis University rapachde@slu.edu Guofu

More information

Predictability of Stock Returns: A Quantile Regression Approach

Predictability of Stock Returns: A Quantile Regression Approach Predictability of Stock Returns: A Quantile Regression Approach Tolga Cenesizoglu HEC Montreal Allan Timmermann UCSD April 13, 2007 Abstract Recent empirical studies suggest that there is only weak evidence

More information

Risk-Adjusted Futures and Intermeeting Moves

Risk-Adjusted Futures and Intermeeting Moves issn 1936-5330 Risk-Adjusted Futures and Intermeeting Moves Brent Bundick Federal Reserve Bank of Kansas City First Version: October 2007 This Version: June 2008 RWP 07-08 Abstract Piazzesi and Swanson

More information

Financial Times Series. Lecture 6

Financial Times Series. Lecture 6 Financial Times Series Lecture 6 Extensions of the GARCH There are numerous extensions of the GARCH Among the more well known are EGARCH (Nelson 1991) and GJR (Glosten et al 1993) Both models allow for

More information

Unpublished Appendices to Déjà Vol: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables

Unpublished Appendices to Déjà Vol: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables Unpublished Appendices to Déjà Vol: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables Bradley S. Paye Terry College of Business, University of Georgia, Athens,

More information

Solving dynamic portfolio choice problems by recursing on optimized portfolio weights or on the value function?

Solving dynamic portfolio choice problems by recursing on optimized portfolio weights or on the value function? DOI 0.007/s064-006-9073-z ORIGINAL PAPER Solving dynamic portfolio choice problems by recursing on optimized portfolio weights or on the value function? Jules H. van Binsbergen Michael W. Brandt Received:

More information

Lecture 5. Predictability. Traditional Views of Market Efficiency ( )

Lecture 5. Predictability. Traditional Views of Market Efficiency ( ) Lecture 5 Predictability Traditional Views of Market Efficiency (1960-1970) CAPM is a good measure of risk Returns are close to unpredictable (a) Stock, bond and foreign exchange changes are not predictable

More information

Lecture 9: Markov and Regime

Lecture 9: Markov and Regime Lecture 9: Markov and Regime Switching Models Prof. Massimo Guidolin 20192 Financial Econometrics Spring 2017 Overview Motivation Deterministic vs. Endogeneous, Stochastic Switching Dummy Regressiom Switching

More information

Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson Siegel Class of Models

Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson Siegel Class of Models Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson Siegel Class of Models August 30, 2018 Hokuto Ishii Graduate School of Economics, Nagoya University Abstract This paper

More information

Chinese Stock Market Volatility and the Role of U.S. Economic Variables

Chinese Stock Market Volatility and the Role of U.S. Economic Variables Chinese Stock Market Volatility and the Role of U.S. Economic Variables Jian Chen Fuwei Jiang Hongyi Li Weidong Xu Current version: June 2015 Abstract This paper investigates the effects of U.S. economic

More information

Lecture 8: Markov and Regime

Lecture 8: Markov and Regime Lecture 8: Markov and Regime Switching Models Prof. Massimo Guidolin 20192 Financial Econometrics Spring 2016 Overview Motivation Deterministic vs. Endogeneous, Stochastic Switching Dummy Regressiom Switching

More information

Out-of-sample stock return predictability in Australia

Out-of-sample stock return predictability in Australia University of Wollongong Research Online Faculty of Business - Papers Faculty of Business 1 Out-of-sample stock return predictability in Australia Yiwen Dou Macquarie University David R. Gallagher Macquarie

More information

Final Exam Suggested Solutions

Final Exam Suggested Solutions University of Washington Fall 003 Department of Economics Eric Zivot Economics 483 Final Exam Suggested Solutions This is a closed book and closed note exam. However, you are allowed one page of handwritten

More information

Forecasting Robust Bond Risk Premia using Technical Indicators

Forecasting Robust Bond Risk Premia using Technical Indicators Forecasting Robust Bond Risk Premia using Technical Indicators M. Noteboom 414137 Bachelor Thesis Quantitative Finance Econometrics & Operations Research Erasmus School of Economics Supervisor: Xiao Xiao

More information

Robust Econometric Inference for Stock Return Predictability

Robust Econometric Inference for Stock Return Predictability Robust Econometric Inference for Stock Return Predictability Alex Kostakis (MBS), Tassos Magdalinos (Southampton) and Michalis Stamatogiannis (Bath) Alex Kostakis, MBS Marie Curie, Konstanz (Alex Kostakis,

