• Like
Spending and Investment for Shortfall-Averse Endowments
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Spending and Investment for Shortfall-Averse Endowments

  • 166 views
Published

 

Published in Economy & Finance , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
166
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
1
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Model Solution Under the Hood Spending and Investment for Shortfall Averse Endowments Paolo Guasoni1,2 Gur Huberman3 Dan Ren4 Boston University1 Dublin City University2 Columbia Business School3 University of Dayton4 Financial and Actuarial Mathematics Seminar University of Michigan October 31st , 2013
  • 2. Model Solution Under the Hood Tobin (1974) • "The trustees of an endowed institution are the guardians of the future against the claims of the present. • "Their task is to preserve equity among generations. • "The trustees of an endowed university like my own assume the institution to be immortal. • "They want to know, therefore, the rate of consumption from endowment which can be sustained indefinitely. • "Sustainable consumption is their conception of permanent endowment income. • "In formal terms, the trustees are supposed to have a zero subjective rate of time preference."
  • 3. Model Solution Under the Hood Tobin (1974) • "The trustees of an endowed institution are the guardians of the future against the claims of the present. • "Their task is to preserve equity among generations. • "The trustees of an endowed university like my own assume the institution to be immortal. • "They want to know, therefore, the rate of consumption from endowment which can be sustained indefinitely. • "Sustainable consumption is their conception of permanent endowment income. • "In formal terms, the trustees are supposed to have a zero subjective rate of time preference."
  • 4. Model Solution Under the Hood Tobin (1974) • "The trustees of an endowed institution are the guardians of the future against the claims of the present. • "Their task is to preserve equity among generations. • "The trustees of an endowed university like my own assume the institution to be immortal. • "They want to know, therefore, the rate of consumption from endowment which can be sustained indefinitely. • "Sustainable consumption is their conception of permanent endowment income. • "In formal terms, the trustees are supposed to have a zero subjective rate of time preference."
  • 5. Model Solution Under the Hood Tobin (1974) • "The trustees of an endowed institution are the guardians of the future against the claims of the present. • "Their task is to preserve equity among generations. • "The trustees of an endowed university like my own assume the institution to be immortal. • "They want to know, therefore, the rate of consumption from endowment which can be sustained indefinitely. • "Sustainable consumption is their conception of permanent endowment income. • "In formal terms, the trustees are supposed to have a zero subjective rate of time preference."
  • 6. Model Solution Under the Hood Tobin (1974) • "The trustees of an endowed institution are the guardians of the future against the claims of the present. • "Their task is to preserve equity among generations. • "The trustees of an endowed university like my own assume the institution to be immortal. • "They want to know, therefore, the rate of consumption from endowment which can be sustained indefinitely. • "Sustainable consumption is their conception of permanent endowment income. • "In formal terms, the trustees are supposed to have a zero subjective rate of time preference."
  • 7. Model Solution Under the Hood Tobin (1974) • "The trustees of an endowed institution are the guardians of the future against the claims of the present. • "Their task is to preserve equity among generations. • "The trustees of an endowed university like my own assume the institution to be immortal. • "They want to know, therefore, the rate of consumption from endowment which can be sustained indefinitely. • "Sustainable consumption is their conception of permanent endowment income. • "In formal terms, the trustees are supposed to have a zero subjective rate of time preference."
  • 8. Model Solution Under the Hood Outline • Motivation: Endowment Management. Universities, sovereign funds, trust funds. Retirement planning? • Model: Constant investment opportunities. Constant Relative Risk and Shortfall Aversion. • Result: Optimal spending and investment.
  • 9. Model Solution Under the Hood Outline • Motivation: Endowment Management. Universities, sovereign funds, trust funds. Retirement planning? • Model: Constant investment opportunities. Constant Relative Risk and Shortfall Aversion. • Result: Optimal spending and investment.
  • 10. Model Solution Under the Hood Outline • Motivation: Endowment Management. Universities, sovereign funds, trust funds. Retirement planning? • Model: Constant investment opportunities. Constant Relative Risk and Shortfall Aversion. • Result: Optimal spending and investment.
  • 11. Model Solution Under the Hood Endowment Management • How to invest? How much to withdraw? • Major goal: keep spending level. • Minor goal: increasing it. • Increasing and then decreasing spending is worse than not increasing in the first place. • How to capture this feature? • Heuristic rule among endowments and private foundations: spend a constant proportion of the moving average of assets.
  • 12. Model Solution Under the Hood Endowment Management • How to invest? How much to withdraw? • Major goal: keep spending level. • Minor goal: increasing it. • Increasing and then decreasing spending is worse than not increasing in the first place. • How to capture this feature? • Heuristic rule among endowments and private foundations: spend a constant proportion of the moving average of assets.
  • 13. Model Solution Under the Hood Endowment Management • How to invest? How much to withdraw? • Major goal: keep spending level. • Minor goal: increasing it. • Increasing and then decreasing spending is worse than not increasing in the first place. • How to capture this feature? • Heuristic rule among endowments and private foundations: spend a constant proportion of the moving average of assets.
  • 14. Model Solution Under the Hood Endowment Management • How to invest? How much to withdraw? • Major goal: keep spending level. • Minor goal: increasing it. • Increasing and then decreasing spending is worse than not increasing in the first place. • How to capture this feature? • Heuristic rule among endowments and private foundations: spend a constant proportion of the moving average of assets.
  • 15. Model Solution Under the Hood Endowment Management • How to invest? How much to withdraw? • Major goal: keep spending level. • Minor goal: increasing it. • Increasing and then decreasing spending is worse than not increasing in the first place. • How to capture this feature? • Heuristic rule among endowments and private foundations: spend a constant proportion of the moving average of assets.
  • 16. Model Solution Under the Hood Endowment Management • How to invest? How much to withdraw? • Major goal: keep spending level. • Minor goal: increasing it. • Increasing and then decreasing spending is worse than not increasing in the first place. • How to capture this feature? • Heuristic rule among endowments and private foundations: spend a constant proportion of the moving average of assets.
  • 17. Model Solution Under the Hood Theory • Classical Merton model: maximize ∞ e−βt E 0 ct1−γ dt 1−γ over spending rate ct and wealth in stocks Ht . • Optimal ct and Ht are constant fractions of current wealth Xt : c 1 1 = β+ 1− X γ γ r+ µ2 2γσ 2 H µ = X γσ 2 with interest rate r , equity premium µ, and stocks volatility σ. • Spending as volatile as wealth. • Want stable spending? Hold nearly no stocks. But then ct ≈ rXt . • Not easy with interest rates near zero. Not used by financial planners.
