Long and Short Run Correlation Risk in Stock Returns
|
|
|
- Rudolf Cobb
- 5 years ago
- Views:
Transcription
1 Long and Short Run Correlation Risk in Stock Returns Discussion by Ric Colacito Econometric Society Winter Meetings, Denver, 1/ / 10
2 Contribution 1 Background: market variance risk premium predicts the equity premium. 2 / 10
3 Contribution 1 Background: market variance risk premium predicts the equity premium. 2 Question: is this because of individual securities risk or because of correlation risk? 2 / 10
4 Contribution 1 Background: market variance risk premium predicts the equity premium. 2 Question: is this because of individual securities risk or because of correlation risk? 3 This paper: explanatory power is driven by correlations (especially long-run correlations!) 2 / 10
5 Empirical contribution in a nutshell 1 Market variance risk premium predicts the equity premium Monthly Return Regressions Quarterly Return Regressions Panel A: Predictability Regressions Excluding Traditional Predictors VRP Mt (2.33) (4.66) VRP t ( 0.53) ( 0.28) ( 1.18) ( 1.03) CRP t (2.81) (2.73) (3.88) (3.86) 2 3 / 10
6 Empirical contribution in a nutshell 1 Market variance risk premium predicts the equity premium 2 Explanatory power is driven by correlations and not by idiosyncratic risk Monthly Return Regressions Quarterly Return Regressions Panel A: Predictability Regressions Excluding Traditional Predictors VRP Mt (2.33) (4.66) VRP t ( 0.53) ( 0.28) ( 1.18) ( 1.03) CRP t (2.81) (2.73) (3.88) (3.86) 2 3 / 10
7 Including the usual suspects... Panel B: Predictability Regressions Including Traditional Predictors V RP Mt (2.93) (4.16) V RP t ( 0.16) ( 0.10) ( 1.02) ( 1.26) CRP t (2.01) (2.06) (2.84) (3.07) RV Mt (1.98) (2.21) RV t ( 0.34) ( 0.13) ( 1.24) ( 0.78) RC t (1.98) (2.02) (2.44) (2.26) CAY t (1.04) (2.53) (0.43) (0.37) (0.33) (2.57) ( 0.55) ( 0.04) DEF t ( 0.16) (0.21) ( 0.13) ( 0.13) ( 0.96) ( 0.39) ( 0.78) ( 0.71) log(p/e) t ( 2.19) ( 0.58) ( 0.91) ( 0.35) ( 2.33) (0.02) ( 0.55) (0.15) T ERM t (0.33) ( 0.48) ( 1.59) ( 1.42) (0.71) ( 0.45) ( 1.50) ( 1.72) Adj. R 2 (%) Usual suspects are not significant Does this hold also for other forecasting horizons? 4 / 10
8 Including the usual suspects... Panel B: Predictability Regressions Including Traditional Predictors V RP Mt (2.93) (4.16) V RP t ( 0.16) ( 0.10) ( 1.02) ( 1.26) CRP t (2.01) (2.06) (2.84) (3.07) RV Mt (1.98) (2.21) RV t ( 0.34) ( 0.13) ( 1.24) ( 0.78) RC t (1.98) (2.02) (2.44) (2.26) CAY t (1.04) (2.53) (0.43) (0.37) (0.33) (2.57) ( 0.55) ( 0.04) DEF t ( 0.16) (0.21) ( 0.13) ( 0.13) ( 0.96) ( 0.39) ( 0.78) ( 0.71) log(p/e) t ( 2.19) ( 0.58) ( 0.91) ( 0.35) ( 2.33) (0.02) ( 0.55) (0.15) T ERM t (0.33) ( 0.48) ( 1.59) ( 1.42) (0.71) ( 0.45) ( 1.50) ( 1.72) Adj. R 2 (%) Economic significance How do returns respond to a one standard deviation shock? 4 / 10
9 Economic significance β σ skewness kurtosis effect on returns CAY b.p. VRP M b.p. CRP b.p. 5 / 10
10 Economic significance β σ skewness kurtosis effect on returns CAY b.p. VRP M b.p. CRP b.p. 1 Returns even more sensitive to correlation shocks: why? 5 / 10
11 Economic significance β σ skewness kurtosis effect on returns CAY b.p. VRP M b.p. CRP b.p. 1 Returns even more sensitive to correlation shocks: why? 2 Adjusted R 2 are 3 to 4 times as large with correlations. 5 / 10
12 Economic significance β σ skewness kurtosis effect on returns CAY b.p. VRP M b.p. CRP b.p. 1 Returns even more sensitive to correlation shocks: why? 2 Adjusted R 2 are 3 to 4 times as large with correlations. Are we capturing something more? 5 / 10
13 Comments on time series analysis 1 Comment on statistical insignificance of usual suspects 2 Comment on economic significance of one standard deviation shock to correlation (check the units) 6 / 10
14 Comments on time series analysis 1 Comment on statistical insignificance of usual suspects 2 Comment on economic significance of one standard deviation shock to correlation (check the units) 3 Does the result hold at longer forecasting horizons? 6 / 10
15 Comments on time series analysis 1 Comment on statistical insignificance of usual suspects 2 Comment on economic significance of one standard deviation shock to correlation (check the units) 3 Does the result hold at longer forecasting horizons? 4 Can we decompose correlation into short- and long-run component also in the time series analysis? 6 / 10