More information

Addendum. Multifactor models and their consistency with the ICAPM

Addendum. Multifactor models and their consistency with the ICAPM Addendum Multifactor models and their consistency with the ICAPM Paulo Maio 1 Pedro Santa-Clara This version: February 01 1 Hanken School of Economics. E-mail: paulofmaio@gmail.com. Nova School of Business

More information

Amath 546/Econ 589 Univariate GARCH Models: Advanced Topics

Amath 546/Econ 589 Univariate GARCH Models: Advanced Topics Amath 546/Econ 589 Univariate GARCH Models: Advanced Topics Eric Zivot April 29, 2013 Lecture Outline The Leverage Effect Asymmetric GARCH Models Forecasts from Asymmetric GARCH Models GARCH Models with

More information

FORECASTING PERFORMANCE OF MARKOV-SWITCHING GARCH MODELS: A LARGE-SCALE EMPIRICAL STUDY

FORECASTING PERFORMANCE OF MARKOV-SWITCHING GARCH MODELS: A LARGE-SCALE EMPIRICAL STUDY FORECASTING PERFORMANCE OF MARKOV-SWITCHING GARCH MODELS: A LARGE-SCALE EMPIRICAL STUDY Latest version available on SSRN https://ssrn.com/abstract=2918413 Keven Bluteau Kris Boudt Leopoldo Catania R/Finance

More information

Predicting Inflation without Predictive Regressions

Predicting Inflation without Predictive Regressions Predicting Inflation without Predictive Regressions Liuren Wu Baruch College, City University of New York Joint work with Jian Hua 6th Annual Conference of the Society for Financial Econometrics June 12-14,

More information

Return predictability

Return predictability UNIVERSITEIT GENT FACULTEIT ECONOMIE EN BEDRIJFSKUNDE ACADEMIEJAAR 015 016 Return predictability Can you outperform the historical average? Gilles Bekaert & Thibaut Van Weehaeghe onder leiding van Prof.

More information

Foreign Exchange Market and Equity Risk Premium Forecasting

Foreign Exchange Market and Equity Risk Premium Forecasting Foreign Exchange Market and Equity Risk Premium Forecasting Jun Tu Singapore Management University Yuchen Wang Singapore Management University October 08, 2013 Corresponding author. Send correspondence

More information

A Note on Predicting Returns with Financial Ratios

A Note on Predicting Returns with Financial Ratios A Note on Predicting Returns with Financial Ratios Amit Goyal Goizueta Business School Emory University Ivo Welch Yale School of Management Yale Economics Department NBER December 16, 2003 Abstract This

More information

Journal of Economics and Financial Analysis, Vol:1, No:1 (2017) 1-13

Journal of Economics and Financial Analysis, Vol:1, No:1 (2017) 1-13 Journal of Economics and Financial Analysis, Vol:1, No:1 (2017) 1-13 Journal of Economics and Financial Analysis Type: Double Blind Peer Reviewed Scientific Journal Printed ISSN: 2521-6627 Online ISSN:

More information

Robust Econometric Inference for Stock Return Predictability

Robust Econometric Inference for Stock Return Predictability Robust Econometric Inference for Stock Return Predictability Alex Kostakis (MBS), Tassos Magdalinos (Southampton) and Michalis Stamatogiannis (Bath) Alex Kostakis, MBS 2nd ISNPS, Cadiz (Alex Kostakis,

More information

A Markov switching regime model of the South African business cycle

A Markov switching regime model of the South African business cycle A Markov switching regime model of the South African business cycle Elna Moolman Abstract Linear models are incapable of capturing business cycle asymmetries. This has recently spurred interest in non-linear

More information

Applied Macro Finance

Applied Macro Finance Master in Money and Finance Goethe University Frankfurt Week 2: Factor models and the cross-section of stock returns Fall 2012/2013 Please note the disclaimer on the last page Announcements Next week (30

More information

Discussion of "Yield Curve Premia" by Brooks and Moskowitz

Discussion of Yield Curve Premia by Brooks and Moskowitz Discussion of "Yield Curve Premia" by Brooks and Moskowitz Monika Piazzesi Stanford & NBER SI AP Meeting 2017 Piazzesi (Stanford) SI AP Meeting 2017 1 / 16 summary "carry" and "value" predict excess returns