  • 18. Model Solution Under the Hood Theory • Classical Merton model: maximize ∞ e−βt E 0 ct1−γ dt 1−γ over spending rate ct and wealth in stocks Ht . • Optimal ct and Ht are constant fractions of current wealth Xt : c 1 1 = β+ 1− X γ γ r+ µ2 2γσ 2 H µ = X γσ 2 with interest rate r , equity premium µ, and stocks volatility σ. • Spending as volatile as wealth. • Want stable spending? Hold nearly no stocks. But then ct ≈ rXt . • Not easy with interest rates near zero. Not used by financial planners.
  • 19. Model Solution Under the Hood Theory • Classical Merton model: maximize ∞ e−βt E 0 ct1−γ dt 1−γ over spending rate ct and wealth in stocks Ht . • Optimal ct and Ht are constant fractions of current wealth Xt : c 1 1 = β+ 1− X γ γ r+ µ2 2γσ 2 H µ = X γσ 2 with interest rate r , equity premium µ, and stocks volatility σ. • Spending as volatile as wealth. • Want stable spending? Hold nearly no stocks. But then ct ≈ rXt . • Not easy with interest rates near zero. Not used by financial planners.
  • 20. Model Solution Under the Hood Theory • Classical Merton model: maximize ∞ e−βt E 0 ct1−γ dt 1−γ over spending rate ct and wealth in stocks Ht . • Optimal ct and Ht are constant fractions of current wealth Xt : c 1 1 = β+ 1− X γ γ r+ µ2 2γσ 2 H µ = X γσ 2 with interest rate r , equity premium µ, and stocks volatility σ. • Spending as volatile as wealth. • Want stable spending? Hold nearly no stocks. But then ct ≈ rXt . • Not easy with interest rates near zero. Not used by financial planners.
  • 21. Model Solution Under the Hood Theory • Classical Merton model: maximize ∞ e−βt E 0 ct1−γ dt 1−γ over spending rate ct and wealth in stocks Ht . • Optimal ct and Ht are constant fractions of current wealth Xt : c 1 1 = β+ 1− X γ γ r+ µ2 2γσ 2 H µ = X γσ 2 with interest rate r , equity premium µ, and stocks volatility σ. • Spending as volatile as wealth. • Want stable spending? Hold nearly no stocks. But then ct ≈ rXt . • Not easy with interest rates near zero. Not used by financial planners.
  • 22. Model Solution Under the Hood The Four-Percent Rule • Bengen (1994). Popular with financial planners. • When you retire, spend in the first year 4% of your savings. Keep same amount for future years. Invest 50% to 75% in stocks. • Historically, savings will last at least 30 years. • Inconsistent with theory. Spending-wealth ratio variable, but portfolio weight fixed. Bankruptcy possible. • Time-inconsistent. With equal initial capital, consume more if retired in Dec 2007 than if retired in Dec 2008. But savings will always be lower. • How to embed preference for stable spending in objective?
  • 23. Model Solution Under the Hood The Four-Percent Rule • Bengen (1994). Popular with financial planners. • When you retire, spend in the first year 4% of your savings. Keep same amount for future years. Invest 50% to 75% in stocks. • Historically, savings will last at least 30 years. • Inconsistent with theory. Spending-wealth ratio variable, but portfolio weight fixed. Bankruptcy possible. • Time-inconsistent. With equal initial capital, consume more if retired in Dec 2007 than if retired in Dec 2008. But savings will always be lower. • How to embed preference for stable spending in objective?
  • 24. Model Solution Under the Hood The Four-Percent Rule • Bengen (1994). Popular with financial planners. • When you retire, spend in the first year 4% of your savings. Keep same amount for future years. Invest 50% to 75% in stocks. • Historically, savings will last at least 30 years. • Inconsistent with theory. Spending-wealth ratio variable, but portfolio weight fixed. Bankruptcy possible. • Time-inconsistent. With equal initial capital, consume more if retired in Dec 2007 than if retired in Dec 2008. But savings will always be lower. • How to embed preference for stable spending in objective?
  • 25. Model Solution Under the Hood The Four-Percent Rule • Bengen (1994). Popular with financial planners. • When you retire, spend in the first year 4% of your savings. Keep same amount for future years. Invest 50% to 75% in stocks. • Historically, savings will last at least 30 years. • Inconsistent with theory. Spending-wealth ratio variable, but portfolio weight fixed. Bankruptcy possible. • Time-inconsistent. With equal initial capital, consume more if retired in Dec 2007 than if retired in Dec 2008. But savings will always be lower. • How to embed preference for stable spending in objective?
  • 26. Model Solution Under the Hood The Four-Percent Rule • Bengen (1994). Popular with financial planners. • When you retire, spend in the first year 4% of your savings. Keep same amount for future years. Invest 50% to 75% in stocks. • Historically, savings will last at least 30 years. • Inconsistent with theory. Spending-wealth ratio variable, but portfolio weight fixed. Bankruptcy possible. • Time-inconsistent. With equal initial capital, consume more if retired in Dec 2007 than if retired in Dec 2008. But savings will always be lower. • How to embed preference for stable spending in objective?
  • 27. Model Solution Under the Hood The Four-Percent Rule • Bengen (1994). Popular with financial planners. • When you retire, spend in the first year 4% of your savings. Keep same amount for future years. Invest 50% to 75% in stocks. • Historically, savings will last at least 30 years. • Inconsistent with theory. Spending-wealth ratio variable, but portfolio weight fixed. Bankruptcy possible. • Time-inconsistent. With equal initial capital, consume more if retired in Dec 2007 than if retired in Dec 2008. But savings will always be lower. • How to embed preference for stable spending in objective?
  • 28. Model Solution Under the Hood Related Literature • Consumption ratcheting: allow only increasing spending rates... Dybvig (1995), Bayraktar and Young (2008), Riedel (2009). • ...or force maximum drawdown on consumption from its peak. Thillaisundaram (2012). • Consume less than interest, or bankruptcy possible. No solvency with zero interest. • Habit formation: utility from consumption minus habit. Sundaresan (1989), Constantinides (1990), Detemple and Zapatero (1992), Campbell and Cochrane (1999), Detemple and Karatzas (2003). ∞ e−βt max E 0 (ct − xt )1−γ dt 1−γ where dxt = (bct − axt )dt • Infinite marginal utility at habit level. Like zero consumption. No solution if habit too high relative to wealth (x0 > X0 (r + a − b)). • Habit transitory. Time heals.