16 Comments on time series analysis 1 Comment on statistical insignificance of usual suspects 2 Comment on economic significance of one standard deviation shock to correlation (check the units) 3 Does the result hold at longer forecasting horizons? 4 Can we decompose correlation into short- and long-run component also in the time series analysis? E.g. extract correlations from options with various maturities... 6 / 10
17 Comments on time series analysis 1 Comment on statistical insignificance of usual suspects 2 Comment on economic significance of one standard deviation shock to correlation (check the units) 3 Does the result hold at longer forecasting horizons? 4 Can we decompose correlation into short- and long-run component also in the time series analysis? E.g. extract correlations from options with various maturities... 5 Comment on the methodology for estimating equi-correlations... 6 / 10
18 Methodology for estimating correlations 1 Assume that all correlations are identical (ρ) 7 / 10
19 Methodology for estimating correlations 1 Assume that all correlations are identical (ρ) 2 Consider a vector of portfolio weights (w = [w 1,w 2,...,w n ] ) 7 / 10
20 Methodology for estimating correlations 1 Assume that all correlations are identical (ρ) 2 Consider a vector of portfolio weights (w = [w 1,w 2,...,w n ] ) 3 Note that portfolio variance is V[w r] = n w 2 j=1 j V[r j ] + 2ρ n n j=1 i=1,i j w j w i V[r i ]V[r j ] 7 / 10
21 Methodology for estimating correlations 1 Assume that all correlations are identical (ρ) 2 Consider a vector of portfolio weights (w = [w 1,w 2,...,w n ] ) 3 Note that portfolio variance is V[w r] = n w 2 j=1 j V[r j ] + 2ρ n n j=1 i=1,i j w j w i V[r i ]V[r j ] 4 Estimate separately individual variances (V[r 1 ],V[r 2 ],...) and portfolio variance (V[w r]). 7 / 10
22 Methodology for estimating correlations 1 Assume that all correlations are identical (ρ) 2 Consider a vector of portfolio weights (w = [w 1,w 2,...,w n ] ) 3 Note that portfolio variance is V[w r] = n w 2 j=1 j V[r j ] + 2ρ n n j=1 i=1,i j w j w i V[r i ]V[r j ] 4 Estimate separately individual variances (V[r 1 ],V[r 2 ],...) and portfolio variance (V[w r]). 5 Use known portfolio weights to back out correlation (ρ) 7 / 10
23 Methodology for estimating correlations 1 Assume that all correlations are identical (ρ) 2 Consider a vector of portfolio weights (w = [w 1,w 2,...,w n ] ) 3 Note that portfolio variance is V[w r] = n w 2 j=1 j V[r j ] + 2ρ n n j=1 i=1,i j w j w i V[r i ]V[r j ] 4 Estimate separately individual variances (V[r 1 ],V[r 2 ],...) and portfolio variance (V[w r]). 5 Use known portfolio weights to back out correlation (ρ) Comparison to DECO model by Engle and Kelly (2009)? 7 / 10
24 Methodology for estimating correlations 1 Assume that all correlations are identical (ρ) 2 Consider a vector of portfolio weights (w = [w 1,w 2,...,w n ] ) 3 Note that portfolio variance is V[w r] = n w 2 j=1 j V[r j ] + 2ρ n n j=1 i=1,i j w j w i V[r i ]V[r j ] 4 Estimate separately individual variances (V[r 1 ],V[r 2 ],...) and portfolio variance (V[w r]). 5 Use known portfolio weights to back out correlation (ρ) Comparison to DECO model by Engle and Kelly (2009)? Is this helpful for asset allocation in large systems? 7 / 10
25 Cross-sectional results RM (3.25) (2.08) (3.20) (2.15) (2.65) (1.74) (3.90) (2.20) (2.78) (1.88) HF MV OL ( 3.98) ( 3.92) LF MV OL ( 1.99) ( 1.87) HF IV OL (0.92) (0.93) (1.14) (1.14) LF IV OL ( 1.82) ( 1.72) ( 2.87) ( 2.25) HF COR ( 1.73) ( 1.70) ( 1.71) ( 1.72) LF COR ( 2.89) ( 2.86) ( 3.54) ( 3.32) SMB (0.62) (0.84) (0.92) (1.00) (0.73) (0.88) (0.30) (0.54) (0.44) (0.55) HML (2.15) (0.67) (1.32) (0.50) (1.64) (0.61) (2.82) (1.28) (1.79) (1.04) UMD ( 2.00) ( 1.33) ( 1.71) ( 1.99) ( 1.09) ILLIQ ( 0.86) ( 1.06) ( 0.96) ( 0.67) ( 0.63) Wald p-value (0.94) (0.91) (0.89) (0.87) (0.94) (0.92) (0.57) (0.51) (0.45) (0.38) Long-run correlation is significant, short-run correlation is not 8 / 10
26 Cross-sectional results RM (3.25) (2.08) (3.20) (2.15) (2.65) (1.74) (3.90) (2.20) (2.78) (1.88) HF MV OL ( 3.98) ( 3.92) LF MV OL ( 1.99) ( 1.87) HF IV OL (0.92) (0.93) (1.14) (1.14) LF IV OL ( 1.82) ( 1.72) ( 2.87) ( 2.25) HF COR ( 1.73) ( 1.70) ( 1.71) ( 1.72) LF COR ( 2.89) ( 2.86) ( 3.54) ( 3.32) SMB (0.62) (0.84) (0.92) (1.00) (0.73) (0.88) (0.30) (0.54) (0.44) (0.55) HML (2.15) (0.67) (1.32) (0.50) (1.64) (0.61) (2.82) (1.28) (1.79) (1.04) UMD ( 2.00) ( 1.33) ( 1.71) ( 1.99) ( 1.09) ILLIQ ( 0.86) ( 1.06) ( 0.96) ( 0.67) ( 0.63) Wald p-value (0.94) (0.91) (0.89) (0.87) (0.94) (0.92) (0.57) (0.51) (0.45) (0.38) Long-run correlation is significant, short-run correlation is not Economic significance? 8 / 10
