China s Model of Managing the Financial System

Transcription

1 China s Model of Managing the Financial System Markus Brunnermeier, Princeton University Michael Sockin, University of Texas, Austin Wei Xiong, Princeton University 2nd Annual Bank OF Canada-University of Toronto-Rotman Concerence on the Chinese Economy October 22, 2016

2 Motivation Growing concerns about nancial instability of China Chinese stock market: Turmoil in 2015 Chinese exchange market: Concerns about FX management Housing markets: Overheating Rising leverage across the nation China s nancial system: new conceptual framework needed China has a distinct economic model: two-track system state sector vs. private sector (planning mixed with market economy) nancial system serves mainly to fund the state sector Distinct institutional setting in the nancial markets Large population of inexperienced retail investors Heavy interventions by the government

3 Government s Paternalistic Philosophy Large price volatility in China s stock markets and heavy turnover highest turnover rate among major stock markets Asset prices often deviate from fundamentals large price di erentials between A-B and A-H stock pairs, e.g., Mei, Scheinkman and Xiong (2009) dramatic warrant bubble in , e.g., Xiong and Yu (2011) Large population of inexperienced retail investors retail investors hold 50% of tradable shares and contribute to 90% of trading volume CSRC s mission: protect retail investors and stabilize markets

4 Government nterventions in China s Financial System Counter-cyclical policies and regulations interest rate policy and bank reserve ratio policy since Nov 2014, interest rate was reduced 6 times and reserve ratio 5 times suspension and quota control of PO issuance stamp tax on stock trading mortgage rate and rst payment requirement... Public guidance by o cial media, such as People s Daily and Xinhua Press Direct trading in stock markets A national team was directed to bail out the stock market in summer 2015

5 Reserve Requirement Ratio in China Active monetary policy instrument: up 32 times, down 4 times from Powerful and direct impact on credit supply, money multiplier

6 PO ssuance in A-Share Markets The government (CSRC) directly controls PO issuance had suspended PO issuance 8 times quantity and allocation of quota

7 Stamp Tax in Stock Trading

8 Conceptual Questions Need a framework to analyze the e ects of government intervention in asset markets How would government intervention a ect market dynamics? How would market participants react to government intervention? trade along with or against the government? What is the right objective of government intervention? reduce price volatility or improve information e ciency? We develope a framework ntensive intervention makes government noise a pricing factor in asset prices and this factor gets further magni ed by market speculation Potential inconsistency: reducing price volatility and improving information e ciency

9 A Model with Perfect nformation n nitely many periods: t = 0, 1, 2... A risky asset, which pays a stream of dividends over time: D t = θ t + ε D t θ t is an exogenous fundamental variable: θ t = ρ θ θ t 1 + ε θ t Publicly observable will be made unobservable later to introduce information frictions and policy errors Government intervention does not directly a ect asset cash ow di erent from Pastor & Veronesi (2012) and Bond & Goldstein (2015), which focus on policy interventions that a ect cash ow

10 A Model with Perfect nformation Noise traders submit random market orders: N t = ρ N N t 1 + σ N ε N t Price insensitive orders, capturing unstable market forces Rational short-term investors each maximize myopic trading pro t: h i Ut i = max E exp γw i Xt i t+1 j θ t, N t with W i t+1 = Rf W + X i t R t+1 and R t+1 = D t+1 + P t+1 R f P t. Equilibrium without any government intervention: Z 1 0 X i t dt = N t

11 Market Breakdown Conjecture a linear equilibrium: P t = ρ θ R f ρ θ θ t + p N N t Optimal position of each myopic investor: h i Xt i = 1 E t D t+1 + P t+1 R f P t = 1 p N ρ N R f γ Var t [D t+1 + P t+1 ] γ σ 2 D + R f 2 N t ρ σ 2 θ θ + pn 2 σ2 N The market breaks down when R f σ N > σn = R f ρ r N. 2γ σ 2 D + R f 2 R f ρ σ 2 θ θ Short-term investors ine ectively in trading against noise trader risk, a la DSSW (1990)

12 Volatility Explosion

13 Government ntervention ntroduce a government which trades the asset X G t = ϑ N N t Again conjecture a linear equilibrium: P t = ρ θ R f ρ θ θ t + p N N t The market clearing R 1 0 xi t dt + X G t = N t implies the market breaks down only when σ N > 1 R f ρ N 1 ϑ N r 2γ i σ 2 D + R f R f ρ θ 2 σ 2 θ ϑ N > 0 mitigates the region of market failure and may prevent failure if su ciently large

14 Volatility Explosion

15 Government ntervention De ne the government objective: choose ϑ N to maximize γ σ Var [ P t (ϑ N ) ] γ θ Var h P t (ϑ N ) 1 R f ρ θ θ t+1 i ψvar [ϑ N N t ] Penalty for price volatility, penalty for price deviation from fundamental, and cost of trading Two possible objectives: reducing volatility and improving information e ciency often treated as equivalent reducing price volatility is more convenient and widely adopted in practice, e.g., in US monetary policy - Stein and Sundarem (2016) The government internalizes the market failure by taking a su ciently large ϑ N to prevent market breakdown