  • 29. Model Solution Under the Hood Related Literature • Consumption ratcheting: allow only increasing spending rates... Dybvig (1995), Bayraktar and Young (2008), Riedel (2009). • ...or force maximum drawdown on consumption from its peak. Thillaisundaram (2012). • Consume less than interest, or bankruptcy possible. No solvency with zero interest. • Habit formation: utility from consumption minus habit. Sundaresan (1989), Constantinides (1990), Detemple and Zapatero (1992), Campbell and Cochrane (1999), Detemple and Karatzas (2003). ∞ e−βt max E 0 (ct − xt )1−γ dt 1−γ where dxt = (bct − axt )dt • Infinite marginal utility at habit level. Like zero consumption. No solution if habit too high relative to wealth (x0 > X0 (r + a − b)). • Habit transitory. Time heals.
  • 30. Model Solution Under the Hood Related Literature • Consumption ratcheting: allow only increasing spending rates... Dybvig (1995), Bayraktar and Young (2008), Riedel (2009). • ...or force maximum drawdown on consumption from its peak. Thillaisundaram (2012). • Consume less than interest, or bankruptcy possible. No solvency with zero interest. • Habit formation: utility from consumption minus habit. Sundaresan (1989), Constantinides (1990), Detemple and Zapatero (1992), Campbell and Cochrane (1999), Detemple and Karatzas (2003). ∞ e−βt max E 0 (ct − xt )1−γ dt 1−γ where dxt = (bct − axt )dt • Infinite marginal utility at habit level. Like zero consumption. No solution if habit too high relative to wealth (x0 > X0 (r + a − b)). • Habit transitory. Time heals.
  • 31. Model Solution Under the Hood Related Literature • Consumption ratcheting: allow only increasing spending rates... Dybvig (1995), Bayraktar and Young (2008), Riedel (2009). • ...or force maximum drawdown on consumption from its peak. Thillaisundaram (2012). • Consume less than interest, or bankruptcy possible. No solvency with zero interest. • Habit formation: utility from consumption minus habit. Sundaresan (1989), Constantinides (1990), Detemple and Zapatero (1992), Campbell and Cochrane (1999), Detemple and Karatzas (2003). ∞ e−βt max E 0 (ct − xt )1−γ dt 1−γ where dxt = (bct − axt )dt • Infinite marginal utility at habit level. Like zero consumption. No solution if habit too high relative to wealth (x0 > X0 (r + a − b)). • Habit transitory. Time heals.
  • 32. Model Solution Under the Hood Related Literature • Consumption ratcheting: allow only increasing spending rates... Dybvig (1995), Bayraktar and Young (2008), Riedel (2009). • ...or force maximum drawdown on consumption from its peak. Thillaisundaram (2012). • Consume less than interest, or bankruptcy possible. No solvency with zero interest. • Habit formation: utility from consumption minus habit. Sundaresan (1989), Constantinides (1990), Detemple and Zapatero (1992), Campbell and Cochrane (1999), Detemple and Karatzas (2003). ∞ e−βt max E 0 (ct − xt )1−γ dt 1−γ where dxt = (bct − axt )dt • Infinite marginal utility at habit level. Like zero consumption. No solution if habit too high relative to wealth (x0 > X0 (r + a − b)). • Habit transitory. Time heals.
  • 33. Model Solution Under the Hood Related Literature • Consumption ratcheting: allow only increasing spending rates... Dybvig (1995), Bayraktar and Young (2008), Riedel (2009). • ...or force maximum drawdown on consumption from its peak. Thillaisundaram (2012). • Consume less than interest, or bankruptcy possible. No solvency with zero interest. • Habit formation: utility from consumption minus habit. Sundaresan (1989), Constantinides (1990), Detemple and Zapatero (1992), Campbell and Cochrane (1999), Detemple and Karatzas (2003). ∞ e−βt max E 0 (ct − xt )1−γ dt 1−γ where dxt = (bct − axt )dt • Infinite marginal utility at habit level. Like zero consumption. No solution if habit too high relative to wealth (x0 > X0 (r + a − b)). • Habit transitory. Time heals.
  • 34. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 35. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 36. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 37. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 38. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 39. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 40. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 41. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 42. Model Solution Under the Hood This Paper • Inputs • Risky asset follows geometric Brownian motion. Constant interest rate. • Constant relative risk aversion. Constant relative shortfall aversion by new parameter α. • Outputs • Spending constant between endogenous boundaries, bliss and gloom. Increases in small amounts at bliss. Declines smoothly at gloom. • Portfolio weight varies between high and low bounds. • Features • Solution solvent for any initial capital. Even with zero interest. • Spending target permanent. You never forget.
  • 43. Model Solution Under the Hood Shortfall Aversion and Prospect Theory • Kanheman and Tversky (1979,1991). Precursor: Markowitz (1952). • Reference dependence: utility is from gains and losses relative to reference level. • Loss Aversion: marginal utility lower for gains than for losses. • Prospect theory focuses on wealth gambles. • We bring these features to spending instead. Shortfall aversion as losses aversion for spending.
  • 44. Model Solution Under the Hood Shortfall Aversion and Prospect Theory • Kanheman and Tversky (1979,1991). Precursor: Markowitz (1952). • Reference dependence: utility is from gains and losses relative to reference level. • Loss Aversion: marginal utility lower for gains than for losses. • Prospect theory focuses on wealth gambles. • We bring these features to spending instead. Shortfall aversion as losses aversion for spending.
  • 45. Model Solution Under the Hood Shortfall Aversion and Prospect Theory • Kanheman and Tversky (1979,1991). Precursor: Markowitz (1952). • Reference dependence: utility is from gains and losses relative to reference level. • Loss Aversion: marginal utility lower for gains than for losses. • Prospect theory focuses on wealth gambles. • We bring these features to spending instead. Shortfall aversion as losses aversion for spending.
  • 46. Model Solution Under the Hood Shortfall Aversion and Prospect Theory • Kanheman and Tversky (1979,1991). Precursor: Markowitz (1952). • Reference dependence: utility is from gains and losses relative to reference level. • Loss Aversion: marginal utility lower for gains than for losses. • Prospect theory focuses on wealth gambles. • We bring these features to spending instead. Shortfall aversion as losses aversion for spending.
  • 47. Model Solution Under the Hood Shortfall Aversion and Prospect Theory • Kanheman and Tversky (1979,1991). Precursor: Markowitz (1952). • Reference dependence: utility is from gains and losses relative to reference level. • Loss Aversion: marginal utility lower for gains than for losses. • Prospect theory focuses on wealth gambles. • We bring these features to spending instead. Shortfall aversion as losses aversion for spending.
  • 48. Model Solution Under the Hood Model • Safe rate r . Risky asset price follows geometric Brownian motion: dSt = µdt + σdWt St for Brownian Motion (Wt )t≥0 with natural filtration (Ft )t≥0 . • Utility from consumption rate ct : ∞ E 0 1−γ (ct /htα ) 1−γ dt where ht = sup cs 0≤s≤t • Risk aversion γ > 1 to make the problem well posed. • α ∈ [0, 1) relative loss aversion. Strict monotonicity in c implies that α < 1 and strict concavity that α ≥ 0. • If you could forget the past you would be happier. But you can’t. • More is better today... but makes less worse tomorrow. • No loss aversion with ct increasing... or decreasing.