27 Cross-sectional results RM (3.25) (2.08) (3.20) (2.15) (2.65) (1.74) (3.90) (2.20) (2.78) (1.88) HF MV OL ( 3.98) ( 3.92) LF MV OL ( 1.99) ( 1.87) HF IV OL (0.92) (0.93) (1.14) (1.14) LF IV OL ( 1.82) ( 1.72) ( 2.87) ( 2.25) HF COR ( 1.73) ( 1.70) ( 1.71) ( 1.72) LF COR ( 2.89) ( 2.86) ( 3.54) ( 3.32) SMB (0.62) (0.84) (0.92) (1.00) (0.73) (0.88) (0.30) (0.54) (0.44) (0.55) HML (2.15) (0.67) (1.32) (0.50) (1.64) (0.61) (2.82) (1.28) (1.79) (1.04) UMD ( 2.00) ( 1.33) ( 1.71) ( 1.99) ( 1.09) ILLIQ ( 0.86) ( 1.06) ( 0.96) ( 0.67) ( 0.63) Wald p-value (0.94) (0.91) (0.89) (0.87) (0.94) (0.92) (0.57) (0.51) (0.45) (0.38) Long-run correlation is significant, short-run correlation is not Economic significance? Robustness of results to alternative estimators of short- and long-run covariance matrices? 8 / 10
28 One more intriguing results HF LF HF LF HF LF Mean Std. Dev. RM MVOL MVOL IVOL IVOL COR COR SMB HML UMD ILLIQ RM HF MVOL LF MVOL HF IVOL LF IVOL HF COR LF COR SMB HML UMD ILLIQ Short-run: market volatility and correlation co-move a lot 9 / 10
29 One more intriguing results HF LF HF LF HF LF Mean Std. Dev. RM MVOL MVOL IVOL IVOL COR COR SMB HML UMD ILLIQ RM HF MVOL LF MVOL HF IVOL LF IVOL HF COR LF COR SMB HML UMD ILLIQ Short-run: market volatility and correlation co-move a lot Long-run: market volatility and correlation co-move way less 9 / 10
30 One more intriguing results HF LF HF LF HF LF Mean Std. Dev. RM MVOL MVOL IVOL IVOL COR COR SMB HML UMD ILLIQ RM HF MVOL LF MVOL HF IVOL LF IVOL HF COR LF COR SMB HML UMD ILLIQ Short-run: market volatility and correlation co-move a lot Long-run: market volatility and correlation co-move way less Implications for asset allocation at various horizons 9 / 10
31 Concluding remarks A very interesting paper! Suggestions: 1 economic significance? 2 robustness? 3 can methodology be extended to other applications? 4 more comments on the co-movement of correlations and volatilities at different frequencies 10 / 10
Is Economic Uncertainty Priced in the Cross-Section of Stock Returns?
Is Economic Uncertainty Priced in the Cross-Section of Stock Returns? Turan Bali, Georgetown University Stephen Brown, NYU Stern, University Yi Tang, Fordham University 2018 CARE Conference, Washington
Asset Pricing and Excess Returns over the Market Return
Supplemental material for Asset Pricing and Excess Returns over the Market Return Seung C. Ahn Arizona State University Alex R. Horenstein University of Miami This documents contains an additional figure
Predicting the Equity Premium with Implied Volatility Spreads
Predicting the Equity Premium with Implied Volatility Spreads Charles Cao, Timothy Simin, and Han Xiao Department of Finance, Smeal College of Business, Penn State University Department of Economics, Penn
What Does Risk-Neutral Skewness Tell Us About Future Stock Returns? Supplementary Online Appendix
What Does Risk-Neutral Skewness Tell Us About Future Stock Returns? Supplementary Online Appendix 1 Tercile Portfolios The main body of the paper presents results from quintile RNS-sorted portfolios. Here,
Option-Implied Correlations, Factor Models, and Market Risk
Option-Implied Correlations, Factor Models, and Market Risk Adrian Buss Lorenzo Schönleber Grigory Vilkov INSEAD Frankfurt School Frankfurt School of Finance & Management of Finance & Management 17th November
Volatility Appendix. B.1 Firm-Specific Uncertainty and Aggregate Volatility
B Volatility Appendix The aggregate volatility risk explanation of the turnover effect relies on three empirical facts. First, the explanation assumes that firm-specific uncertainty comoves with aggregate
Betting against Beta or Demand for Lottery
Turan G. Bali 1 Stephen J. Brown 2 Scott Murray 3 Yi Tang 4 1 McDonough School of Business, Georgetown University 2 Stern School of Business, New York University 3 College of Business Administration, University
A Lottery Demand-Based Explanation of the Beta Anomaly. Online Appendix
A Lottery Demand-Based Explanation of the Beta Anomaly Online Appendix Section I provides details of the calculation of the variables used in the paper. Section II examines the robustness of the beta anomaly.