More information

Macro-Investment Risks and Style Selection Michael Howell

Macro-Investment Risks and Style Selection Michael Howell Macro-Investment Risks and Style Selection Michael Howell LQG Spring Seminar 18th May 2017 At The Royal Geographical Society 1 Kensington Gore, SW7 2AR D-Star (Position of Curvature Peak in Years, 6-month

More information

Indian Institute of Management Calcutta. Working Paper Series. WPS No. 797 March Implied Volatility and Predictability of GARCH Models

Indian Institute of Management Calcutta. Working Paper Series. WPS No. 797 March Implied Volatility and Predictability of GARCH Models Indian Institute of Management Calcutta Working Paper Series WPS No. 797 March 2017 Implied Volatility and Predictability of GARCH Models Vivek Rajvanshi Assistant Professor, Indian Institute of Management

More information

Bayesian Estimation of the Markov-Switching GARCH(1,1) Model with Student-t Innovations

Bayesian Estimation of the Markov-Switching GARCH(1,1) Model with Student-t Innovations Bayesian Estimation of the Markov-Switching GARCH(1,1) Model with Student-t Innovations Department of Quantitative Economics, Switzerland david.ardia@unifr.ch R/Rmetrics User and Developer Workshop, Meielisalp,

More information

Online Appendix Not For Publication

Online Appendix Not For Publication Online Appendix Not For Publication For A Tale of Two Volatilities: Sectoral Uncertainty, Growth, and Asset Prices OA.1. Supplemental Sections OA.1.1. Description of TFP Data From Fernald (212) This section

More information

Investor Sentiment Aligned: A Powerful Predictor of Stock Returns

Investor Sentiment Aligned: A Powerful Predictor of Stock Returns Investor Sentiment Aligned: A Powerful Predictor of Stock Returns Dashan Huang Singapore Management University Jun Tu Singapore Management University Fuwei Jiang Singapore Management University Guofu Zhou

More information

Internet Appendix for: Cyclical Dispersion in Expected Defaults

Internet Appendix for: Cyclical Dispersion in Expected Defaults Internet Appendix for: Cyclical Dispersion in Expected Defaults March, 2018 Contents 1 1 Robustness Tests The results presented in the main text are robust to the definition of debt repayments, and the

More information

Analyzing Oil Futures with a Dynamic Nelson-Siegel Model

Analyzing Oil Futures with a Dynamic Nelson-Siegel Model Analyzing Oil Futures with a Dynamic Nelson-Siegel Model NIELS STRANGE HANSEN & ASGER LUNDE DEPARTMENT OF ECONOMICS AND BUSINESS, BUSINESS AND SOCIAL SCIENCES, AARHUS UNIVERSITY AND CENTER FOR RESEARCH

More information

HIDDEN SLIDE. How do low interest rates affect asset allocation? What pension funds do and should do. Own research

HIDDEN SLIDE. How do low interest rates affect asset allocation? What pension funds do and should do. Own research Pension fund asset allocation in a low interest rate environment How do low interest rates affect asset allocation? Dennis Bams, Peter Schotman and Mukul Tyagi Peter Dennis Rogier Mukul Schotman Bams Quaedvlieg

More information

Further Test on Stock Liquidity Risk With a Relative Measure

Further Test on Stock Liquidity Risk With a Relative Measure International Journal of Education and Research Vol. 1 No. 3 March 2013 Further Test on Stock Liquidity Risk With a Relative Measure David Oima* David Sande** Benjamin Ombok*** Abstract Negative relationship

More information

Predicting the equity premium via its components

Predicting the equity premium via its components Predicting the equity premium via its components Fabian Baetje and Lukas Menkhoff Abstract We propose a refined way of forecasting the equity premium. Our approach rests on the sum-ofparts approach which

More information

Forecasting Market Returns: Bagging or Combining?