  • 49. Model Solution Under the Hood Model • Safe rate r . Risky asset price follows geometric Brownian motion: dSt = µdt + σdWt St for Brownian Motion (Wt )t≥0 with natural filtration (Ft )t≥0 . • Utility from consumption rate ct : ∞ E 0 1−γ (ct /htα ) 1−γ dt where ht = sup cs 0≤s≤t • Risk aversion γ > 1 to make the problem well posed. • α ∈ [0, 1) relative loss aversion. Strict monotonicity in c implies that α < 1 and strict concavity that α ≥ 0. • If you could forget the past you would be happier. But you can’t. • More is better today... but makes less worse tomorrow. • No loss aversion with ct increasing... or decreasing.
  • 50. Model Solution Under the Hood Model • Safe rate r . Risky asset price follows geometric Brownian motion: dSt = µdt + σdWt St for Brownian Motion (Wt )t≥0 with natural filtration (Ft )t≥0 . • Utility from consumption rate ct : ∞ E 0 1−γ (ct /htα ) 1−γ dt where ht = sup cs 0≤s≤t • Risk aversion γ > 1 to make the problem well posed. • α ∈ [0, 1) relative loss aversion. Strict monotonicity in c implies that α < 1 and strict concavity that α ≥ 0. • If you could forget the past you would be happier. But you can’t. • More is better today... but makes less worse tomorrow. • No loss aversion with ct increasing... or decreasing.
  • 51. Model Solution Under the Hood Model • Safe rate r . Risky asset price follows geometric Brownian motion: dSt = µdt + σdWt St for Brownian Motion (Wt )t≥0 with natural filtration (Ft )t≥0 . • Utility from consumption rate ct : ∞ E 0 1−γ (ct /htα ) 1−γ dt where ht = sup cs 0≤s≤t • Risk aversion γ > 1 to make the problem well posed. • α ∈ [0, 1) relative loss aversion. Strict monotonicity in c implies that α < 1 and strict concavity that α ≥ 0. • If you could forget the past you would be happier. But you can’t. • More is better today... but makes less worse tomorrow. • No loss aversion with ct increasing... or decreasing.
  • 52. Model Solution Under the Hood Model • Safe rate r . Risky asset price follows geometric Brownian motion: dSt = µdt + σdWt St for Brownian Motion (Wt )t≥0 with natural filtration (Ft )t≥0 . • Utility from consumption rate ct : ∞ E 0 1−γ (ct /htα ) 1−γ dt where ht = sup cs 0≤s≤t • Risk aversion γ > 1 to make the problem well posed. • α ∈ [0, 1) relative loss aversion. Strict monotonicity in c implies that α < 1 and strict concavity that α ≥ 0. • If you could forget the past you would be happier. But you can’t. • More is better today... but makes less worse tomorrow. • No loss aversion with ct increasing... or decreasing.
  • 53. Model Solution Under the Hood Model • Safe rate r . Risky asset price follows geometric Brownian motion: dSt = µdt + σdWt St for Brownian Motion (Wt )t≥0 with natural filtration (Ft )t≥0 . • Utility from consumption rate ct : ∞ E 0 1−γ (ct /htα ) 1−γ dt where ht = sup cs 0≤s≤t • Risk aversion γ > 1 to make the problem well posed. • α ∈ [0, 1) relative loss aversion. Strict monotonicity in c implies that α < 1 and strict concavity that α ≥ 0. • If you could forget the past you would be happier. But you can’t. • More is better today... but makes less worse tomorrow. • No loss aversion with ct increasing... or decreasing.
  • 54. Model Solution Under the Hood Model • Safe rate r . Risky asset price follows geometric Brownian motion: dSt = µdt + σdWt St for Brownian Motion (Wt )t≥0 with natural filtration (Ft )t≥0 . • Utility from consumption rate ct : ∞ E 0 1−γ (ct /htα ) 1−γ dt where ht = sup cs 0≤s≤t • Risk aversion γ > 1 to make the problem well posed. • α ∈ [0, 1) relative loss aversion. Strict monotonicity in c implies that α < 1 and strict concavity that α ≥ 0. • If you could forget the past you would be happier. But you can’t. • More is better today... but makes less worse tomorrow. • No loss aversion with ct increasing... or decreasing.
  • 55. Model Solution Under the Hood Sliding Kink • Because ct ≤ ht , utility effectively has a kink at ht : U c, h h c • Optimality: marginal utilities of spending and wealth equal. • Spending ht optimal for marginal utility of wealth between left and right derivative at kink. • State variable: ratio ht /Xt between reference spending and wealth. • Investor rich when ht /Xt low, and poor when ht /Xt high.
  • 56. Model Solution Under the Hood Sliding Kink • Because ct ≤ ht , utility effectively has a kink at ht : U c, h h c • Optimality: marginal utilities of spending and wealth equal. • Spending ht optimal for marginal utility of wealth between left and right derivative at kink. • State variable: ratio ht /Xt between reference spending and wealth. • Investor rich when ht /Xt low, and poor when ht /Xt high.
  • 57. Model Solution Under the Hood Sliding Kink • Because ct ≤ ht , utility effectively has a kink at ht : U c, h h c • Optimality: marginal utilities of spending and wealth equal. • Spending ht optimal for marginal utility of wealth between left and right derivative at kink. • State variable: ratio ht /Xt between reference spending and wealth. • Investor rich when ht /Xt low, and poor when ht /Xt high.
  • 58. Model Solution Under the Hood Sliding Kink • Because ct ≤ ht , utility effectively has a kink at ht : U c, h h c • Optimality: marginal utilities of spending and wealth equal. • Spending ht optimal for marginal utility of wealth between left and right derivative at kink. • State variable: ratio ht /Xt between reference spending and wealth. • Investor rich when ht /Xt low, and poor when ht /Xt high.
  • 59. Model Solution Under the Hood Sliding Kink • Because ct ≤ ht , utility effectively has a kink at ht : U c, h h c • Optimality: marginal utilities of spending and wealth equal. • Spending ht optimal for marginal utility of wealth between left and right derivative at kink. • State variable: ratio ht /Xt between reference spending and wealth. • Investor rich when ht /Xt low, and poor when ht /Xt high.