The bottom-up beta of momentum
The bottom-up beta of momentum Pedro Barroso First version: September 2012 This version: November 2014 Abstract A direct measure of the cyclicality of momentum at a given point in time, its bottom-up beta
APPLYING MULTIVARIATE
Swiss Society for Financial Market Research (pp. 201 211) MOMTCHIL POJARLIEV AND WOLFGANG POLASEK APPLYING MULTIVARIATE TIME SERIES FORECASTS FOR ACTIVE PORTFOLIO MANAGEMENT Momtchil Pojarliev, INVESCO
Leverage and Asymmetric Volatility: The Firm Level Evidence
Leverage and Asymmetric Volatility: The Firm Level Evidence Presented by Stefano Mazzotta Kennesaw State University joint work with Jan Ericsson, McGill University and Xiao Huang, Kennesaw State University
Realized and Anticipated Macroeconomic Conditions Forecast Stock Returns
Realized and Anticipated Macroeconomic Conditions Forecast Stock Returns Alessandro Beber Michael W. Brandt Maurizio Luisi Cass Business School Fuqua School of Business Quantitative City University Duke
Empirics of the Oslo Stock Exchange:. Asset pricing results
Empirics of the Oslo Stock Exchange:. Asset pricing results. 1980 2016. Bernt Arne Ødegaard Jan 2017 Abstract We show the results of numerous asset pricing specifications on the crossection of assets at
Example 1 of econometric analysis: the Market Model
Example 1 of econometric analysis: the Market Model IGIDR, Bombay 14 November, 2008 The Market Model Investors want an equation predicting the return from investing in alternative securities. Return is
Does Idiosyncratic Volatility Proxy for Risk Exposure?
Does Idiosyncratic Volatility Proxy for Risk Exposure? Zhanhui Chen Nanyang Technological University Ralitsa Petkova Purdue University We thank Geert Bekaert (editor), two anonymous referees, and seminar
Estimating Risk-Return Relations with Price Targets
Estimating Risk-Return Relations with Price Targets Liuren Wu Baruch College March 29, 2016 Liuren Wu (Baruch) Equity risk premium March 29, 2916 1 / 13 Overview Asset pricing theories generate implications
The Effect of Kurtosis on the Cross-Section of Stock Returns
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2012 The Effect of Kurtosis on the Cross-Section of Stock Returns Abdullah Al Masud Utah State University
Does Idiosyncratic Volatility Proxy for Risk Exposure?
Does Idiosyncratic Volatility Proxy for Risk Exposure? Zhanhui Chen Nanyang Technological University Ralitsa Petkova Purdue University We decompose aggregate market variance into an average correlation
Cross-Sectional Dispersion and Expected Returns
Cross-Sectional Dispersion and Expected Returns Thanos Verousis a and Nikolaos Voukelatos b a Newcastle University Business School, Newcastle University b Kent Business School, University of Kent Abstract
Liquidity skewness premium
Liquidity skewness premium Giho Jeong, Jangkoo Kang, and Kyung Yoon Kwon * Abstract Risk-averse investors may dislike decrease of liquidity rather than increase of liquidity, and thus there can be asymmetric
The New Issues Puzzle
The New Issues Puzzle Professor B. Espen Eckbo Advanced Corporate Finance, 2009 Contents 1 IPO Sample and Issuer Characteristics 1 1.1 Annual Sample Distribution................... 1 1.2 IPO Firms are
Revisiting Idiosyncratic Volatility and Stock Returns. Fatma Sonmez 1
Revisiting Idiosyncratic Volatility and Stock Returns Fatma Sonmez 1 Abstract This paper s aim is to revisit the relation between idiosyncratic volatility and future stock returns. There are three key
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
ECO 317 Economics of Uncertainty Fall Term 2009 Tuesday October 6 Portfolio Allocation Mean-Variance Approach
ECO 317 Economics of Uncertainty Fall Term 2009 Tuesday October 6 ortfolio Allocation Mean-Variance Approach Validity of the Mean-Variance Approach Constant absolute risk aversion (CARA): u(w ) = exp(
Cross Sectional Asset Pricing Tests: Ex Ante versus Ex Post Approaches
Cross Sectional Asset Pricing Tests: Ex Ante versus Ex Post Approaches Mahmoud Botshekan Smurfit School of Business, University College Dublin, Ireland mahmoud.botshekan@ucd.ie, +353-1-716-8976 John Cotter
Long-run Consumption Risks in Assets Returns: Evidence from Economic Divisions
Long-run Consumption Risks in Assets Returns: Evidence from Economic Divisions Abdulrahman Alharbi 1 Abdullah Noman 2 Abstract: Bansal et al (2009) paper focus on measuring risk in consumption especially
Explaining individual firm credit default swap spreads with equity volatility and jump risks
Explaining individual firm credit default swap spreads with equity volatility and jump risks By Y B Zhang (Fitch), H Zhou (Federal Reserve Board) and H Zhu (BIS) Presenter: Kostas Tsatsaronis Bank for