Forecasting Market Returns: Bagging or Combining? Forecasting Market Returns: Bagging or Combining? Steven J. Jordan Econometric Solutions econometric.solutions@yahoo.com Andrew Vivian Loughborough University a.j.vivian@lboro.ac.uk Mark E. Wohar University

More information

Financial Econometrics Series SWP 2015/13. Stock Return Forecasting: Some New Evidence. D. H. B. Phan, S. S. Sharma, P.K. Narayan

Financial Econometrics Series SWP 2015/13. Stock Return Forecasting: Some New Evidence. D. H. B. Phan, S. S. Sharma, P.K. Narayan Faculty of Business and Law School of Accounting, Economics and Finance Financial Econometrics Series SWP 015/13 Stock Return Forecasting: Some New Evidence D. H. B. Phan, S. S. Sharma, P.K. Narayan The

More information

Global connectedness across bond markets

Global connectedness across bond markets Global connectedness across bond markets Stig V. Møller Jesper Rangvid June 2018 Abstract We provide first tests of gradual diffusion of information across bond markets. We show that excess returns on

More information

A Regime-Switching Relative Value Arbitrage Rule

A Regime-Switching Relative Value Arbitrage Rule A Regime-Switching Relative Value Arbitrage Rule Michael Bock and Roland Mestel University of Graz, Institute for Banking and Finance Universitaetsstrasse 15/F2, A-8010 Graz, Austria {michael.bock,roland.mestel}@uni-graz.at

More information

A Closer Look at High-Frequency Data and Volatility Forecasting in a HAR Framework 1

A Closer Look at High-Frequency Data and Volatility Forecasting in a HAR Framework 1 A Closer Look at High-Frequency Data and Volatility Forecasting in a HAR Framework 1 Derek Song ECON 21FS Spring 29 1 This report was written in compliance with the Duke Community Standard 2 1. Introduction

More information

GDP, Share Prices, and Share Returns: Australian and New Zealand Evidence

GDP, Share Prices, and Share Returns: Australian and New Zealand Evidence Journal of Money, Investment and Banking ISSN 1450-288X Issue 5 (2008) EuroJournals Publishing, Inc. 2008 http://www.eurojournals.com/finance.htm GDP, Share Prices, and Share Returns: Australian and New

More information

Overseas unspanned factors and domestic bond returns

Overseas unspanned factors and domestic bond returns Overseas unspanned factors and domestic bond returns Andrew Meldrum Bank of England Marek Raczko Bank of England 9 October 2015 Peter Spencer University of York PRELIMINARY AND INCOMPLETE Abstract Using

More information

Breaks in Return Predictability

Breaks in Return Predictability Breaks in Return Predictability Simon C. Smith a, Allan Timmermann b a USC Dornsife INET, Department of Economics, USC, 3620 South Vermont Ave., CA, 90089-0253, USA b University of California, San Diego,

More information

Online Appendix for Demand for Crash Insurance, Intermediary Constraints, and Risk Premia in Financial Markets

Online Appendix for Demand for Crash Insurance, Intermediary Constraints, and Risk Premia in Financial Markets Online Appendix for Demand for Crash Insurance, Intermediary Constraints, and Risk Premia in Financial Markets Hui Chen Scott Joslin Sophie Ni January 19, 2016 1 An Extension of the Dynamic Model Our model

More information

A Quantile Regression Approach to Equity Premium Prediction

A Quantile Regression Approach to Equity Premium Prediction A Quantile Regression Approach to Equity Premium Prediction Loukia Meligkotsidou a, Ekaterini Panopoulou b, Ioannis D.Vrontos c, Spyridon D. Vrontos b a Department of Mathematics, University of Athens,

More information

A potentially useful approach to model nonlinearities in time series is to assume different behavior (structural break) in different subsamples

A potentially useful approach to model nonlinearities in time series is to assume different behavior (structural break) in different subsamples 1.3 Regime switching models A potentially useful approach to model nonlinearities in time series is to assume different behavior (structural break) in different subsamples (or regimes). If the dates, the

More information

An Implementation of Markov Regime Switching GARCH Models in Matlab

An Implementation of Markov Regime Switching GARCH Models in Matlab An Implementation of Markov Regime Switching GARCH Models in Matlab Thomas Chuffart Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS Abstract MSGtool is a MATLAB toolbox which

More information

Time-variation of CAPM betas across market volatility regimes for Book-to-market and Momentum portfolios

Time-variation of CAPM betas across market volatility regimes for Book-to-market and Momentum portfolios Time-variation of CAPM betas across market volatility regimes for Book-to-market and Momentum portfolios Azamat Abdymomunov James Morley Department of Economics Washington University in St. Louis October