  • 60. Model Solution Under the Hood Control Argument • Value function V (x, h) depends on wealth x = Xt and target h = ht . • Set Jt = t 0 U(cs , hs )ds + V (Xt , ht ). By Itô’s formula: dJt = L(Xt , πt , ct , ht )dt + Vh (Xt , ht )dht + Vx (Xt , ht )Xt πt σ dWt where drift L(Xt , πt , ct , ht ) equals U(ct , ht ) + (Xt rt − ct + Xt πt µ)Vx (Xt , ht ) + Vxx (Xt , ht ) 2 Xt πt Σπt 2 ˆ • Jt supermartingale for any c, and martingale for optimizer c . Hence: max(sup L(x, π, c, h), Vh (x, h)) = 0 π,c • That is, either supπ,c L(x, π, c, h) = 0, or Vh (x, h) = 0. V • Optimal portfolio has usual expression π = − xVx Σ−1 µ. ˆ xx • Free-boundaries: When do you increase spending? When do you cut it below ht ?
  • 61. Model Solution Under the Hood Control Argument • Value function V (x, h) depends on wealth x = Xt and target h = ht . • Set Jt = t 0 U(cs , hs )ds + V (Xt , ht ). By Itô’s formula: dJt = L(Xt , πt , ct , ht )dt + Vh (Xt , ht )dht + Vx (Xt , ht )Xt πt σ dWt where drift L(Xt , πt , ct , ht ) equals U(ct , ht ) + (Xt rt − ct + Xt πt µ)Vx (Xt , ht ) + Vxx (Xt , ht ) 2 Xt πt Σπt 2 ˆ • Jt supermartingale for any c, and martingale for optimizer c . Hence: max(sup L(x, π, c, h), Vh (x, h)) = 0 π,c • That is, either supπ,c L(x, π, c, h) = 0, or Vh (x, h) = 0. V • Optimal portfolio has usual expression π = − xVx Σ−1 µ. ˆ xx • Free-boundaries: When do you increase spending? When do you cut it below ht ?
  • 62. Model Solution Under the Hood Control Argument • Value function V (x, h) depends on wealth x = Xt and target h = ht . • Set Jt = t 0 U(cs , hs )ds + V (Xt , ht ). By Itô’s formula: dJt = L(Xt , πt , ct , ht )dt + Vh (Xt , ht )dht + Vx (Xt , ht )Xt πt σ dWt where drift L(Xt , πt , ct , ht ) equals U(ct , ht ) + (Xt rt − ct + Xt πt µ)Vx (Xt , ht ) + Vxx (Xt , ht ) 2 Xt πt Σπt 2 ˆ • Jt supermartingale for any c, and martingale for optimizer c . Hence: max(sup L(x, π, c, h), Vh (x, h)) = 0 π,c • That is, either supπ,c L(x, π, c, h) = 0, or Vh (x, h) = 0. V • Optimal portfolio has usual expression π = − xVx Σ−1 µ. ˆ xx • Free-boundaries: When do you increase spending? When do you cut it below ht ?
  • 63. Model Solution Under the Hood Control Argument • Value function V (x, h) depends on wealth x = Xt and target h = ht . • Set Jt = t 0 U(cs , hs )ds + V (Xt , ht ). By Itô’s formula: dJt = L(Xt , πt , ct , ht )dt + Vh (Xt , ht )dht + Vx (Xt , ht )Xt πt σ dWt where drift L(Xt , πt , ct , ht ) equals U(ct , ht ) + (Xt rt − ct + Xt πt µ)Vx (Xt , ht ) + Vxx (Xt , ht ) 2 Xt πt Σπt 2 ˆ • Jt supermartingale for any c, and martingale for optimizer c . Hence: max(sup L(x, π, c, h), Vh (x, h)) = 0 π,c • That is, either supπ,c L(x, π, c, h) = 0, or Vh (x, h) = 0. V • Optimal portfolio has usual expression π = − xVx Σ−1 µ. ˆ xx • Free-boundaries: When do you increase spending? When do you cut it below ht ?
  • 64. Model Solution Under the Hood Control Argument • Value function V (x, h) depends on wealth x = Xt and target h = ht . • Set Jt = t 0 U(cs , hs )ds + V (Xt , ht ). By Itô’s formula: dJt = L(Xt , πt , ct , ht )dt + Vh (Xt , ht )dht + Vx (Xt , ht )Xt πt σ dWt where drift L(Xt , πt , ct , ht ) equals U(ct , ht ) + (Xt rt − ct + Xt πt µ)Vx (Xt , ht ) + Vxx (Xt , ht ) 2 Xt πt Σπt 2 ˆ • Jt supermartingale for any c, and martingale for optimizer c . Hence: max(sup L(x, π, c, h), Vh (x, h)) = 0 π,c • That is, either supπ,c L(x, π, c, h) = 0, or Vh (x, h) = 0. V • Optimal portfolio has usual expression π = − xVx Σ−1 µ. ˆ xx • Free-boundaries: When do you increase spending? When do you cut it below ht ?
  • 65. Model Solution Under the Hood Control Argument • Value function V (x, h) depends on wealth x = Xt and target h = ht . • Set Jt = t 0 U(cs , hs )ds + V (Xt , ht ). By Itô’s formula: dJt = L(Xt , πt , ct , ht )dt + Vh (Xt , ht )dht + Vx (Xt , ht )Xt πt σ dWt where drift L(Xt , πt , ct , ht ) equals U(ct , ht ) + (Xt rt − ct + Xt πt µ)Vx (Xt , ht ) + Vxx (Xt , ht ) 2 Xt πt Σπt 2 ˆ • Jt supermartingale for any c, and martingale for optimizer c . Hence: max(sup L(x, π, c, h), Vh (x, h)) = 0 π,c • That is, either supπ,c L(x, π, c, h) = 0, or Vh (x, h) = 0. V • Optimal portfolio has usual expression π = − xVx Σ−1 µ. ˆ xx • Free-boundaries: When do you increase spending? When do you cut it below ht ?