FE670 Algorithmic Trading Strategies. Stevens Institute of Technology
FE670 Algorithmic Trading Strategies Lecture 4. Cross-Sectional Models and Trading Strategies Steve Yang Stevens Institute of Technology 09/26/2013 Outline 1 Cross-Sectional Methods for Evaluation of Factor
Predictability of Interest Rates and Interest-Rate Portfolios
Predictability of Interest Rates and Interest-Rate Portfolios Liuren Wu Zicklin School of Business, Baruch College Joint work with Turan Bali and Massoud Heidari July 7, 2007 The Bank of Canada - Rotman
Correlated Volatility Shocks
Correlated Volatility Shocks Xiao Qiao,a and Yongning Wang,b a SummerHaven Investment Management, LLC b Booth School of Business, University of Chicago January 10, 2017 Abstract Commonality in idiosyncratic
Common Risk Factors in the Cross-Section of Corporate Bond Returns
Common Risk Factors in the Cross-Section of Corporate Bond Returns Online Appendix Section A.1 discusses the results from orthogonalized risk characteristics. Section A.2 reports the results for the downside
Firm specific uncertainty around earnings announcements and the cross section of stock returns
Firm specific uncertainty around earnings announcements and the cross section of stock returns Sergey Gelman International College of Economics and Finance & Laboratory of Financial Economics Higher School
Volatility Forecasting in the 90-Day Australian Bank Bill Futures Market
Volatility Forecasting in the 90-Day Australian Bank Bill Futures Market Nathan K. Kelly a,, J. Scott Chaput b a Ernst & Young Auckland, New Zealand b Lecturer Department of Finance and Quantitative Analysis
Discount Rates. John H. Cochrane. January 8, University of Chicago Booth School of Business
Discount Rates John H. Cochrane University of Chicago Booth School of Business January 8, 2011 Discount rates 1. Facts: How risk discount rates vary over time and across assets. 2. Theory: Why discount
NHY examples. Bernt Arne Ødegaard. 23 November Estimating dividend growth in Norsk Hydro 8
NHY examples Bernt Arne Ødegaard 23 November 2017 Abstract Finance examples using equity data for Norsk Hydro (NHY) Contents 1 Calculating Beta 4 2 Cost of Capital 7 3 Estimating dividend growth in Norsk
The Pricing of Exchange Rates in Japan: The Cases of the Japanese Automobile Industry Firms after the US Lehman Shock
International Journal of Business and Management; Vol. 7, No. 24; 2012 ISSN 1833-3850 E-ISSN 1833-8119 Published by Canadian Center of Science and Education The Pricing of Exchange Rates in Japan: The
University of California Berkeley
University of California Berkeley A Comment on The Cross-Section of Volatility and Expected Returns : The Statistical Significance of FVIX is Driven by a Single Outlier Robert M. Anderson Stephen W. Bianchi
Common Macro Factors and Their Effects on U.S Stock Returns
2011 Common Macro Factors and Their Effects on U.S Stock Returns IBRAHIM CAN HALLAC 6/22/2011 Title: Common Macro Factors and Their Effects on U.S Stock Returns Name : Ibrahim Can Hallac ANR: 374842 Date
Empirical Asset Pricing for Tactical Asset Allocation
Introduction Process Model Conclusion Department of Finance The University of Connecticut School of Business stephen.r.rush@gmail.com May 10, 2012 Background Portfolio Managers Want to justify fees with
P s =(0,W 0 R) safe; P r =(W 0 σ,w 0 µ) risky; Beyond P r possible if leveraged borrowing OK Objective function Mean a (Std.Dev.
ECO 305 FALL 2003 December 2 ORTFOLIO CHOICE One Riskless, One Risky Asset Safe asset: gross return rate R (1 plus interest rate) Risky asset: random gross return rate r Mean µ = E[r] >R,Varianceσ 2 =
Idiosyncratic risk, insurance, and aggregate consumption dynamics: a likelihood perspective
Idiosyncratic risk, insurance, and aggregate consumption dynamics: a likelihood perspective Alisdair McKay Boston University June 2013 Microeconomic evidence on insurance - Consumption responds to idiosyncratic
Market Efficiency and Idiosyncratic Volatility in Vietnam
International Journal of Business and Management; Vol. 10, No. 6; 2015 ISSN 1833-3850 E-ISSN 1833-8119 Published by Canadian Center of Science and Education Market Efficiency and Idiosyncratic Volatility
Are the Fama-French Factors Proxying News Related to GDP Growth? The Australian Evidence
Are the Fama-French Factors Proxying News Related to GDP Growth? The Australian Evidence Annette Nguyen, Robert Faff and Philip Gharghori Department of Accounting and Finance, Monash University, VIC 3800,
The Myth of Long Horizon Predictability: An Asset Allocation Perspective.
The Myth of Long Horizon Predictability: An Asset Allocation Perspective. René Garcia a, Abraham Lioui b and Patrice Poncet c Preliminary and Incomplete Please do not quote without the authors permission.