More information

A Nonlinear Approach to the Factor Augmented Model: The FASTR Model

A Nonlinear Approach to the Factor Augmented Model: The FASTR Model A Nonlinear Approach to the Factor Augmented Model: The FASTR Model B.J. Spruijt - 320624 Erasmus University Rotterdam August 2012 This research seeks to combine Factor Augmentation with Smooth Transition

More information

Corporate Investment and Portfolio Returns in Japan: A Markov Switching Approach

Corporate Investment and Portfolio Returns in Japan: A Markov Switching Approach Corporate Investment and Portfolio Returns in Japan: A Markov Switching Approach 1 Faculty of Economics, Chuo University, Tokyo, Japan Chikashi Tsuji 1 Correspondence: Chikashi Tsuji, Professor, Faculty

More information

What is the Optimal Investment in a Hedge Fund? ERM symposium Chicago

What is the Optimal Investment in a Hedge Fund? ERM symposium Chicago What is the Optimal Investment in a Hedge Fund? ERM symposium Chicago March 29 2007 Phelim Boyle Wilfrid Laurier University and Tirgarvil Capital pboyle at wlu.ca Phelim Boyle Hedge Funds 1 Acknowledgements

More information

Overseas unspanned factors and domestic bond returns

Overseas unspanned factors and domestic bond returns Overseas unspanned factors and domestic bond returns Andrew Meldrum Bank of England Marek Raczko Bank of England 19 November 215 Peter Spencer University of York Abstract Using data on government bonds

More information

Commodity Prices, Commodity Currencies, and Global Economic Developments

Commodity Prices, Commodity Currencies, and Global Economic Developments Commodity Prices, Commodity Currencies, and Global Economic Developments Jan J. J. Groen Paolo A. Pesenti Federal Reserve Bank of New York August 16-17, 2012 FGV-Vale Conference The Economics and Econometrics

More information

Return Predictability Revisited Using Weighted Least Squares

Return Predictability Revisited Using Weighted Least Squares Return Predictability Revisited Using Weighted Least Squares Travis L. Johnson McCombs School of Business The University of Texas at Austin January 2017 Abstract I show that important conclusions about

More information

Inflation Dynamics During the Financial Crisis

Inflation Dynamics During the Financial Crisis Inflation Dynamics During the Financial Crisis S. Gilchrist 1 1 Boston University and NBER MFM Summer Camp June 12, 2016 DISCLAIMER: The views expressed are solely the responsibility of the authors and

More information

Analyzing the Determinants of Project Success: A Probit Regression Approach

Analyzing the Determinants of Project Success: A Probit Regression Approach 2016 Annual Evaluation Review, Linked Document D 1 Analyzing the Determinants of Project Success: A Probit Regression Approach 1. This regression analysis aims to ascertain the factors that determine development

More information

in-depth Invesco Actively Managed Low Volatility Strategies The Case for

in-depth Invesco Actively Managed Low Volatility Strategies The Case for Invesco in-depth The Case for Actively Managed Low Volatility Strategies We believe that active LVPs offer the best opportunity to achieve a higher risk-adjusted return over the long term. Donna C. Wilson

More information

Can Hedge Funds Time the Market?

Can Hedge Funds Time the Market? International Review of Finance, 2017 Can Hedge Funds Time the Market? MICHAEL W. BRANDT,FEDERICO NUCERA AND GIORGIO VALENTE Duke University, The Fuqua School of Business, Durham, NC LUISS Guido Carli

More information

Risk Premia and the Conditional Tails of Stock Returns

Risk Premia and the Conditional Tails of Stock Returns Risk Premia and the Conditional Tails of Stock Returns Bryan Kelly NYU Stern and Chicago Booth Outline Introduction An Economic Framework Econometric Methodology Empirical Findings Conclusions Tail Risk

More information

Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS001) p approach

Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS001) p approach Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS001) p.5901 What drives short rate dynamics? approach A functional gradient descent Audrino, Francesco University

More information

Stock market firm-level information and real economic activity

Stock market firm-level information and real economic activity Stock market firm-level information and real economic activity F. di Mauro, F. Fornari, D. Mannucci Presentation at the EFIGE Associate Partner Meeting Milano, 31 March 2011 March 29, 2011 The Great Recession

More information

The Effectiveness of Alternative Monetary Policy Tools in a Zero Lower Bound Environment

The Effectiveness of Alternative Monetary Policy Tools in a Zero Lower Bound Environment The Effectiveness of Alternative Monetary Policy Tools in a Zero Lower Bound Environment James D. Hamilton Jing (Cynthia) Wu Department of Economics UC San Diego Hamilton and Wu (UCSD) ZLB 1 / 33 What