  • 66. Model Solution Under the Hood Duality ˜ • Setting U(y , h) = supc≥0 [U(c, h) − cy ], HJB equation becomes V 2 (x, h) ˜ U(Vx , h) + xrVx (x, h) − x µ Σ−1 µ = 0 2Vxx (x, h) ˜ • Nonlinear equation. Linear in dual V (y , h) = supx≥0 [V (x, h) − xy ] µ Σ−1 µ 2 ˜ ˜ ˜ U(y , h) − ry Vy + y Vyy = 0 2 • Plug U(c, h) = (c/hα )1−γ . 1−γ Kink spawns two cases:  ∗  h1−γ  − hy ˜ U(y , h) = 1 − γ  (yhα )1−1/γ  1−1/γ where γ ∗ = α + (1 − α)γ. ∗ if (1 − α)h−γ ≤ y ≤ h−γ if y > h−γ ∗ ∗
  • 67. Model Solution Under the Hood Duality ˜ • Setting U(y , h) = supc≥0 [U(c, h) − cy ], HJB equation becomes V 2 (x, h) ˜ U(Vx , h) + xrVx (x, h) − x µ Σ−1 µ = 0 2Vxx (x, h) ˜ • Nonlinear equation. Linear in dual V (y , h) = supx≥0 [V (x, h) − xy ] µ Σ−1 µ 2 ˜ ˜ ˜ U(y , h) − ry Vy + y Vyy = 0 2 • Plug U(c, h) = (c/hα )1−γ . 1−γ Kink spawns two cases:  ∗  h1−γ  − hy ˜ U(y , h) = 1 − γ  (yhα )1−1/γ  1−1/γ where γ ∗ = α + (1 − α)γ. ∗ if (1 − α)h−γ ≤ y ≤ h−γ if y > h−γ ∗ ∗
  • 68. Model Solution Under the Hood Duality ˜ • Setting U(y , h) = supc≥0 [U(c, h) − cy ], HJB equation becomes V 2 (x, h) ˜ U(Vx , h) + xrVx (x, h) − x µ Σ−1 µ = 0 2Vxx (x, h) ˜ • Nonlinear equation. Linear in dual V (y , h) = supx≥0 [V (x, h) − xy ] µ Σ−1 µ 2 ˜ ˜ ˜ U(y , h) − ry Vy + y Vyy = 0 2 • Plug U(c, h) = (c/hα )1−γ . 1−γ Kink spawns two cases:  ∗  h1−γ  − hy ˜ U(y , h) = 1 − γ  (yhα )1−1/γ  1−1/γ where γ ∗ = α + (1 − α)γ. ∗ if (1 − α)h−γ ≤ y ≤ h−γ if y > h−γ ∗ ∗
  • 69. Model Solution Under the Hood Homogeneity ∗ ∗ ˜ • V (λx, λh) = λ1−γ V (x, h) implies that V (y , h) = h1−γ q(z), where γ∗ z = yh . • HJB equation reduces to: µ Σ−1 µ 2 z q 2 (z) − rzq (z) = z− 1 1−γ 1−1/γ z 1−1/γ 1−α≤z ≤1 z>1 • Condition Vh (x, h) = 0 holds when desired spending must increase: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0 for z ≤ 1 − α • Agent “rich” for z ∈ [1 − α, 1]. Consumes at desired level, and increases it at bliss point 1 − α. • Agent becomes “poor” at gloom point 1. For z > 1, consumes below desired level.
  • 70. Model Solution Under the Hood Homogeneity ∗ ∗ ˜ • V (λx, λh) = λ1−γ V (x, h) implies that V (y , h) = h1−γ q(z), where γ∗ z = yh . • HJB equation reduces to: µ Σ−1 µ 2 z q 2 (z) − rzq (z) = z− 1 1−γ 1−1/γ z 1−1/γ 1−α≤z ≤1 z>1 • Condition Vh (x, h) = 0 holds when desired spending must increase: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0 for z ≤ 1 − α • Agent “rich” for z ∈ [1 − α, 1]. Consumes at desired level, and increases it at bliss point 1 − α. • Agent becomes “poor” at gloom point 1. For z > 1, consumes below desired level.
  • 71. Model Solution Under the Hood Homogeneity ∗ ∗ ˜ • V (λx, λh) = λ1−γ V (x, h) implies that V (y , h) = h1−γ q(z), where γ∗ z = yh . • HJB equation reduces to: µ Σ−1 µ 2 z q 2 (z) − rzq (z) = z− 1 1−γ 1−1/γ z 1−1/γ 1−α≤z ≤1 z>1 • Condition Vh (x, h) = 0 holds when desired spending must increase: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0 for z ≤ 1 − α • Agent “rich” for z ∈ [1 − α, 1]. Consumes at desired level, and increases it at bliss point 1 − α. • Agent becomes “poor” at gloom point 1. For z > 1, consumes below desired level.
  • 72. Model Solution Under the Hood Homogeneity ∗ ∗ ˜ • V (λx, λh) = λ1−γ V (x, h) implies that V (y , h) = h1−γ q(z), where γ∗ z = yh . • HJB equation reduces to: µ Σ−1 µ 2 z q 2 (z) − rzq (z) = z− 1 1−γ 1−1/γ z 1−1/γ 1−α≤z ≤1 z>1 • Condition Vh (x, h) = 0 holds when desired spending must increase: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0 for z ≤ 1 − α • Agent “rich” for z ∈ [1 − α, 1]. Consumes at desired level, and increases it at bliss point 1 − α. • Agent becomes “poor” at gloom point 1. For z > 1, consumes below desired level.
  • 73. Model Solution Under the Hood Homogeneity ∗ ∗ ˜ • V (λx, λh) = λ1−γ V (x, h) implies that V (y , h) = h1−γ q(z), where γ∗ z = yh . • HJB equation reduces to: µ Σ−1 µ 2 z q 2 (z) − rzq (z) = z− 1 1−γ 1−1/γ z 1−1/γ 1−α≤z ≤1 z>1 • Condition Vh (x, h) = 0 holds when desired spending must increase: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0 for z ≤ 1 − α • Agent “rich” for z ∈ [1 − α, 1]. Consumes at desired level, and increases it at bliss point 1 − α. • Agent becomes “poor” at gloom point 1. For z > 1, consumes below desired level.
  • 74. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 75. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 76. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 77. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 78. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 79. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 80. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 81. Model Solution Under the Hood Solving it • For r = 0, q(z) has solution: q(z) = C21 + C22 z C31 + 1+ µ 2r Σ−1 µ − z r + 2 log z (1−γ)(2r +µ Σ−1 µ) γ 1−1/γ (1−γ)δ0 z if 0 < z ≤ 1, if z > 1, where δα defined shortly. • Neumann condition at z = 1 − α: (1 − γ ∗ )q(z) + γ ∗ zq (z) = 0. • Value matching at z = 1: q(z−) = q(z+). • Smooth-pasting at z = 1: q (z−) = q (z+). • Fourth condition? • Intuitively, it should be at z = ∞. • Marginal utility z infinite when wealth x is zero. • Since q (z) = −x/h, the condition is limz→∞ q (z) = 0.
  • 82. Model Solution Under the Hood Main Quantities • Ratio between safe rate and half squared Sharpe ratio: ρ= 2r (µ/σ)2 In practice, ρ is small ≈ 8%. • Gloom ratio g (as wealth/target). 1/g coincides with Merton ratio: 1 = g 1− 1 γ r+ µ2 2γσ 2 • Bliss ratio: b=g (γ − 1)(1 − α)ρ+1 + (γρ + 1)(α(γ − 1)(ρ + 1) − γ(ρ + 1) + 1) (α − 1)γ 2 ρ(ρ + 1) • For ρ small, limit: g ≈ b 1−α+ α γ2 − 1 γ (1 1−α 1 − γ )(1 − α) log(1 − α) Ratio insensitive to market parameters, for typical investment opportunities.