16 An Extended Model with nformation Frictions Suppose that θ t is unobservable The public market information set Ft M = σ fd s, P s g st ˆθ M h i t+1 = E θ t+1 j Ft M serves as the anchor of asset valuation nvestor i chooses a i t 2 f0, 1g to acquire private information: h i 1/2 h i 1/2 st i = θ t+1 + at i τ s ε s,i t or gt i = G t at i τ g ε g,i t F i t = F M t _ a i t si t + 1 a i t g i t His belief ˆθ i t+1 = E θ t+1 j F i t and myopic objecctive: U i t = max E at i 2f0,1g " max E Xt i h exp γwt+1 i j F i t i j F M t 1 #

17 An Extended Model with nformation Frictions The government has no private information and intervenes Xt G = ϑ ˆN q M ˆN t + Var ϑ ˆN M ˆN t j Ft M 1 Gt ˆN M t = E h N t j F M t i is the market perceived noise trading G t s N 0, σ 2 G is iid noise, caused by frictions or moral hazard more noise gets in when the government trades more intensively Gt is a pricing factor in asset prices, revealed at t but unobservable before t A myopic preference for trading: γ θ Var P t max ϑ N γ σ Var 1 ϑ ˆN h P t R f θ t+1 j Ft M 1 ρ θ i j F M t 1 ψvar ϑ ˆN h X G t j F M t 1 i

18 Noisy Rational Expectations Equilibrium h State vectore Ψ t = ˆθ M t+1 ˆN M t G t Ĝ M t+1 i nvestor optimization: at t, investor i chooses at i = a i (Ψ t 1 ) and trades X i Ψ t, at i si t + 1 at i g i t, P t Government optimization: at t, the government chooses ϑ ˆN. Market clearing: Z 1 0 X i Ψ t, a i ts i t + 1 at i gt i, P t di + X G (Ψ t ) = N t,

19 A Benchmark without Government ntervention The setting in each period is similar to Hellwig (1980) ˆθ M h i t = E θ t+1 j Ft M acts as the anchor of the market valuation Xt i linearly increases with si t Market clearing implies 1 P t = R f ˆθ M t+1 and decreases with P t ˆθ M t+1 + p θ θ t+1 ˆθ M t+1 + p N N t ρ θ asymmetric information makes the market easier to break down reducing volatility is consistent with improving information e ciency

20 Market Breakdown with nformation Frictions & No Government ntervention

21 Equilibria with Government ntervention G t and Ĝ M t+1 = E hg t+1 j F M t i enter the price A fundamental-centric equilibrium - all investors acquire information about θ t+1 1 P t = ˆθ M R f t+1 + p θ θ t+1 ˆθ M t+1 + p g G t + p N N t ρ θ A government-centric equilibrium - all investors acquire information about G t+1 1 P t = ˆθ M R f t+1 + pĝ Ĝt+1 M ρ + p G G t+1 Ĝt+1 M + p g G t + p N N t θ A mixed equilibrium - some investors on θ t+1 some on G t+1 1 P t = ˆθ M R f t+1 + pĝ Ĝt+1 M ρ + p θ θ t+1 ˆθ M t+1 θ +p G G t+1 Ĝt+1 M + p g G t + p N N t

22 nvestor nformation Acquisition Policy nvestor i chooses at i 2 f0, 1g to improve prediction of E R t+1 j Ft i Exponential utility =) minimize Var h R t+1 jft M, at i si t + 1 at i i g i t a i t = 1 if Cov[R t+1,g i t j F M t ] 2 Var[g i t j F M t ] a i t = 0 if Cov[R t+1,g i t j F M t ] 2 Var[g i t j F M t ] a i t = 0 or 1 otherwise > Cov[R t+1,s i t j F M t ] 2 Var[s i t j F M t ] < Cov[R t+1,s i t j F M t ] 2 Var[s i t j F M t ]

23 Numerical llustration Table : Baseline Model Parameters Government: γ σ = 1, γ θ = 0, ψ = 1, σ 2 G = 2 Asset Fundamental: ρ θ = 0.75, σ 2 θ = 0.01, σ2 D =.8 Noise Trading: ρ N = 0, σ 2 N = 0.2 nvestors: γ = 1, τ s = 500, τ g = 500, R f = 1.01

24 Market Equilibrium vs Noise Trading Volatility

25 The market shifts to the government-centric equilibrium as intervention intensi es volatility jumps down but price ine ciency jumps up inconsistency between reducing volatility and improving price e ciency The government trades less in the government-centric equilibrium investors private information about fundamental may cause them to trade against the government

26 Market Equilibrium vs ncentive to Reduce Volatility

27 Market Equilibrium vs ncentive to mprove Price Efficiency

28 Conclusion Unregulated market can be highly volatile and might break down, especially when noise trading risk is large ntuition: short-term investors ine ective in trading against noise traders Government intervention helps to stabilize the market Adverse e ects: Active government intervention renders noise in government trading a pricing factor intervention induces investors to speculate on government noise rather than fundamentals, which ampli es e ects of policy errors nconsistency between objectives of reducing price volatility and improving information e ciency

29 Risks in China s Financial System Commonly concerned risks Noise trader risk created by inexperienced retail investors Rising leverage across the nation Overheating housing markets Another risk: policy errors magni ed by nancial market speculation China s model of transforming the real economy crossing the river by touching the stone This approach may not work for reforming the nancial system highly demanding on regulator expertise a nancial policy error may be immediately ampli ed by market speculation, leading to violent market uctuations the stock market turmoil in summer 2015 the breakdown of the circuit breaker in January 2016 the exchange rate crash in August 2015