Integration Of Long-term Investment Portfolio Of A Variety Of Market Risk

The long term portfolio theory extends the classic one-period investment problem to the multi-period problem, and claims that the conclusions in long term and short term will be different. Under some restrictions, the long term and short term framework might come to the same conclusion. But if the conditions have been changed, there will be different consequence. The asset allocation puzzle discovered that the long term stock price risk might be smaller, since the stock price will have a mean reverting trend in the long run and the long term bond price will have some interest rate risk. Thus, we use the multi-period model to calculate the optimization investment strategy in the long run and find something that will influence the portfolio, such as preference, etc. After the theoretical conclusions we will do some empirical analysis or calculation, but the value of empirical studies will be limited by the China's data of stock and bond prices. Furthermore, when choosing the weight of the risky asset, the investment opportunity set will include diverse risks, including interest rate risk, labor income uncertainty, dynamic learning, event rick, etc, which will be discussed respectively in later chapters.Chapter 1 and Chapter 2 give a first sight on the reference research in the past and put forward the research interest and motivation of this dissertation, preparing for later modeling. Chapter 3 sets up the general equilibrium framework for studying the long term investor's strategy, without labor income uncertainty, facing some state variables or risks. Transferring some non-linear problem into linear problem, we find some obvious conclusions and find that the risk asset allocation can be divided into two parts. The first demand is the myopic demand which is the classic part against risk; the second part is the hedging demand which can be used to hedge different kinds of risks. This risk or state variables could be one or more than one (covariance should be considered). Then simple calculations find that the factors influencing the investment weight include the risk aversion and intertemporal substitution. Then we can use this framework to find the risk aversion of different funds and find their real implied risk aversion extent.Chapter 4 discusses the continuous circumstance instead of discrete one, and finds some obvious and neat conclusions, explaining the division of investment demand. Chapter 5 sets up the investment portfolio optimization model with labor income risk and analyses the influence on the investment strategy by some variables such as the labor income growth rate and its volatility, unemployment probability, risk asset growth rate and its volatility, relative risk aversion.Chapter 6 investigates the influences by dynamic learning process of long term investor on the investment portfolio. Since the return predictability will influence the typical investment strategy for the consideration of learning. The learning model includes the volatility estimation of the predicative variables and the covariance of stock return and this predicative variable. And the investment weight will decrease with the time horizon, which is different with the typical case. Chapter 7 discusses the event risk's influence on the portfolio. Since the investor cannot control the change of wealth due to the discontinuity and jump of price or volatility. The investor can be deemed as facing a liquidity constraint. Then the investment demand can be divided into two parts: the first myopic demand is similar to the classic case, the second hedging demand can be used to offset the effect of event jump risk or non-liquidity demand. Chapter 8 give some theoretical conclusion and summarizes some empirical or policy implications.