  • 83. Model Solution Under the Hood Main Quantities • Ratio between safe rate and half squared Sharpe ratio: ρ= 2r (µ/σ)2 In practice, ρ is small ≈ 8%. • Gloom ratio g (as wealth/target). 1/g coincides with Merton ratio: 1 = g 1− 1 γ r+ µ2 2γσ 2 • Bliss ratio: b=g (γ − 1)(1 − α)ρ+1 + (γρ + 1)(α(γ − 1)(ρ + 1) − γ(ρ + 1) + 1) (α − 1)γ 2 ρ(ρ + 1) • For ρ small, limit: g ≈ b 1−α+ α γ2 − 1 γ (1 1−α 1 − γ )(1 − α) log(1 − α) Ratio insensitive to market parameters, for typical investment opportunities.
  • 84. Model Solution Under the Hood Main Quantities • Ratio between safe rate and half squared Sharpe ratio: ρ= 2r (µ/σ)2 In practice, ρ is small ≈ 8%. • Gloom ratio g (as wealth/target). 1/g coincides with Merton ratio: 1 = g 1− 1 γ r+ µ2 2γσ 2 • Bliss ratio: b=g (γ − 1)(1 − α)ρ+1 + (γρ + 1)(α(γ − 1)(ρ + 1) − γ(ρ + 1) + 1) (α − 1)γ 2 ρ(ρ + 1) • For ρ small, limit: g ≈ b 1−α+ α γ2 − 1 γ (1 1−α 1 − γ )(1 − α) log(1 − α) Ratio insensitive to market parameters, for typical investment opportunities.
  • 85. Model Solution Under the Hood Main Quantities • Ratio between safe rate and half squared Sharpe ratio: ρ= 2r (µ/σ)2 In practice, ρ is small ≈ 8%. • Gloom ratio g (as wealth/target). 1/g coincides with Merton ratio: 1 = g 1− 1 γ r+ µ2 2γσ 2 • Bliss ratio: b=g (γ − 1)(1 − α)ρ+1 + (γρ + 1)(α(γ − 1)(ρ + 1) − γ(ρ + 1) + 1) (α − 1)γ 2 ρ(ρ + 1) • For ρ small, limit: g ≈ b 1−α+ α γ2 − 1 γ (1 1−α 1 − γ )(1 − α) log(1 − α) Ratio insensitive to market parameters, for typical investment opportunities.
  • 86. Model Solution Under the Hood Main Result Theorem For r = 0, the optimal spending policy is:  Xt /b if ht ≤ Xt /b  ˆ ct = ht if Xt /b ≤ ht ≤ Xt /g   Xt /g if ht ≥ Xt /g (1) The optimal weight of the risky asset is the Merton risky asset weight when the wealth to target ratio is lower than the gloom point, Xt /ht ≤ g. Otherwise, i.e., when Xt /ht ≥ g the weight of the risky asset is πt = ˆ ρ γρ + (γ − 1)z ρ+1 + 1 µ (γρ + 1)(ρ + (γ − 1)(ρ + 1)z) − (γ − 1)z ρ+1 σ 2 (2) where the variable z satisfies the equation: (γρ + 1)(ρ + (γ − 1)(ρ + 1)z) − (γ − 1)z ρ+1 x = (γ − 1)(ρ + 1)rz(γρ + 1) h (3)
  • 87. Model Solution Under the Hood Bliss and Gloom – α 2.0 gloom Habit Wealth Ratio 1.5 bliss 1.0 0.5 0.0 0.0 0.2 0.4 0.6 Loss Aversion µ/σ = 0.4, γ = 2, r = 1% 0.8 1.0
  • 88. Model Solution Under the Hood Bliss and Gloom • Gloom ratio independent of the shortfall aversion α, and its inverse equals the Merton consumption rate. • Bliss ratio increases as the shortfall aversion α increases. • Within the target region, the optimal investment policy is independent of the shortfall aversion α. • At α = 0 the model degenerates to the Merton model and b = g, i.e., the bliss and the gloom points coincide. At α = 1 the bliss point is infinity, i.e., shortfall aversion is so strong that the solution calls for no spending increases at all.
  • 89. Model Solution Under the Hood Bliss and Gloom • Gloom ratio independent of the shortfall aversion α, and its inverse equals the Merton consumption rate. • Bliss ratio increases as the shortfall aversion α increases. • Within the target region, the optimal investment policy is independent of the shortfall aversion α. • At α = 0 the model degenerates to the Merton model and b = g, i.e., the bliss and the gloom points coincide. At α = 1 the bliss point is infinity, i.e., shortfall aversion is so strong that the solution calls for no spending increases at all.
  • 90. Model Solution Under the Hood Bliss and Gloom • Gloom ratio independent of the shortfall aversion α, and its inverse equals the Merton consumption rate. • Bliss ratio increases as the shortfall aversion α increases. • Within the target region, the optimal investment policy is independent of the shortfall aversion α. • At α = 0 the model degenerates to the Merton model and b = g, i.e., the bliss and the gloom points coincide. At α = 1 the bliss point is infinity, i.e., shortfall aversion is so strong that the solution calls for no spending increases at all.
  • 91. Model Solution Under the Hood Bliss and Gloom • Gloom ratio independent of the shortfall aversion α, and its inverse equals the Merton consumption rate. • Bliss ratio increases as the shortfall aversion α increases. • Within the target region, the optimal investment policy is independent of the shortfall aversion α. • At α = 0 the model degenerates to the Merton model and b = g, i.e., the bliss and the gloom points coincide. At α = 1 the bliss point is infinity, i.e., shortfall aversion is so strong that the solution calls for no spending increases at all.