Internet Appendix to Leverage Constraints and Asset Prices: Insights from Mutual Fund Risk Taking
Internet Appendix to Leverage Constraints and Asset Prices: Insights from Mutual Fund Risk Taking In this Internet Appendix, we provide further discussion and additional empirical results to evaluate robustness
The great moderation and the US external imbalance
The great moderation and the US external imbalance Alessandra Fogli 1 Fabrizio Perri 2 1 Minneapolis FED 2 University of Minnesota and Minneapolis FED SED Winter Meetings, 2008 1984 Conditional Standard
CHAPTER 10. Arbitrage Pricing Theory and Multifactor Models of Risk and Return INVESTMENTS BODIE, KANE, MARCUS
CHAPTER 10 Arbitrage Pricing Theory and Multifactor Models of Risk and Return McGraw-Hill/Irwin Copyright 2011 by The McGraw-Hill Companies, Inc. All rights reserved. 10-2 Single Factor Model Returns on
Dissecting the Market Pricing of Return Volatility
Dissecting the Market Pricing of Return Volatility Torben G. Andersen Kellogg School, Northwestern University, NBER and CREATES Oleg Bondarenko University of Illinois at Chicago Measuring Dependence in
Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles
: A Potential Resolution of Asset Pricing Puzzles, JF (2004) Presented by: Esben Hedegaard NYUStern October 12, 2009 Outline 1 Introduction 2 The Long-Run Risk Solving the 3 Data and Calibration Results
The Common Factor in Idiosyncratic Volatility:
The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications Bryan Kelly University of Chicago Booth School of Business (with Bernard Herskovic, Hanno Lustig, and Stijn Van Nieuwerburgh)
FUND OF HEDGE FUNDS DO THEY REALLY ADD VALUE?
FUND OF HEDGE FUNDS DO THEY REALLY ADD VALUE? Florian Albrecht, Jean-Francois Bacmann, Pierre Jeanneret & Stefan Scholz, RMF Investment Management Man Investments Hedge funds have attracted significant
Quantitative Portfolio Theory & Performance Analysis
550.447 Quantitative Portfolio Theory & Performance Analysis Week of April 15, 013 & Arbitrage-Free Pricing Theory (APT) Assignment For April 15 (This Week) Read: A&L, Chapter 5 & 6 Read: E&G Chapters
Return Reversals, Idiosyncratic Risk and Expected Returns
Return Reversals, Idiosyncratic Risk and Expected Returns Wei Huang, Qianqiu Liu, S.Ghon Rhee and Liang Zhang Shidler College of Business University of Hawaii at Manoa 2404 Maile Way Honolulu, Hawaii,
Oil Price Effects on Exchange Rate and Price Level: The Case of South Korea
Oil Price Effects on Exchange Rate and Price Level: The Case of South Korea Mirzosaid SULTONOV 東北公益文科大学総合研究論集第 34 号抜刷 2018 年 7 月 30 日発行 研究論文 Oil Price Effects on Exchange Rate and Price Level: The Case
4. EMPIRICAL RESULTS
4. EMPIRICAL RESULTS 4 DECRIPTIVE STATISTICS Table 4-1 provides descriptive statistics on the explanatory variables from regressions in which the dependent variable is the excess stock return. We report
Small Sample Performance of Instrumental Variables Probit Estimators: A Monte Carlo Investigation
Small Sample Performance of Instrumental Variables Probit : A Monte Carlo Investigation July 31, 2008 LIML Newey Small Sample Performance? Goals Equations Regressors and Errors Parameters Reduced Form
Risk-Based Investing & Asset Management Final Examination
Risk-Based Investing & Asset Management Final Examination Thierry Roncalli February 6 th 2015 Contents 1 Risk-based portfolios 2 2 Regularizing portfolio optimization 3 3 Smart beta 5 4 Factor investing
Interpreting factor models
Discussion of: Interpreting factor models by: Serhiy Kozak, Stefan Nagel and Shrihari Santosh Kent Daniel Columbia University, Graduate School of Business 2015 AFA Meetings 4 January, 2015 Paper Outline
On the robustness of the CAPM, Fama-French Three-Factor Model and the Carhart Four-Factor Model on the Dutch stock market.
Tilburg University 2014 Bachelor Thesis in Finance On the robustness of the CAPM, Fama-French Three-Factor Model and the Carhart Four-Factor Model on the Dutch stock market. Name: Humberto Levarht y Lopez
Smart Beta: Managing Diversification of Minimum Variance Portfolios
Smart Beta: Managing Diversification of Minimum Variance Portfolios Jean-Charles Richard and Thierry Roncalli Lyxor Asset Management 1, France University of Évry, France Risk Based and Factor Investing
Monetary policy perceptions and risk-adjusted returns: Have investors from G-7 countries benefitted?