More information

Predicting Bear and Bull Stock Markets with Dynamic Binary Time Series Models

Predicting Bear and Bull Stock Markets with Dynamic Binary Time Series Models ömmföäflsäafaäsflassflassflas ffffffffffffffffffffffffffffffffff Discussion Papers Predicting Bear and Bull Stock Markets with Dynamic Binary Time Series Models Henri Nyberg University of Helsinki Discussion

More information

Optimal Window Selection for Forecasting in The Presence of Recent Structural Breaks

Optimal Window Selection for Forecasting in The Presence of Recent Structural Breaks Optimal Window Selection for Forecasting in The Presence of Recent Structural Breaks Yongli Wang University of Leicester Econometric Research in Finance Workshop on 15 September 2017 SGH Warsaw School

More information

Growth Opportunities, Investment-Specific Technology Shocks and the Cross-Section of Stock Returns

Growth Opportunities, Investment-Specific Technology Shocks and the Cross-Section of Stock Returns Growth Opportunities, Investment-Specific Technology Shocks and the Cross-Section of Stock Returns Leonid Kogan 1 Dimitris Papanikolaou 2 1 MIT and NBER 2 Northwestern University Boston, June 5, 2009 Kogan,

More information

Statistical Understanding. of the Fama-French Factor model. Chua Yan Ru

Statistical Understanding. of the Fama-French Factor model. Chua Yan Ru i Statistical Understanding of the Fama-French Factor model Chua Yan Ru NATIONAL UNIVERSITY OF SINGAPORE 2012 ii Statistical Understanding of the Fama-French Factor model Chua Yan Ru (B.Sc National University

More information

Banca d Italia. Ministero dell Economia e delle Finanze. November Real time forecasts of in ation: the role of.

Banca d Italia. Ministero dell Economia e delle Finanze. November Real time forecasts of in ation: the role of. Banca d Italia Ministero dell Economia e delle Finanze November 2008 We present a mixed to forecast in ation in real time It can be easily estimated on a daily basis using all the information available

More information

Online Appendix for. Short-Run and Long-Run Consumption Risks, Dividend Processes, and Asset Returns

Online Appendix for. Short-Run and Long-Run Consumption Risks, Dividend Processes, and Asset Returns Online Appendix for Short-Run and Long-Run Consumption Risks, Dividend Processes, and Asset Returns 1 More on Fama-MacBeth regressions This section compares the performance of Fama-MacBeth regressions

More information

Rediscover Predictability: Information from the Relative Prices of Long-term and Short-term Dividends

Rediscover Predictability: Information from the Relative Prices of Long-term and Short-term Dividends Rediscover Predictability: Information from the Relative Prices of Long-term and Short-term Dividends Ye Li Chen Wang March 6, 2018 Abstract The prices of dividends at alternative horizons contain critical

More information

Forecasting Volatility movements using Markov Switching Regimes. This paper uses Markov switching models to capture volatility dynamics in exchange

Forecasting Volatility movements using Markov Switching Regimes. This paper uses Markov switching models to capture volatility dynamics in exchange Forecasting Volatility movements using Markov Switching Regimes George S. Parikakis a1, Theodore Syriopoulos b a Piraeus Bank, Corporate Division, 4 Amerikis Street, 10564 Athens Greece bdepartment of

More information

Investing through Economic Cycles with Ensemble Machine Learning Algorithms

Investing through Economic Cycles with Ensemble Machine Learning Algorithms Investing through Economic Cycles with Ensemble Machine Learning Algorithms Thomas Raffinot Silex Investment Partners Big Data in Finance Conference Thomas Raffinot (Silex-IP) Economic Cycles-Machine Learning

More information

Monetary and Fiscal Policy Switching with Time-Varying Volatilities

Monetary and Fiscal Policy Switching with Time-Varying Volatilities Monetary and Fiscal Policy Switching with Time-Varying Volatilities Libo Xu and Apostolos Serletis Department of Economics University of Calgary Calgary, Alberta T2N 1N4 Forthcoming in: Economics Letters

More information

On the economic significance of stock return predictability: Evidence from macroeconomic state variables

On the economic significance of stock return predictability: Evidence from macroeconomic state variables On the economic significance of stock return predictability: Evidence from macroeconomic state variables Huacheng Zhang * University of Arizona This draft: 8/31/2012 First draft: 2/28/2012 Abstract We

More information

Predictability of Corporate Bond Returns: A Comprehensive Study

Predictability of Corporate Bond Returns: A Comprehensive Study Predictability of Corporate Bond Returns: A Comprehensive Study Hai Lin Victoria University of Wellington Chunchi Wu State University of New York at Buffalo and Guofu Zhou Washington University in St.