  • 92. Model Solution Under the Hood Spending and Investment as Target/Wealth Varies 5 110 105 100 3 95 90 Portfolio Weight Spending•Wealth 4 2 85 1 1.0 1.5 Bliss 2.0 Gloom 2.5 80 3.0 Wealth•Target µ/σ = 0.4, γ = 2, r = 1%
  • 93. Model Solution Under the Hood Spending and Investment as Wealth/Target Varies 5 110 100 90 3 80 70 Portfolio Weight Spending•Wealth 4 2 60 1 40 Gloom 45 50 55 60Bliss 50 65 Wealth•Target µ/
  • 94. Model Solution Under the Hood Investment – α 140 Portfolio 130 120 a = 0.5 a = 0.75 a = .25 110 100 a=0 90 20 40 60 Wealth•Target 80 100
  • 95. Model Solution Under the Hood Steady State Theorem • The long-run average time spent in the target zone is a fraction 1 − (1 − α)1+ρ of the total time. This fraction is approximately α because reasonable values of ρ are close to zero. • Starting from a point z0 ∈ [0, 1] in the target region, the expected time before reaching gloom is  Ex,h [τgloom ] = ρ log(z0 ) − (ρ + 1)r ρ+1 (1 − α)−ρ−1 z0 − 1 ρ+1 In particular, starting from bliss (z0 = 1 − α) and for small ρ, Ex,h [τgloom ] = ρ r α + log(1 − α) + O(ρ2 ) 1−α  
  • 96. Model Solution Under the Hood Steady State Theorem • The long-run average time spent in the target zone is a fraction 1 − (1 − α)1+ρ of the total time. This fraction is approximately α because reasonable values of ρ are close to zero. • Starting from a point z0 ∈ [0, 1] in the target region, the expected time before reaching gloom is  Ex,h [τgloom ] = ρ log(z0 ) − (ρ + 1)r ρ+1 (1 − α)−ρ−1 z0 − 1 ρ+1 In particular, starting from bliss (z0 = 1 − α) and for small ρ, Ex,h [τgloom ] = ρ r α + log(1 − α) + O(ρ2 ) 1−α  
  • 97. Model Solution Under the Hood Steady State Theorem • The long-run average time spent in the target zone is a fraction 1 − (1 − α)1+ρ of the total time. This fraction is approximately α because reasonable values of ρ are close to zero. • Starting from a point z0 ∈ [0, 1] in the target region, the expected time before reaching gloom is  Ex,h [τgloom ] = ρ log(z0 ) − (ρ + 1)r ρ+1 (1 − α)−ρ−1 z0 − 1 ρ+1 In particular, starting from bliss (z0 = 1 − α) and for small ρ, Ex,h [τgloom ] = ρ r α + log(1 − α) + O(ρ2 ) 1−α  
  • 98. Model Solution Under the Hood Time Spent at Target – α Portion of Constant Consumption 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 Loss Aversion µ/σ = 0.35, γ = 2, r = 1% 0.8 1.0
  • 99. Model Solution Under the Hood Time from Bliss to Gloom – α Expected Hitting Time at Gloom 120 100 80 60 40 20 0 0.0 0.2 0.4 0.6 Loss Aversion µ/σ = 0.35, γ = 2, r = 1% 0.8 1.0
  • 100. Model Solution Under the Hood Under the Hood • With ∞ 0 U(ct , ht )dt, first-order condition is: Uc (ct , ht ) = yMt where Mt = e−(r +µ Σ−1 µ/2)t−µ Σ−1 Wt is stochastic discount factor. −1 • Candidate ct = I(yMt , ht ) with I(y , h) = Uc (y , h). But what is ht ? • ht increases only at bliss. And at bliss Uc independent of past maximum: −1/γ ht = c0 ∨ y −1/γ inf Ms s≤t
  • 101. Model Solution Under the Hood Under the Hood • With ∞ 0 U(ct , ht )dt, first-order condition is: Uc (ct , ht ) = yMt where Mt = e−(r +µ Σ−1 µ/2)t−µ Σ−1 Wt is stochastic discount factor. −1 • Candidate ct = I(yMt , ht ) with I(y , h) = Uc (y , h). But what is ht ? • ht increases only at bliss. And at bliss Uc independent of past maximum: −1/γ ht = c0 ∨ y −1/γ inf Ms s≤t
  • 102. Model Solution Under the Hood Under the Hood • With ∞ 0 U(ct , ht )dt, first-order condition is: Uc (ct , ht ) = yMt where Mt = e−(r +µ Σ−1 µ/2)t−µ Σ−1 Wt is stochastic discount factor. −1 • Candidate ct = I(yMt , ht ) with I(y , h) = Uc (y , h). But what is ht ? • ht increases only at bliss. And at bliss Uc independent of past maximum: −1/γ ht = c0 ∨ y −1/γ inf Ms s≤t
  • 103. Model Solution Under the Hood Duality Bound • For any spending plan ct : ∞ E 0 (ct /ht α )1−γ x 1−γ dt ≤ E 1−γ 1−γ ∞ 0 1−1/γ Z∗ t ˜ u 1−1/γ Zt Z∗t γ dt γ ˜ ˜ where Zt = Mt and Z∗t = infs≤t Zs , and U(y , h) = − h 1−1/γ u (yh ). • Show that equality holds for candidate optimizer. Lemma ˜ ˆ For γ > 1, limT ↑∞ Ex,h V (yMT , ht (y )) = 0 for all y ≥ 0. • Estimates on Brownian motion and its running maximum.
  • 104. Model Solution Under the Hood Duality Bound • For any spending plan ct : ∞ E 0 (ct /ht α )1−γ x 1−γ dt ≤ E 1−γ 1−γ ∞ 0 1−1/γ Z∗ t ˜ u 1−1/γ Zt Z∗t γ dt γ ˜ ˜ where Zt = Mt and Z∗t = infs≤t Zs , and U(y , h) = − h 1−1/γ u (yh ). • Show that equality holds for candidate optimizer. Lemma ˜ ˆ For γ > 1, limT ↑∞ Ex,h V (yMT , ht (y )) = 0 for all y ≥ 0. • Estimates on Brownian motion and its running maximum.
  • 105. Model Solution Under the Hood Duality Bound • For any spending plan ct : ∞ E 0 (ct /ht α )1−γ x 1−γ dt ≤ E 1−γ 1−γ ∞ 0 1−1/γ Z∗ t ˜ u 1−1/γ Zt Z∗t γ dt γ ˜ ˜ where Zt = Mt and Z∗t = infs≤t Zs , and U(y , h) = − h 1−1/γ u (yh ). • Show that equality holds for candidate optimizer. Lemma ˜ ˆ For γ > 1, limT ↑∞ Ex,h V (yMT , ht (y )) = 0 for all y ≥ 0. • Estimates on Brownian motion and its running maximum.
  • 106. Model Solution Under the Hood Duality Bound • For any spending plan ct : ∞ E 0 (ct /ht α )1−γ x 1−γ dt ≤ E 1−γ 1−γ ∞ 0 1−1/γ Z∗ t ˜ u 1−1/γ Zt Z∗t γ dt γ ˜ ˜ where Zt = Mt and Z∗t = infs≤t Zs , and U(y , h) = − h 1−1/γ u (yh ). • Show that equality holds for candidate optimizer. Lemma ˜ ˆ For γ > 1, limT ↑∞ Ex,h V (yMT , ht (y )) = 0 for all y ≥ 0. • Estimates on Brownian motion and its running maximum.
  • 107. Model Solution Thank You! Under the Hood