Monetary policy perceptions and risk-adjusted returns: Have investors from G-7 countries benefitted? Abstract We examine the effect of the implied federal funds rate on several proxies for riskadjusted
Generalized Dynamic Factor Models and Volatilities: Recovering the Market Volatility Shocks
Generalized Dynamic Factor Models and Volatilities: Recovering the Market Volatility Shocks Paper by: Matteo Barigozzi and Marc Hallin Discussion by: Ross Askanazi March 27, 2015 Paper by: Matteo Barigozzi
Supervisor, Prof. Ph.D. Moisă ALTĂR. MSc. Student, Octavian ALEXANDRU
Supervisor, Prof. Ph.D. Moisă ALTĂR MSc. Student, Octavian ALEXANDRU Presentation structure Purpose of the paper Literature review Price simulations methodology Shock detection methodology Data description
Size Matters, if You Control Your Junk
Discussion of: Size Matters, if You Control Your Junk by: Cliff Asness, Andrea Frazzini, Ronen Israel, Tobias Moskowitz, and Lasse H. Pedersen Kent Daniel Columbia Business School & NBER AFA Meetings 7
Limiting Risk Exposure for Investments in Clean Energy Stocks
Msc Economics & Business Master Specialisation Financial Economics Limiting Risk Exposure for Investments in Clean Energy Stocks A study on the risk exposure of clean energy stocks, using oil as important
GGraph. Males Only. Premium. Experience. GGraph. Gender. 1 0: R 2 Linear = : R 2 Linear = Page 1
GGraph 9 Gender : R Linear =.43 : R Linear =.769 8 7 6 5 4 3 5 5 Males Only GGraph Page R Linear =.43 R Loess 9 8 7 6 5 4 5 5 Explore Case Processing Summary Cases Valid Missing Total N Percent N Percent
The Securities-Correlation Risks and the Volatility Effects in the Japanese Stock Market *
Policy Research Institute, Ministry of Finance, Japan, Public Policy Review, Vol.9, No.3, September 2013 531 The Securities-Correlation Risks and the Volatility Effects in the Japanese Stock Market * Chief
Does Idiosyncratic Risk Really Matter?
THE JOURNAL OF FINANCE VOL. LX, NO. 2 APRIL 2005 Does Idiosyncratic Risk Really Matter? TURAN G. BALI, NUSRET CAKICI, XUEMIN (STERLING) YAN, and ZHE ZHANG ABSTRACT Goyal and Santa-Clara (2003) find a significantly
Economic Review. Wenting Jiao * and Jean-Jacques Lilti
Jiao and Lilti China Finance and Economic Review (2017) 5:7 DOI 10.1186/s40589-017-0051-5 China Finance and Economic Review RESEARCH Open Access Whether profitability and investment factors have additional
Discussion: Disclosure, Volatility and Transparency
Discussion: Disclosure, Volatility and Transparency by Diana Hancock Board of Governors of the Federal Reserve System The views expressed are the views of the author and do not necessarily sarily reflect
1 Estimating risk factors for IBM - using data 95-06
1 Estimating risk factors for IBM - using data 95-06 Basic estimation of asset pricing models, using IBM returns data Market model r IBM = a + br m + ɛ CAPM Fama French 1.1 Using octave/matlab er IBM =
Asset-Specific and Systematic Liquidity on the Swedish Stock Market
Master Essay Asset-Specific and Systematic Liquidity on the Swedish Stock Market Supervisor: Hossein Asgharian Authors: Veronika Lunina Tetiana Dzhumurat 2010-06-04 Abstract This essay studies the effect
Equity Price Dynamics Before and After the Introduction of the Euro: A Note*
Equity Price Dynamics Before and After the Introduction of the Euro: A Note* Yin-Wong Cheung University of California, U.S.A. Frank Westermann University of Munich, Germany Daily data from the German and
Online Appendix. Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle
Online Appendix to accompany Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle by Robert F. Stambaugh, Jianfeng Yu, and Yu Yuan November 4, 2014 Contents Table AI: Idiosyncratic Volatility Effects
Liquidity Creation as Volatility Risk
Liquidity Creation as Volatility Risk Itamar Drechsler, NYU and NBER Alan Moreira, Rochester Alexi Savov, NYU and NBER JHU Carey Finance Conference June, 2018 1 Liquidity and Volatility 1. Liquidity creation
A Sensitivity Analysis between Common Risk Factors and Exchange Traded Funds
A Sensitivity Analysis between Common Risk Factors and Exchange Traded Funds Tahura Pervin Dept. of Humanities and Social Sciences, Dhaka University of Engineering & Technology (DUET), Gazipur, Bangladesh
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
Issues in Panel Data Model Selection: The Case of Empirical Analysis of Demand for Reinsurance
Issues in Panel Data Model Selection: The Case of Empirical Analysis of Demand for Reinsurance Augusto Carneiro and Prof Mike Sherris UNSW Actuarial Studies Research Symposium 2005, UNSW Sydney, AUSTRALIA
One-Factor Asset Pricing
One-Factor Asset Pricing with Stefanos Delikouras (University of Miami) Alex Kostakis MBS 12 January 217, WBS Alex Kostakis (MBS) One-Factor Asset Pricing 12 January 217, WBS 1 / 32 Presentation Outline
Optimal Debt-to-Equity Ratios and Stock Returns
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2014 Optimal Debt-to-Equity Ratios and Stock Returns Courtney D. Winn Utah State University Follow this
One-Factor Asset Pricing
One-Factor Asset Pricing with Stefanos Delikouras (University of Miami) Alex Kostakis Manchester June 2017, WFA (Whistler) Alex Kostakis (Manchester) One-Factor Asset Pricing June 2017, WFA (Whistler)