More information

Market Timing Does Work: Evidence from the NYSE 1

Market Timing Does Work: Evidence from the NYSE 1 Market Timing Does Work: Evidence from the NYSE 1 Devraj Basu Alexander Stremme Warwick Business School, University of Warwick November 2005 address for correspondence: Alexander Stremme Warwick Business

More information

Forecasting Singapore economic growth with mixed-frequency data

Forecasting Singapore economic growth with mixed-frequency data Edith Cowan University Research Online ECU Publications 2013 2013 Forecasting Singapore economic growth with mixed-frequency data A. Tsui C.Y. Xu Zhaoyong Zhang Edith Cowan University, zhaoyong.zhang@ecu.edu.au

More information

Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking?

Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking? Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking? October 19, 2009 Ulrike Malmendier, UC Berkeley (joint work with Stefan Nagel, Stanford) 1 The Tale of Depression Babies I don t know

More information

20135 Theory of Finance Part I Professor Massimo Guidolin

20135 Theory of Finance Part I Professor Massimo Guidolin MSc. Finance/CLEFIN 2014/2015 Edition 20135 Theory of Finance Part I Professor Massimo Guidolin A FEW SAMPLE QUESTIONS, WITH SOLUTIONS SET 2 WARNING: These are just sample questions. Please do not count

More information

Discussion of Trend Inflation in Advanced Economies

Discussion of Trend Inflation in Advanced Economies Discussion of Trend Inflation in Advanced Economies James Morley University of New South Wales 1. Introduction Garnier, Mertens, and Nelson (this issue, GMN hereafter) conduct model-based trend/cycle decomposition

More information

Risk-managed 52-week high industry momentum, momentum crashes, and hedging macroeconomic risk

Risk-managed 52-week high industry momentum, momentum crashes, and hedging macroeconomic risk Risk-managed 52-week high industry momentum, momentum crashes, and hedging macroeconomic risk Klaus Grobys¹ This draft: January 23, 2017 Abstract This is the first study that investigates the profitability

More information

Evaluating the time-varying impact of economic data on the. accuracy of stock market volatility forecasts

Evaluating the time-varying impact of economic data on the. accuracy of stock market volatility forecasts Evaluating the time-varying impact of economic data on the accuracy of stock market volatility forecasts Annika Lindblad July 10, 2018 Abstract I assess the time-variation in predictive ability arising

More information

Effects of Outliers and Parameter Uncertainties in Portfolio Selection

Effects of Outliers and Parameter Uncertainties in Portfolio Selection Effects of Outliers and Parameter Uncertainties in Portfolio Selection Luiz Hotta 1 Carlos Trucíos 2 Esther Ruiz 3 1 Department of Statistics, University of Campinas. 2 EESP-FGV (postdoctoral). 3 Department

More information

Discussion of The Term Structure of Growth-at-Risk

Discussion of The Term Structure of Growth-at-Risk Discussion of The Term Structure of Growth-at-Risk Frank Schorfheide University of Pennsylvania, CEPR, NBER, PIER March 2018 Pushing the Frontier of Central Bank s Macro Modeling Preliminaries This paper

More information

A RIDGE REGRESSION ESTIMATION APPROACH WHEN MULTICOLLINEARITY IS PRESENT

A RIDGE REGRESSION ESTIMATION APPROACH WHEN MULTICOLLINEARITY IS PRESENT Fundamental Journal of Applied Sciences Vol. 1, Issue 1, 016, Pages 19-3 This paper is available online at http://www.frdint.com/ Published online February 18, 016 A RIDGE REGRESSION ESTIMATION APPROACH

More information

Consumption Fluctuations and Expected Returns

Consumption Fluctuations and Expected Returns Consumption Fluctuations and Expected Returns Victoria Atanasov, Stig Vinther Møller, and Richard Priestley Abstract This paper introduces a new consumption-based variable, cyclical consumption, and examines

More information