Implementing Quantitative Risk Management and VaR in a Chinese Investment Bank Case Solution
Jasper Wang – the protagonist had an extensive experience in effective management or risk exposure in the international financial-institutions. With speedy progress in the Chinese economy;Jasper wanted to return back to China,to exploit the opportunity & he was also convinced by the Chief Executive Officer of Guang Guo – one of the domestic investment banks of China, to lead the risk management functions. He had intended to introduce the international standards in the risk management as well as measurementin the firm. He was concerned about how he would push more quantitative framework for risk and control in the face of domain issues, dismissal of risk management measures used outside of China as well as the basis differences in culture regarding how things get done within the domestic organization.
Why does Jasper choose to make the VaR the first step towards rationalizing the trading function? What is the appeal of the VaR model generally?
Jasper Wang took the role of creating the formal risk management functions in the Shanghai-based Guang Guo Investment bank. He was intrigued by the likelihood of building the risk management functions of Guang Guo, with the ability to influence the strategic direction of the company as a whole.
Over the period of time; Jasper came to realize that with the advent of new investment opportunities, followed the economic boom in China, which made traders less interested in continuing their commitments with the trading strategies of the bank, due to which he had proposed the idea of implementing the simple trading limits as well as daily reporting of profit and loss, but his ideas was not given enough importance by Jianguo Lu, who was in charge of asset allocation as well as the trading strategy. Additionally, he was struck by visible disconnect between the enthusiasm of CEO and the lack of concern of operating and trading team of the company in effective management of risk. Due to this reason, he contemplated to make the statistical Value at Risk (VaR) framework one of the key centerpiece of his efforts.
He made Value at Risk (VaR) the first step of rationalizing the function of trading, because of the reason that the staff and the traders didn’t stress over the significance of formal risk management system. Also, Jianguo Lu also opposed the idea presented by Jasper, by stating that no formal risk management system could replace his years of experience and expertise in the Chinese market, and he also stipulated that no statistical model could be successful in capturing the Shanghai equity markets, which were dominated by the speculative retailinvestors who were highly prone to uncertain fluctuations. Also, he analyzed that no one at Guang Guo stressed over the significance of analyzing the range of the possible outcomes of the activities and make comparison of profit and loss from specific type of trades to the capital that was invested at risk and no one was evaluating the risk adjusted returns on the activities, which were helpful in evaluating the performance of the fund or asset class and tended to help make better investment decisions.
Furthermore, his purpose of introducing the Value at Risk (VaR) model is a mean of quantifying the risks of the various trading desk, which could be useful in calculating the maximum expected loss on the investment over the period of time. Moreover, he intended to use the model to determine the level of exposure as well as the potential losses of the trading portfolio, in order take required measures to control the risk in trading. Additionally, the model would allow the company to ensure that the firm was earning adequate risks-adjusted returns on the activities. The benefits of using the Value at Risk also included that it measures the market risks, which tend to combine the sensitivity of the portfolio to the changes in market and the probability of the given market change.(Alexandra, 2015).
Based on the excel data provided, run backtests of the VaR predictions against actual daily gains or losses for both the S&P 500 index and the Shanghai index using the following parameters:
The Value at Risk is calculated by using the Variance-covariance method,which calculates the standard deviation of the returns for each S&P and Shanghai.TheVariance-covariance provides a clear picture of the most likely return on the asset, and only requires expected returns and its standard deviation. The standard deviation of the historical returns provides strong foundation of predicting the volatility in returns, in the forthcoming years. By assuming that the daily returns on the assets follows the normal distribution with mean, which is equal to zero; the Value at Risk model at the confidence level is calculated using the formula provided below:
VaRc = z * σ
The simple back test stacks up the actual return on the asset against the forecasted Value at Risk return, in order to calculate the number of exceptions. The number of exceptions occur if the value of loss is greater than the forecaster Value at Risk value.
In the developed model, the number of exceptions are calculated for 3 months’ time period for both S&P and Shanghai stock exchange. The exceptions for S&P one month, two month and three months,are: 3.31, 1.72 and 0.85, respectively, which shows that the number of the actual observations exceed above and over the expected level of return calculated using Variance-Covariance method.
On the other hand, the exceptions for Shanghai one month, two month and three months are:1.18, 1.44 and 1.48, respectively, which shows that the number of the actual observations exceeds above and over the expected level of return calculated using the Variance-Covariance method.
As per the concept, the good Value at Risk model tends to generate high number of exceptions; the exceptions calculated for S&P is greater as compared to exceptions calculated for Shanghai stock exchange,except for third month, which implies that the returns on S&P are greater than the returns on Shanghai stock exchange. Thus, Jasper is required to witness how his plan would theoretically perform and could gauge the efficiency of the trading strategy……..
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