Equivalent definitions

Equality

For all ${\displaystyle t\in \{0,1,...,T-1\}:\rho _{t+1}(X)=\rho _{t+1}(Y)\Rightarrow \rho _{t}(X)=\rho _{t}(Y)}$

Recursive

For all ${\displaystyle t\in \{0,1,...,T-1\}:\rho _{t}(X)=\rho _{t}(-\rho _{t+1}(X))}$

Acceptance Set

For all ${\displaystyle t\in \{0,1,...,T-1\}:A_{t}=A_{t,t+1}+A_{t+1}}$ where ${\displaystyle A_{t}}$ is the time ${\displaystyle t}$ acceptance set and ${\displaystyle A_{t,t+1}=A_{t}\cap L^{p}({\mathcal {F}}_{t+1})}$^[2]

Cocycle condition (for convex risk measures)

For all ${\displaystyle t\in \{0,1,...,T-1\}:\alpha _{t}(Q)=\alpha _{t,t+1}(Q)+\mathbb {E} ^{Q}[\alpha _{t+1}(Q)\mid {\mathcal {F}}_{t}]}$ where ${\displaystyle \alpha _{t}(Q)=\operatorname {*} {esssup}_{X\in A_{t}}\mathbb {E} ^{Q}[-X\mid {\mathcal {F}}_{t}]}$ is the minimal penalty function (where ${\displaystyle A_{t}}$ is an acceptance set and ${\displaystyle \operatorname {*} {esssup}}$ denotes the essential supremum) at time ${\displaystyle t}$ and ${\displaystyle \alpha _{t,t+1}(Q)=\operatorname {*} {esssup}_{X\in A_{t,t+1}}\mathbb {E} ^{Q}[-X\mid {\mathcal {F}}_{t}]}$.^[3]

Value at risk and average value at risk

Both dynamic value at risk and dynamic average value at risk are not a time consistent risk measures.

Time consistent alternative

The time consistent alternative to the dynamic average value at risk with parameter ${\displaystyle \alpha _{t}}$ at time t is defined by

${\displaystyle \rho _{t}(X)={\text{ess}}\sup _{Q\in {\mathcal {Q}}}E^{Q}[-X|{\mathcal {F}}_{t}]}$

such that ${\displaystyle {\mathcal {Q}}=\left\{Q\in {\mathcal {M}}_{1}:E\left[{\frac {dQ}{dP}}|{\mathcal {F}}_{j}\right]\leq \alpha _{j-1}E\left[{\frac {dQ}{dP}}|{\mathcal {F}}_{j-1}\right]\forall j=1,...,T\right\}}$.^[4]

Dynamic superhedging price

The dynamic superhedging price is a time consistent risk measure.^[5]

Dynamic entropic risk

The dynamic entropic risk measure is a time consistent risk measure if the risk aversion parameter is constant.^[5]

Continuous time

In continuous time, a time consistent coherent risk measure can be given by:

${\displaystyle \rho _{g}(X):=\mathbb {E} ^{g}[-X]}$

for a sublinear choice of function ${\displaystyle g}$ where ${\displaystyle \mathbb {E} ^{g}}$ denotes a g-expectation. If the function ${\displaystyle g}$ is convex, then the corresponding risk measure is convex.^[6]