Internet Appendix for Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle *
Internet Appendix for Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle * ROBERT F. STAMBAUGH, JIANFENG YU, and YU YUAN * This appendix contains additional results not reported in the published
Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas Online Appendix
Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas Online Appendix Thomas Gilbert Christopher Hrdlicka Jonathan Kalodimos Stephan Siegel December 17, 2013 Abstract In this Online Appendix,
Adjusting discount rate for Uncertainty
Page 1 Adjusting discount rate for Uncertainty The Issue A simple approach: WACC Weighted average Cost of Capital A better approach: CAPM Capital Asset Pricing Model Massachusetts Institute of Technology
Income Inequality and Stock Pricing in the U.S. Market
Lawrence University Lux Lawrence University Honors Projects 5-29-2013 Income Inequality and Stock Pricing in the U.S. Market Minh T. Nguyen Lawrence University, mnguyenlu27@gmail.com Follow this and additional
Microéconomie de la finance
Microéconomie de la finance 7 e édition Christophe Boucher christophe.boucher@univ-lorraine.fr 1 Chapitre 6 7 e édition Les modèles d évaluation d actifs 2 Introduction The Single-Index Model - Simplifying
Value at Risk and Expected Stock Returns
Value at isk and Expected Stock eturns August 2003 Turan G. Bali Associate Professor of Finance Department of Economics & Finance Baruch College, Zicklin School of Business City University of New York
Problem Set 1 answers
Business 3595 John H. Cochrane Problem Set 1 answers 1. It s always a good idea to make sure numbers are reasonable. Notice how slow-moving DP is. In some sense we only realy have 3-4 data points, which
The Market Price of Risk of the Volatility Term Structure
The Market Price of Risk of the Volatility Term Structure George Dotsis Preliminary and Incomplete This Draft: 07/09/09 Abstract In this paper I examine the market price of risk of the volatility term
Tail Risk Premia and Predictability. Viktor Todorov Northwestern University
Tail Risk Premia and Predictability Viktor Todorov Northwestern University Joint work with Tim Bollerslev and Lai Xu January, 2014 Motivation Market Volatility changes over time this risk is significantly
John H. Cochrane. April University of Chicago Booth School of Business
Comments on "Volatility, the Macroeconomy and Asset Prices, by Ravi Bansal, Dana Kiku, Ivan Shaliastovich, and Amir Yaron, and An Intertemporal CAPM with Stochastic Volatility John Y. Campbell, Stefano
The Norwegian State Equity Ownership
The Norwegian State Equity Ownership B A Ødegaard 15 November 2018 Contents 1 Introduction 1 2 Doing a performance analysis 1 2.1 Using R....................................................................
CHAPTER 10. Arbitrage Pricing Theory and Multifactor Models of Risk and Return INVESTMENTS BODIE, KANE, MARCUS
CHAPTER 10 Arbitrage Pricing Theory and Multifactor Models of Risk and Return INVESTMENTS BODIE, KANE, MARCUS McGraw-Hill/Irwin Copyright 2011 by The McGraw-Hill Companies, Inc. All rights reserved. INVESTMENTS
Liquidity Creation as Volatility Risk
Liquidity Creation as Volatility Risk Itamar Drechsler Alan Moreira Alexi Savov Wharton Rochester NYU Chicago November 2018 1 Liquidity and Volatility 1. Liquidity creation - makes it cheaper to pledge
Risk Reward Optimisation for Long-Run Investors: an Empirical Analysis
GoBack Risk Reward Optimisation for Long-Run Investors: an Empirical Analysis M. Gilli University of Geneva and Swiss Finance Institute E. Schumann University of Geneva AFIR / LIFE Colloquium 2009 München,
Index Models and APT
Index Models and APT (Text reference: Chapter 8) Index models Parameter estimation Multifactor models Arbitrage Single factor APT Multifactor APT Index models predate CAPM, originally proposed as a simplification
Volatility Jump Risk in the Cross-Section of Stock Returns. Yu Li University of Houston. September 29, 2017
Volatility Jump Risk in the Cross-Section of Stock Returns Yu Li University of Houston September 29, 2017 Abstract Jumps in aggregate volatility has been established as an important factor affecting the
Stock Market Dispersion, the Business Cycle and Expected Factor Returns Timotheos Angelidis a,*, Athanasios Sakkas b and Nikolaos Tessaromatis c
Stock Market Dispersion, the Business Cycle and Expected Factor Returns Timotheos Angelidis a,*, Athanasios Sakkas b and Nikolaos Tessaromatis c a,* Department of Economics, University of Peloponnese,
Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk
THE JOURNAL OF FINANCE VOL. LVI, NO. 1 FEB. 2001 Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk JOHN Y. CAMPBELL, MARTIN LETTAU, BURTON G. MALKIEL, and YEXIAO
Supplementary Material and Data for Catering Through Nominal Share Prices. Malcolm Baker, Robin Greenwood, and Jeffrey Wurgler October 1, 2008
Supplementary Material and Data for Catering Through Nominal Share Prices Malcolm Baker, Robin Greenwood, and Jeffrey Wurgler October, 2008 Table A. Data table: Raw Market-to-Book Data. The market-to-book