A Report on Financial Market Volatility and Strategic Asset Allocation

This report delves into the intricacies of financial market volatility, employing the GARCH and EWMA models to understand asset price fluctuations. It explores the application of these models in both short-term and long-term contexts, demonstrating their relevance in forecasting and strategic asset allocation over a 5-10 year period. The report highlights the significance of macroeconomic data within the GARCH model and the simplistic nature of the EWMA model. It differentiates between short-term and long-term volatility through strategic asset allocation, emphasizing the models' statistical implications in analyzing stock variations within the financial market. The paper references key academic sources to support its analysis and findings, providing a comprehensive overview of volatility in financial markets and strategic asset allocation.

Running Head: Finance 1
Finance

Finance 2
Abstract:
The paper explores the aspect of volatility encountered in the financial markets and for this
purpose it took the relevance of the models like GARCH and EWMA to understand the
fluctuation level in the price of the assets in the market. The paper has also successfully shown
the aspect of forecasting by means of strategic asset allocation for a long term period of 5 to 10
years. It has taken the relevance of the secondary resources to have an insight of the level of
fluctuations that the financial market encounter across the longer and shorter duration of time.
The paper strive to address the research theme of the aspect of volatility experienced by the
financial assets in the market for different time horizons.
The paper takes relevance of the Generalised Autoregressive Conditional Heteroskedasticity
(GARCH) to understand the aspect of volatility experienced in the financial market. This model
is very much relevant in this case as it determines the irregularity showcasing the variation
experienced by the financial assets like stocks by means of a statistical model (Brown, Beekes, &
Verhoeven, 2011). GARCH model has been upheld in this case as it took consideration of the
macroeconomic data for evaluating the aspect of volatility encountered by the financial assets
like bonds, stocks and market indices. The model has been explained well by plotting the stock
returns at a uniformed manner before the financial crisis in 2007 but post-financial crisis scenario
shows a positive return ensuring continuance of the volatility moving forward (Rocheteau,
Wright, & Zhang, 2018).
The short-term volatility also takes relevance of Exponentially Weighted Moving Average
(EWMA) model taking specification of the preferred dataset. This particular model is the
simplistic extension of the standardised weighting scheme carrying equivalent weightage of the
financial assets like bonds and stocks for determining its volatility (Sonenshine, Larson, &
Cauvel, 2016). EWMA model has rightly taken relevance of the limitations of its statistical
implications like intercepts equivalent to zero and have the levelling parameter in tandem with
the autoregressive parameter to come up with the statistically tested model. The model
apparently strives for a thumb rule to determine the data frequency and estimate a fixed lambda
for the process (Rocheteau, Wright, & Zhang, 2018).
The paper upholds a distinction between the aspect of short-term volatility and the long-term
volatility shown by the strategic asset allocation strategy. This particular model is upheld for

Finance 3
setting allocation targets of the different assets classes like stocks and bonds and rebalance the
same periodically as per the nature or goal of the investment process (Brown, Beekes, &
Verhoeven, 2011). It is used for a longer duration as it deviates significantly off the initial
settings owing to variable returns from different classes of assets. The paper has its relevance in
showing a striking variation amidst the short-term variation and long-term variation of the stocks
using the respective models. Again it is unified in upholding the level of volatility experienced
by the stocks in the financial market as models like EWMA and GARCH adds a statistical
implication to it.

Finance 4
References
Brown, P., Beekes, W., & Verhoeven, P. (2011). Corporate governance, accounting and finance:
A review. Accounting & finance, 51(1), 96-172.
Hillier, D., Grinblatt, M., & Titman, S. (2011). Financial markets and corporate strategy (2nd
Eu ed.). London: McGraw Hill.
Rocheteau, G., Wright, R., & Zhang, C. (2018). Corporate finance and monetary policy.
American Economic Review, 108(4-5), 1147-86.
Sonenshine, R., Larson, N., & Cauvel, M. (2016). Determinants of CEO Compensation before
and after the Financial Crisis. Modern Economy, 7(12), 1455.